var bibbase_data = {"data":"<img src=\"//bibbase.org/img/ajax-loader.gif\" id=\"spinner\"\n  style=\"display: none;\" alt=\"Loading..\" />\n\n<div id=\"bibbase\">\n\n  <script type=\"text/javascript\">\n    var bibbase = {\n      params: {\"bib\":\"https://niravatgit.github.io/patelnirav.com/resources/Nirav_Publicationst.bib\",\"jsonp\":\"1\",\"group0\":\"type\",\"folding\":\"1\",\"css\":\"niravatgit.github.io/patelnirav.com/css/style.css\",\"filter\":\"key:he2019toward|he2018NNControl|he2019enabling|urias2019artificial\",\"host\":\"bibbase.org\"},\n      data: [{\"bibtype\":\"article\",\"type\":\"article\",\"abstract\":\"Purpose: Retinal microsurgery requires highly dexterous and precise maneuvering of instruments inserted into the eyeball through the sclerotomy port. During such procedures, the sclera can potentially be injured from extreme tool-to-sclera contact force caused by surgeon's unintentional misoperations. Methods: We present an active interventional robotic system to prevent such iatrogenic accidents by enabling the robotic system to actively counteract the surgeon's possible unsafe operations in advance of their occurrence. Relying on a novel force sensing tool to measure and collect scleral forces, we construct a recurrent neural network with long short-term memory unit to oversee surgeon's operation and predict possible unsafe scleral forces up to the next 200 ms. We then apply a linear admittance control to actuate the robot to reduce the undesired scleral force. The system is implemented using an existing “steady hand” eye robot platform. The proposed method is evaluated on an artificial eye phantom by performing a “vessel following” mock retinal surgery operation. Results: Empirical validation over multiple trials indicates that the proposed active interventional robotic system could help to reduce the number of unsafe manipulation events. Conclusions: We develop an active interventional robotic system to actively prevent surgeon's unsafe operations in retinal surgery. The result of the evaluation experiments shows that the proposed system can improve the surgeon's performance.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"He\"],\"firstnames\":[\"Changyan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Patel\"],\"firstnames\":[\"Niravkumar\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ebrahimi\"],\"firstnames\":[\"Ali\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kobilarov\"],\"firstnames\":[\"Marin\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Iordachita\"],\"firstnames\":[\"Iulian\"],\"suffixes\":[]}],\"doi\":\"10.1007/s11548-019-01947-9\",\"issn\":\"18616429\",\"journal\":\"International Journal of Computer Assisted Radiology and Surgery\",\"keywords\":\"Interventional system,Medical robot,Recurrent neural network,Retinal surgery\",\"pages\":\"1–10\",\"pmid\":\"30887423\",\"publisher\":\"Springer\",\"title\":\"Preliminary study of an RNN-based active interventional robotic system (AIRS) in retinal microsurgery\",\"url\":\"https://doi.org/10.1007/s11548-019-01947-9\",\"year\":\"2019\",\"bibtex\":\"@article{he2018NNControl,\\nabstract = {Purpose: Retinal microsurgery requires highly dexterous and precise maneuvering of instruments inserted into the eyeball through the sclerotomy port. During such procedures, the sclera can potentially be injured from extreme tool-to-sclera contact force caused by surgeon's unintentional misoperations. Methods: We present an active interventional robotic system to prevent such iatrogenic accidents by enabling the robotic system to actively counteract the surgeon's possible unsafe operations in advance of their occurrence. Relying on a novel force sensing tool to measure and collect scleral forces, we construct a recurrent neural network with long short-term memory unit to oversee surgeon's operation and predict possible unsafe scleral forces up to the next 200 ms. We then apply a linear admittance control to actuate the robot to reduce the undesired scleral force. The system is implemented using an existing “steady hand” eye robot platform. The proposed method is evaluated on an artificial eye phantom by performing a “vessel following” mock retinal surgery operation. Results: Empirical validation over multiple trials indicates that the proposed active interventional robotic system could help to reduce the number of unsafe manipulation events. Conclusions: We develop an active interventional robotic system to actively prevent surgeon's unsafe operations in retinal surgery. The result of the evaluation experiments shows that the proposed system can improve the surgeon's performance.},\\nauthor = {He, Changyan and Patel, Niravkumar and Ebrahimi, Ali and Kobilarov, Marin and Iordachita, Iulian},\\ndoi = {10.1007/s11548-019-01947-9},\\nissn = {18616429},\\njournal = {International Journal of Computer Assisted Radiology and Surgery},\\nkeywords = {Interventional system,Medical robot,Recurrent neural network,Retinal surgery},\\npages = {1--10},\\npmid = {30887423},\\npublisher = {Springer},\\ntitle = {{Preliminary study of an RNN-based active interventional robotic system (AIRS) in retinal microsurgery}},\\nurl = {https://doi.org/10.1007/s11548-019-01947-9},\\nyear = {2019}\\n}\\n\",\"author_short\":[\"He, C.\",\"Patel, N.\",\"Ebrahimi, A.\",\"Kobilarov, M.\",\"Iordachita, I.\"],\"key\":\"he2018NNControl\",\"id\":\"he2018NNControl\",\"bibbaseid\":\"he-patel-ebrahimi-kobilarov-iordachita-preliminarystudyofanrnnbasedactiveinterventionalroboticsystemairsinretinalmicrosurgery-2019\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://doi.org/10.1007/s11548-019-01947-9\"},\"keyword\":[\"Interventional system\",\"Medical robot\",\"Recurrent neural network\",\"Retinal surgery\"],\"metadata\":{\"authorlinks\":{\"he, c\":\"https://niravatgit.github.io/patelnirav.com/index.html\"}},\"downloads\":6,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"abstract\":\"Retinal microsurgery is technically demanding and requires high surgical skill with very little room for manipulation error. During surgery the tool needs to be inserted into the eyeball while maintaining constant contact with the sclera. Any unexpected manipulation could cause extreme tool-sclera contact force (scleral force) thus damage the sclera. The introduction of robotic assistance could enhance and expand the surgeon's manipulation capabilities during surgery. However, the potential intra-operative danger from surgeon's mis-operations remains difficult to detect and prevent by existing robotic systems. Therefore, we propose a method to predict imminent unsafe manipulation in robot-assisted retinal surgery and generate feedback to the surgeon via auditory substitution. The surgeon could then react to the possible unsafe events in advance. This work specifically focuses on minimizing sclera damage using a force-sensing tool calibrated to measure small scleral forces. A recurrent neural network is designed and trained to predict the force safety status up to 500 milliseconds in the future. The system is implemented using an existing 'steady hand' eye robot. A vessel following manipulation task is designed and performed on a dry eye phantom to emulate the retinal surgery and to analyze the proposed method. Finally, preliminary validation experiments are performed by five users, the results of which indicate that the proposed early warning system could help to reduce the number of unsafe manipulation events.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"He\"],\"firstnames\":[\"Changyan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Patel\"],\"firstnames\":[\"Niravkumar\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Iordachita\"],\"firstnames\":[\"Iulian\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kobilarov\"],\"firstnames\":[\"Marin\"],\"suffixes\":[]}],\"booktitle\":\"Proceedings - IEEE International Conference on Robotics and Automation\",\"doi\":\"10.1109/ICRA.2019.8794427\",\"isbn\":\"9781538660263\",\"issn\":\"10504729\",\"organization\":\"IEEE\",\"pages\":\"3889–3894\",\"title\":\"Enabling technology for safe robot-assisted retinal surgery: Early warning for unsafe scleral force\",\"volume\":\"2019-May\",\"year\":\"2019\",\"bibtex\":\"@inproceedings{he2019enabling,\\nabstract = {Retinal microsurgery is technically demanding and requires high surgical skill with very little room for manipulation error. During surgery the tool needs to be inserted into the eyeball while maintaining constant contact with the sclera. Any unexpected manipulation could cause extreme tool-sclera contact force (scleral force) thus damage the sclera. The introduction of robotic assistance could enhance and expand the surgeon's manipulation capabilities during surgery. However, the potential intra-operative danger from surgeon's mis-operations remains difficult to detect and prevent by existing robotic systems. Therefore, we propose a method to predict imminent unsafe manipulation in robot-assisted retinal surgery and generate feedback to the surgeon via auditory substitution. The surgeon could then react to the possible unsafe events in advance. This work specifically focuses on minimizing sclera damage using a force-sensing tool calibrated to measure small scleral forces. A recurrent neural network is designed and trained to predict the force safety status up to 500 milliseconds in the future. The system is implemented using an existing 'steady hand' eye robot. A vessel following manipulation task is designed and performed on a dry eye phantom to emulate the retinal surgery and to analyze the proposed method. Finally, preliminary validation experiments are performed by five users, the results of which indicate that the proposed early warning system could help to reduce the number of unsafe manipulation events.},\\nauthor = {He, Changyan and Patel, Niravkumar and Iordachita, Iulian and Kobilarov, Marin},\\nbooktitle = {Proceedings - IEEE International Conference on Robotics and Automation},\\ndoi = {10.1109/ICRA.2019.8794427},\\nisbn = {9781538660263},\\nissn = {10504729},\\norganization = {IEEE},\\npages = {3889--3894},\\ntitle = {{Enabling technology for safe robot-assisted retinal surgery: Early warning for unsafe scleral force}},\\nvolume = {2019-May},\\nyear = {2019}\\n}\\n\",\"author_short\":[\"He, C.\",\"Patel, N.\",\"Iordachita, I.\",\"Kobilarov, M.\"],\"key\":\"he2019enabling\",\"id\":\"he2019enabling\",\"bibbaseid\":\"he-patel-iordachita-kobilarov-enablingtechnologyforsaferobotassistedretinalsurgeryearlywarningforunsafescleralforce-2019\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"he, c\":\"https://niravatgit.github.io/patelnirav.com/index.html\"}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"abstract\":\"Eye surgery, specifically retinal micro-surgery involves sensory and motor skill that approaches human boundaries and physiological limits for steadiness, accuracy, and the ability to detect the small forces involved. Despite assumptions as to the benefit of robots in surgery and also despite great development effort, numerous challenges to the full development and adoption of robotic assistance in surgical ophthalmology, remain. Historically, the first in-human-robot-Assisted retinal surgery occurred nearly 30 years after the first experimental papers on the subject. Similarly, artificial intelligence emerged decades ago and it is only now being more fully realized in ophthalmology. The delay between conception and application has in part been due to the necessary technological advances required to implement new processing strategies. Chief among these has been the better matched processing power of specialty graphics processing units for machine learning. Transcending the classic concept of robots performing repetitive tasks, artificial intelligence and machine learning are related concepts that has proven their abilities to design concepts and solve problems. The implication of such abilities being that future machines may further intrude on the domain of heretofore \\\"human-reserved\\\" tasks. Although the potential of artificial intelligence/machine learning is profound, present marketing promises and hype exceeds its stage of development, analogous to the seventieth century mathematical \\\"boom\\\" with algebra. Nevertheless robotic systems augmented by machine learning may eventually improve robot-Assisted retinal surgery and could potentially transform the discipline. This commentary analyzes advances in retinal robotic surgery, its current drawbacks and limitations, and the potential role of artificial intelligence in robotic retinal surgery.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Urias\"],\"firstnames\":[\"Müller\",\"G.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Patel\"],\"firstnames\":[\"Niravkumar\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"He\"],\"firstnames\":[\"Changyan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ebrahimi\"],\"firstnames\":[\"Ali\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kim\"],\"firstnames\":[\"Ji\",\"Woong\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Iordachita\"],\"firstnames\":[\"Iulian\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Gehlbach\"],\"firstnames\":[\"Peter\",\"L.\"],\"suffixes\":[]}],\"doi\":\"10.1186/s40942-019-0202-y\",\"issn\":\"20569920\",\"journal\":\"International Journal of Retina and Vitreous\",\"keywords\":\"Artificial intelligence,Ophthalmology,Retina,Robotic surgical procedures,Robotics\",\"number\":\"1\",\"pages\":\"1–4\",\"publisher\":\"BioMed Central\",\"title\":\"Artificial intelligence, robotics and eye surgery: Are we overfitted?\",\"volume\":\"5\",\"year\":\"2019\",\"bibtex\":\"@article{urias2019artificial,\\nabstract = {Eye surgery, specifically retinal micro-surgery involves sensory and motor skill that approaches human boundaries and physiological limits for steadiness, accuracy, and the ability to detect the small forces involved. Despite assumptions as to the benefit of robots in surgery and also despite great development effort, numerous challenges to the full development and adoption of robotic assistance in surgical ophthalmology, remain. Historically, the first in-human-robot-Assisted retinal surgery occurred nearly 30 years after the first experimental papers on the subject. Similarly, artificial intelligence emerged decades ago and it is only now being more fully realized in ophthalmology. The delay between conception and application has in part been due to the necessary technological advances required to implement new processing strategies. Chief among these has been the better matched processing power of specialty graphics processing units for machine learning. Transcending the classic concept of robots performing repetitive tasks, artificial intelligence and machine learning are related concepts that has proven their abilities to design concepts and solve problems. The implication of such abilities being that future machines may further intrude on the domain of heretofore \\\"human-reserved\\\" tasks. Although the potential of artificial intelligence/machine learning is profound, present marketing promises and hype exceeds its stage of development, analogous to the seventieth century mathematical \\\"boom\\\" with algebra. Nevertheless robotic systems augmented by machine learning may eventually improve robot-Assisted retinal surgery and could potentially transform the discipline. This commentary analyzes advances in retinal robotic surgery, its current drawbacks and limitations, and the potential role of artificial intelligence in robotic retinal surgery.},\\nauthor = {Urias, M{\\\\\\\"{u}}ller G. and Patel, Niravkumar and He, Changyan and Ebrahimi, Ali and Kim, Ji Woong and Iordachita, Iulian and Gehlbach, Peter L.},\\ndoi = {10.1186/s40942-019-0202-y},\\nissn = {20569920},\\njournal = {International Journal of Retina and Vitreous},\\nkeywords = {Artificial intelligence,Ophthalmology,Retina,Robotic surgical procedures,Robotics},\\nnumber = {1},\\npages = {1--4},\\npublisher = {BioMed Central},\\ntitle = {{Artificial intelligence, robotics and eye surgery: Are we overfitted?}},\\nvolume = {5},\\nyear = {2019}\\n}\\n\",\"author_short\":[\"Urias, M. G.\",\"Patel, N.\",\"He, C.\",\"Ebrahimi, A.\",\"Kim, J. W.\",\"Iordachita, I.\",\"Gehlbach, P. L.\"],\"key\":\"urias2019artificial\",\"id\":\"urias2019artificial\",\"bibbaseid\":\"urias-patel-he-ebrahimi-kim-iordachita-gehlbach-artificialintelligenceroboticsandeyesurgeryareweoverfitted-2019\",\"role\":\"author\",\"urls\":{},\"keyword\":[\"Artificial intelligence\",\"Ophthalmology\",\"Retina\",\"Robotic surgical procedures\",\"Robotics\"],\"metadata\":{\"authorlinks\":{\"he, c\":\"https://niravatgit.github.io/patelnirav.com/index.html\"}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"abstract\":\"Objective: Robotics-assisted retinal microsurgery provides several benefits including improvement of manipulation precision. The assistance provided to the surgeons by current robotic frameworks is, however, a 'passive' support, e.g., by damping hand tremor. Intelligent assistance and active guidance are, however, lacking in the existing robotic frameworks. In this paper, an active interventional control framework (AICF) has been presented to increase operation safety by actively intervening the operation to avoid exertion of excessive forces to the sclera. Methods: AICF consists of the following four components: first, the steady-hand eye robot as the robotic module; second, a sensorized tool to measure tool-to-sclera forces; third, a recurrent neural network to predict occurrence of undesired events based on a short history of time series of sensor measurements; and finally, a variable admittance controller to command the robot away from the undesired instances. Results: A set of user studies were conducted involving 14 participants (with four surgeons). The users were asked to perform a vessel-following task on an eyeball phantom with the assistance of AICF as well as other two benchmark approaches, i.e., auditory feedback (AF) and real-time force feedback (RF). Statistical analysis shows that AICF results in a significant reduction of proportion of undesired instances to about 2.5%, compared with 38.4% and 26.2% using AF and RF, respectively. Conclusion: AICF can effectively predict excessive-force instances and augment performance of the user to avoid undesired events during robot-assisted microsurgical tasks. Significance: The proposed system may be extended to other fields of microsurgery and may potentially reduce tissue injury.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"He\"],\"firstnames\":[\"Changyan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Patel\"],\"firstnames\":[\"Niravkumar\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Shahbazi\"],\"firstnames\":[\"Mahya\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Yang\"],\"firstnames\":[\"Yang\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Gehlbach\"],\"firstnames\":[\"Peter\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kobilarov\"],\"firstnames\":[\"Marin\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Iordachita\"],\"firstnames\":[\"Iulian\"],\"suffixes\":[]}],\"doi\":\"10.1109/TBME.2019.2926060\",\"issn\":\"15582531\",\"journal\":\"IEEE Transactions on Biomedical Engineering\",\"keywords\":\"Medical robotics,recurrent neural network,retinal surgery,safety in microsurgery\",\"number\":\"4\",\"pages\":\"966–977\",\"pmid\":\"31265381\",\"publisher\":\"IEEE\",\"title\":\"Toward Safe Retinal Microsurgery: Development and Evaluation of an RNN-Based Active Interventional Control Framework\",\"volume\":\"67\",\"year\":\"2020\",\"bibtex\":\"@article{he2019toward,\\nabstract = {Objective: Robotics-assisted retinal microsurgery provides several benefits including improvement of manipulation precision. The assistance provided to the surgeons by current robotic frameworks is, however, a 'passive' support, e.g., by damping hand tremor. Intelligent assistance and active guidance are, however, lacking in the existing robotic frameworks. In this paper, an active interventional control framework (AICF) has been presented to increase operation safety by actively intervening the operation to avoid exertion of excessive forces to the sclera. Methods: AICF consists of the following four components: first, the steady-hand eye robot as the robotic module; second, a sensorized tool to measure tool-to-sclera forces; third, a recurrent neural network to predict occurrence of undesired events based on a short history of time series of sensor measurements; and finally, a variable admittance controller to command the robot away from the undesired instances. Results: A set of user studies were conducted involving 14 participants (with four surgeons). The users were asked to perform a vessel-following task on an eyeball phantom with the assistance of AICF as well as other two benchmark approaches, i.e., auditory feedback (AF) and real-time force feedback (RF). Statistical analysis shows that AICF results in a significant reduction of proportion of undesired instances to about 2.5%, compared with 38.4% and 26.2% using AF and RF, respectively. Conclusion: AICF can effectively predict excessive-force instances and augment performance of the user to avoid undesired events during robot-assisted microsurgical tasks. Significance: The proposed system may be extended to other fields of microsurgery and may potentially reduce tissue injury.},\\nauthor = {He, Changyan and Patel, Niravkumar and Shahbazi, Mahya and Yang, Yang and Gehlbach, Peter and Kobilarov, Marin and Iordachita, Iulian},\\ndoi = {10.1109/TBME.2019.2926060},\\nissn = {15582531},\\njournal = {IEEE Transactions on Biomedical Engineering},\\nkeywords = {Medical robotics,recurrent neural network,retinal surgery,safety in microsurgery},\\nnumber = {4},\\npages = {966--977},\\npmid = {31265381},\\npublisher = {IEEE},\\ntitle = {{Toward Safe Retinal Microsurgery: Development and Evaluation of an RNN-Based Active Interventional Control Framework}},\\nvolume = {67},\\nyear = {2020}\\n}\\n\",\"author_short\":[\"He, C.\",\"Patel, N.\",\"Shahbazi, M.\",\"Yang, Y.\",\"Gehlbach, P.\",\"Kobilarov, M.\",\"Iordachita, I.\"],\"key\":\"he2019toward\",\"id\":\"he2019toward\",\"bibbaseid\":\"he-patel-shahbazi-yang-gehlbach-kobilarov-iordachita-towardsaferetinalmicrosurgerydevelopmentandevaluationofanrnnbasedactiveinterventionalcontrolframework-2020\",\"role\":\"author\",\"urls\":{},\"keyword\":[\"Medical robotics\",\"recurrent neural network\",\"retinal surgery\",\"safety in microsurgery\"],\"metadata\":{\"authorlinks\":{\"he, c\":\"https://niravatgit.github.io/patelnirav.com/index.html\"}},\"html\":\"\"}],\n      metadata: {\"owner\":\"he, c\",\"authorlinks\":{\"he, c\":\"https://niravatgit.github.io/patelnirav.com/index.html\"}},\n      ownerID: \"bvbWAyLYxMkPLQ3XE\"\n    };\n  </script>\n\n  <script type=\"text/javascript\">\n  var site$;\n  if (typeof $ != 'undefined') {\n    console.log('existing jquery: storing and then restoring');\n    site$ = $;\n    $ = null;\n  }\n  </script>\n\n  <script src=\"https://ajax.googleapis.com/ajax/libs/jquery/2.2.4/jquery.min.js\">\n  </script>\n  <script type=\"text/javascript\">\n  if (site$) {\n    console.log('existing jquery: avoiding conflict and restoring');\n    bb$ = $.noConflict(true);\n    $ = site$;\n  } else {\n    bb$ = $;\n  }\n  </script>\n\n  <script src=\"//bibbase.org/js/bibbase.min.js\"\n          type=\"text/javascript\">\n  </script>\n\n  <script type=\"text/javascript\"\n          src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS-MML_HTMLorMML\">\n  </script>\n  <script type=\"text/x-mathjax-config\">\n    MathJax.Hub.Config({tex2jax: {inlineMath: [['$','$'], ['\\\\(','\\\\)']]},\n                        skipTags: [\"body\"],\n                        processClass: \"bibbase_paper\"});\n  </script>\n\n  <style>\n  @media print {\n    .dontprint {\n        display: none !important;\n    }\n\n  .bibbase_group {\n    background-color: #E0E0E0;\n    font-style: italic;\n    font-size: larger;\n    text-shadow: 0px 0px 3px #FFFFFF;\n    border-radius: 4px 4px 4px 4px;\n    -moz-border-radius: 4px 4px 4px 4px;\n    -webkit-border-radius: 4px;\n    padding: 5px 40px 5px;\n    margin-top: 10px;\n    margin-bottom: 5px;\n    cursor: pointer;\n  }\n\n  .bibbase_paper {\n    margin-bottom: 5px;\n  }\n\n  }\n  </style>\n\n  \n  <div style=\"float: right; 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abstract = {Purpose: Retinal microsurgery requires highly dexterous and precise maneuvering of instruments inserted into the eyeball through the sclerotomy port. During such procedures, the sclera can potentially be injured from extreme tool-to-sclera contact force caused by surgeon's unintentional misoperations. Methods: We present an active interventional robotic system to prevent such iatrogenic accidents by enabling the robotic system to actively counteract the surgeon's possible unsafe operations in advance of their occurrence. Relying on a novel force sensing tool to measure and collect scleral forces, we construct a recurrent neural network with long short-term memory unit to oversee surgeon's operation and predict possible unsafe scleral forces up to the next 200 ms. We then apply a linear admittance control to actuate the robot to reduce the undesired scleral force. The system is implemented using an existing “steady hand” eye robot platform. The proposed method is evaluated on an artificial eye phantom by performing a “vessel following” mock retinal surgery operation. Results: Empirical validation over multiple trials indicates that the proposed active interventional robotic system could help to reduce the number of unsafe manipulation events. Conclusions: We develop an active interventional robotic system to actively prevent surgeon's unsafe operations in retinal surgery. The result of the evaluation experiments shows that the proposed system can improve the surgeon's performance.},
author = {He, Changyan and Patel, Niravkumar and Ebrahimi, Ali and Kobilarov, Marin and Iordachita, Iulian},
doi = {10.1007/s11548-019-01947-9},
issn = {18616429},
journal = {International Journal of Computer Assisted Radiology and Surgery},
keywords = {Interventional system,Medical robot,Recurrent neural network,Retinal surgery},
pages = {1--10},
pmid = {30887423},
publisher = {Springer},
title = {{Preliminary study of an RNN-based active interventional robotic system (AIRS) in retinal microsurgery}},
url = {https://doi.org/10.1007/s11548-019-01947-9},
year = {2019}
}


@inproceedings{he2019enabling,
abstract = {Retinal microsurgery is technically demanding and requires high surgical skill with very little room for manipulation error. During surgery the tool needs to be inserted into the eyeball while maintaining constant contact with the sclera. Any unexpected manipulation could cause extreme tool-sclera contact force (scleral force) thus damage the sclera. The introduction of robotic assistance could enhance and expand the surgeon's manipulation capabilities during surgery. However, the potential intra-operative danger from surgeon's mis-operations remains difficult to detect and prevent by existing robotic systems. Therefore, we propose a method to predict imminent unsafe manipulation in robot-assisted retinal surgery and generate feedback to the surgeon via auditory substitution. The surgeon could then react to the possible unsafe events in advance. This work specifically focuses on minimizing sclera damage using a force-sensing tool calibrated to measure small scleral forces. A recurrent neural network is designed and trained to predict the force safety status up to 500 milliseconds in the future. The system is implemented using an existing 'steady hand' eye robot. A vessel following manipulation task is designed and performed on a dry eye phantom to emulate the retinal surgery and to analyze the proposed method. Finally, preliminary validation experiments are performed by five users, the results of which indicate that the proposed early warning system could help to reduce the number of unsafe manipulation events.},
author = {He, Changyan and Patel, Niravkumar and Iordachita, Iulian and Kobilarov, Marin},
booktitle = {Proceedings - IEEE International Conference on Robotics and Automation},
doi = {10.1109/ICRA.2019.8794427},
isbn = {9781538660263},
issn = {10504729},
organization = {IEEE},
pages = {3889--3894},
title = {{Enabling technology for safe robot-assisted retinal surgery: Early warning for unsafe scleral force}},
volume = {2019-May},
year = {2019}
}


@article{urias2019artificial,
abstract = {Eye surgery, specifically retinal micro-surgery involves sensory and motor skill that approaches human boundaries and physiological limits for steadiness, accuracy, and the ability to detect the small forces involved. Despite assumptions as to the benefit of robots in surgery and also despite great development effort, numerous challenges to the full development and adoption of robotic assistance in surgical ophthalmology, remain. Historically, the first in-human-robot-Assisted retinal surgery occurred nearly 30 years after the first experimental papers on the subject. Similarly, artificial intelligence emerged decades ago and it is only now being more fully realized in ophthalmology. The delay between conception and application has in part been due to the necessary technological advances required to implement new processing strategies. Chief among these has been the better matched processing power of specialty graphics processing units for machine learning. Transcending the classic concept of robots performing repetitive tasks, artificial intelligence and machine learning are related concepts that has proven their abilities to design concepts and solve problems. The implication of such abilities being that future machines may further intrude on the domain of heretofore "human-reserved" tasks. Although the potential of artificial intelligence/machine learning is profound, present marketing promises and hype exceeds its stage of development, analogous to the seventieth century mathematical "boom" with algebra. Nevertheless robotic systems augmented by machine learning may eventually improve robot-Assisted retinal surgery and could potentially transform the discipline. This commentary analyzes advances in retinal robotic surgery, its current drawbacks and limitations, and the potential role of artificial intelligence in robotic retinal surgery.},
author = {Urias, M{\"{u}}ller G. and Patel, Niravkumar and He, Changyan and Ebrahimi, Ali and Kim, Ji Woong and Iordachita, Iulian and Gehlbach, Peter L.},
doi = {10.1186/s40942-019-0202-y},
issn = {20569920},
journal = {International Journal of Retina and Vitreous},
keywords = {Artificial intelligence,Ophthalmology,Retina,Robotic surgical procedures,Robotics},
number = {1},
pages = {1--4},
publisher = {BioMed Central},
title = {{Artificial intelligence, robotics and eye surgery: Are we overfitted?}},
volume = {5},
year = {2019}
}


@article{he2019toward,
abstract = {Objective: Robotics-assisted retinal microsurgery provides several benefits including improvement of manipulation precision. The assistance provided to the surgeons by current robotic frameworks is, however, a 'passive' support, e.g., by damping hand tremor. Intelligent assistance and active guidance are, however, lacking in the existing robotic frameworks. In this paper, an active interventional control framework (AICF) has been presented to increase operation safety by actively intervening the operation to avoid exertion of excessive forces to the sclera. Methods: AICF consists of the following four components: first, the steady-hand eye robot as the robotic module; second, a sensorized tool to measure tool-to-sclera forces; third, a recurrent neural network to predict occurrence of undesired events based on a short history of time series of sensor measurements; and finally, a variable admittance controller to command the robot away from the undesired instances. Results: A set of user studies were conducted involving 14 participants (with four surgeons). The users were asked to perform a vessel-following task on an eyeball phantom with the assistance of AICF as well as other two benchmark approaches, i.e., auditory feedback (AF) and real-time force feedback (RF). Statistical analysis shows that AICF results in a significant reduction of proportion of undesired instances to about 2.5%, compared with 38.4% and 26.2% using AF and RF, respectively. Conclusion: AICF can effectively predict excessive-force instances and augment performance of the user to avoid undesired events during robot-assisted microsurgical tasks. Significance: The proposed system may be extended to other fields of microsurgery and may potentially reduce tissue injury.},
author = {He, Changyan and Patel, Niravkumar and Shahbazi, Mahya and Yang, Yang and Gehlbach, Peter and Kobilarov, Marin and Iordachita, Iulian},
doi = {10.1109/TBME.2019.2926060},
issn = {15582531},
journal = {IEEE Transactions on Biomedical Engineering},
keywords = {Medical robotics,recurrent neural network,retinal surgery,safety in microsurgery},
number = {4},
pages = {966--977},
pmid = {31265381},
publisher = {IEEE},
title = {{Toward Safe Retinal Microsurgery: Development and Evaluation of an RNN-Based Active Interventional Control Framework}},
volume = {67},
year = {2020}
}
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During such procedures, the sclera can potentially be injured from extreme tool-to-sclera contact force caused by surgeon&#x27;s unintentional misoperations. Methods: We present an active interventional robotic system to prevent such iatrogenic accidents by enabling the robotic system to actively counteract the surgeon&#x27;s possible unsafe operations in advance of their occurrence. Relying on a novel force sensing tool to measure and collect scleral forces, we construct a recurrent neural network with long short-term memory unit to oversee surgeon&#x27;s operation and predict possible unsafe scleral forces up to the next 200 ms. We then apply a linear admittance control to actuate the robot to reduce the undesired scleral force. The system is implemented using an existing “steady hand” eye robot platform. The proposed method is evaluated on an artificial eye phantom by performing a “vessel following” mock retinal surgery operation. Results: Empirical validation over multiple trials indicates that the proposed active interventional robotic system could help to reduce the number of unsafe manipulation events. Conclusions: We develop an active interventional robotic system to actively prevent surgeon&#x27;s unsafe operations in retinal surgery. The result of the evaluation experiments shows that the proposed system can improve the surgeon&#x27;s performance.},\nauthor = {He, Changyan and Patel, Niravkumar and Ebrahimi, Ali and Kobilarov, Marin and Iordachita, Iulian},\ndoi = {10.1007/s11548-019-01947-9},\nissn = {18616429},\njournal = {International Journal of Computer Assisted Radiology and Surgery},\nkeywords = {Interventional system,Medical robot,Recurrent neural network,Retinal surgery},\npages = {1--10},\npmid = {30887423},\npublisher = {Springer},\ntitle = {{Preliminary study of an RNN-based active interventional robotic system (AIRS) in retinal microsurgery}},\nurl = {https://doi.org/10.1007/s11548-019-01947-9},\nyear = {2019}\n}\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Purpose: Retinal microsurgery requires highly dexterous and precise maneuvering of instruments inserted into the eyeball through the sclerotomy port. During such procedures, the sclera can potentially be injured from extreme tool-to-sclera contact force caused by surgeon's unintentional misoperations. Methods: We present an active interventional robotic system to prevent such iatrogenic accidents by enabling the robotic system to actively counteract the surgeon's possible unsafe operations in advance of their occurrence. Relying on a novel force sensing tool to measure and collect scleral forces, we construct a recurrent neural network with long short-term memory unit to oversee surgeon's operation and predict possible unsafe scleral forces up to the next 200 ms. We then apply a linear admittance control to actuate the robot to reduce the undesired scleral force. The system is implemented using an existing “steady hand” eye robot platform. The proposed method is evaluated on an artificial eye phantom by performing a “vessel following” mock retinal surgery operation. Results: Empirical validation over multiple trials indicates that the proposed active interventional robotic system could help to reduce the number of unsafe manipulation events. Conclusions: We develop an active interventional robotic system to actively prevent surgeon's unsafe operations in retinal surgery. The result of the evaluation experiments shows that the proposed system can improve the surgeon's performance.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"urias-patel-he-ebrahimi-kim-iordachita-gehlbach-artificialintelligenceroboticsandeyesurgeryareweoverfitted-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Artificial intelligence, robotics and eye surgery: Are we overfitted?.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Urias, M. G.; Patel, N.; <a class=\"bibbase author link\" href=\"https://niravatgit.github.io/patelnirav.com/index.html\">He, C.</a>; Ebrahimi, A.; Kim, J. W.; Iordachita, I.;  and Gehlbach, P. L.\n</span>\n\n  \n\n</span>\n\n  <i>International Journal of Retina and Vitreous</i>, 5(1): 1–4.  2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1186/s40942-019-0202-y\"\n     onclick=\"log_download('urias-patel-he-ebrahimi-kim-iordachita-gehlbach-artificialintelligenceroboticsandeyesurgeryareweoverfitted-2019', 'http://doi.org/10.1186/s40942-019-0202-y', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/urias-patel-he-ebrahimi-kim-iordachita-gehlbach-artificialintelligenceroboticsandeyesurgeryareweoverfitted-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('urias-patel-he-ebrahimi-kim-iordachita-gehlbach-artificialintelligenceroboticsandeyesurgeryareweoverfitted-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{urias2019artificial,\nabstract = {Eye surgery, specifically retinal micro-surgery involves sensory and motor skill that approaches human boundaries and physiological limits for steadiness, accuracy, and the ability to detect the small forces involved. Despite assumptions as to the benefit of robots in surgery and also despite great development effort, numerous challenges to the full development and adoption of robotic assistance in surgical ophthalmology, remain. Historically, the first in-human-robot-Assisted retinal surgery occurred nearly 30 years after the first experimental papers on the subject. Similarly, artificial intelligence emerged decades ago and it is only now being more fully realized in ophthalmology. The delay between conception and application has in part been due to the necessary technological advances required to implement new processing strategies. Chief among these has been the better matched processing power of specialty graphics processing units for machine learning. Transcending the classic concept of robots performing repetitive tasks, artificial intelligence and machine learning are related concepts that has proven their abilities to design concepts and solve problems. The implication of such abilities being that future machines may further intrude on the domain of heretofore &quot;human-reserved&quot; tasks. Although the potential of artificial intelligence/machine learning is profound, present marketing promises and hype exceeds its stage of development, analogous to the seventieth century mathematical &quot;boom&quot; with algebra. Nevertheless robotic systems augmented by machine learning may eventually improve robot-Assisted retinal surgery and could potentially transform the discipline. This commentary analyzes advances in retinal robotic surgery, its current drawbacks and limitations, and the potential role of artificial intelligence in robotic retinal surgery.},\nauthor = {Urias, M{\\&quot;{u}}ller G. and Patel, Niravkumar and He, Changyan and Ebrahimi, Ali and Kim, Ji Woong and Iordachita, Iulian and Gehlbach, Peter L.},\ndoi = {10.1186/s40942-019-0202-y},\nissn = {20569920},\njournal = {International Journal of Retina and Vitreous},\nkeywords = {Artificial intelligence,Ophthalmology,Retina,Robotic surgical procedures,Robotics},\nnumber = {1},\npages = {1--4},\npublisher = {BioMed Central},\ntitle = {{Artificial intelligence, robotics and eye surgery: Are we overfitted?}},\nvolume = {5},\nyear = {2019}\n}\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Eye surgery, specifically retinal micro-surgery involves sensory and motor skill that approaches human boundaries and physiological limits for steadiness, accuracy, and the ability to detect the small forces involved. Despite assumptions as to the benefit of robots in surgery and also despite great development effort, numerous challenges to the full development and adoption of robotic assistance in surgical ophthalmology, remain. Historically, the first in-human-robot-Assisted retinal surgery occurred nearly 30 years after the first experimental papers on the subject. Similarly, artificial intelligence emerged decades ago and it is only now being more fully realized in ophthalmology. The delay between conception and application has in part been due to the necessary technological advances required to implement new processing strategies. Chief among these has been the better matched processing power of specialty graphics processing units for machine learning. Transcending the classic concept of robots performing repetitive tasks, artificial intelligence and machine learning are related concepts that has proven their abilities to design concepts and solve problems. The implication of such abilities being that future machines may further intrude on the domain of heretofore \"human-reserved\" tasks. Although the potential of artificial intelligence/machine learning is profound, present marketing promises and hype exceeds its stage of development, analogous to the seventieth century mathematical \"boom\" with algebra. Nevertheless robotic systems augmented by machine learning may eventually improve robot-Assisted retinal surgery and could potentially transform the discipline. This commentary analyzes advances in retinal robotic surgery, its current drawbacks and limitations, and the potential role of artificial intelligence in robotic retinal surgery.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"he-patel-shahbazi-yang-gehlbach-kobilarov-iordachita-towardsaferetinalmicrosurgerydevelopmentandevaluationofanrnnbasedactiveinterventionalcontrolframework-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Toward Safe Retinal Microsurgery: Development and Evaluation of an RNN-Based Active Interventional Control Framework.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://niravatgit.github.io/patelnirav.com/index.html\">He, C.</a>; Patel, N.; Shahbazi, M.; Yang, Y.; Gehlbach, P.; Kobilarov, M.;  and Iordachita, I.\n</span>\n\n  \n\n</span>\n\n  <i>IEEE Transactions on Biomedical Engineering</i>, 67(4): 966–977.  2020.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1109/TBME.2019.2926060\"\n     onclick=\"log_download('he-patel-shahbazi-yang-gehlbach-kobilarov-iordachita-towardsaferetinalmicrosurgerydevelopmentandevaluationofanrnnbasedactiveinterventionalcontrolframework-2020', 'http://doi.org/10.1109/TBME.2019.2926060', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/he-patel-shahbazi-yang-gehlbach-kobilarov-iordachita-towardsaferetinalmicrosurgerydevelopmentandevaluationofanrnnbasedactiveinterventionalcontrolframework-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('he-patel-shahbazi-yang-gehlbach-kobilarov-iordachita-towardsaferetinalmicrosurgerydevelopmentandevaluationofanrnnbasedactiveinterventionalcontrolframework-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{he2019toward,\nabstract = {Objective: Robotics-assisted retinal microsurgery provides several benefits including improvement of manipulation precision. The assistance provided to the surgeons by current robotic frameworks is, however, a &#x27;passive&#x27; support, e.g., by damping hand tremor. Intelligent assistance and active guidance are, however, lacking in the existing robotic frameworks. In this paper, an active interventional control framework (AICF) has been presented to increase operation safety by actively intervening the operation to avoid exertion of excessive forces to the sclera. Methods: AICF consists of the following four components: first, the steady-hand eye robot as the robotic module; second, a sensorized tool to measure tool-to-sclera forces; third, a recurrent neural network to predict occurrence of undesired events based on a short history of time series of sensor measurements; and finally, a variable admittance controller to command the robot away from the undesired instances. Results: A set of user studies were conducted involving 14 participants (with four surgeons). The users were asked to perform a vessel-following task on an eyeball phantom with the assistance of AICF as well as other two benchmark approaches, i.e., auditory feedback (AF) and real-time force feedback (RF). Statistical analysis shows that AICF results in a significant reduction of proportion of undesired instances to about 2.5%, compared with 38.4% and 26.2% using AF and RF, respectively. Conclusion: AICF can effectively predict excessive-force instances and augment performance of the user to avoid undesired events during robot-assisted microsurgical tasks. Significance: The proposed system may be extended to other fields of microsurgery and may potentially reduce tissue injury.},\nauthor = {He, Changyan and Patel, Niravkumar and Shahbazi, Mahya and Yang, Yang and Gehlbach, Peter and Kobilarov, Marin and Iordachita, Iulian},\ndoi = {10.1109/TBME.2019.2926060},\nissn = {15582531},\njournal = {IEEE Transactions on Biomedical Engineering},\nkeywords = {Medical robotics,recurrent neural network,retinal surgery,safety in microsurgery},\nnumber = {4},\npages = {966--977},\npmid = {31265381},\npublisher = {IEEE},\ntitle = {{Toward Safe Retinal Microsurgery: Development and Evaluation of an RNN-Based Active Interventional Control Framework}},\nvolume = {67},\nyear = {2020}\n}\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Objective: Robotics-assisted retinal microsurgery provides several benefits including improvement of manipulation precision. The assistance provided to the surgeons by current robotic frameworks is, however, a 'passive' support, e.g., by damping hand tremor. Intelligent assistance and active guidance are, however, lacking in the existing robotic frameworks. In this paper, an active interventional control framework (AICF) has been presented to increase operation safety by actively intervening the operation to avoid exertion of excessive forces to the sclera. Methods: AICF consists of the following four components: first, the steady-hand eye robot as the robotic module; second, a sensorized tool to measure tool-to-sclera forces; third, a recurrent neural network to predict occurrence of undesired events based on a short history of time series of sensor measurements; and finally, a variable admittance controller to command the robot away from the undesired instances. Results: A set of user studies were conducted involving 14 participants (with four surgeons). The users were asked to perform a vessel-following task on an eyeball phantom with the assistance of AICF as well as other two benchmark approaches, i.e., auditory feedback (AF) and real-time force feedback (RF). Statistical analysis shows that AICF results in a significant reduction of proportion of undesired instances to about 2.5%, compared with 38.4% and 26.2% using AF and RF, respectively. Conclusion: AICF can effectively predict excessive-force instances and augment performance of the user to avoid undesired events during robot-assisted microsurgical tasks. Significance: The proposed system may be extended to other fields of microsurgery and may potentially reduce tissue injury.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_inproceedings\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_inproceedings')\"\n      onkeypress=\"toggleGroup('group_inproceedings')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>inproceedings</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"he-patel-iordachita-kobilarov-enablingtechnologyforsaferobotassistedretinalsurgeryearlywarningforunsafescleralforce-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Enabling technology for safe robot-assisted retinal surgery: Early warning for unsafe scleral force.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://niravatgit.github.io/patelnirav.com/index.html\">He, C.</a>; Patel, N.; Iordachita, I.;  and Kobilarov, M.\n</span>\n\n  \n\n</span>\n\n  In <i>Proceedings - IEEE International Conference on Robotics and Automation</i>, volume 2019-May, pages 3889–3894,  2019. IEEE\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1109/ICRA.2019.8794427\"\n     onclick=\"log_download('he-patel-iordachita-kobilarov-enablingtechnologyforsaferobotassistedretinalsurgeryearlywarningforunsafescleralforce-2019', 'http://doi.org/10.1109/ICRA.2019.8794427', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/he-patel-iordachita-kobilarov-enablingtechnologyforsaferobotassistedretinalsurgeryearlywarningforunsafescleralforce-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('he-patel-iordachita-kobilarov-enablingtechnologyforsaferobotassistedretinalsurgeryearlywarningforunsafescleralforce-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{he2019enabling,\nabstract = {Retinal microsurgery is technically demanding and requires high surgical skill with very little room for manipulation error. During surgery the tool needs to be inserted into the eyeball while maintaining constant contact with the sclera. Any unexpected manipulation could cause extreme tool-sclera contact force (scleral force) thus damage the sclera. The introduction of robotic assistance could enhance and expand the surgeon&#x27;s manipulation capabilities during surgery. However, the potential intra-operative danger from surgeon&#x27;s mis-operations remains difficult to detect and prevent by existing robotic systems. Therefore, we propose a method to predict imminent unsafe manipulation in robot-assisted retinal surgery and generate feedback to the surgeon via auditory substitution. The surgeon could then react to the possible unsafe events in advance. This work specifically focuses on minimizing sclera damage using a force-sensing tool calibrated to measure small scleral forces. A recurrent neural network is designed and trained to predict the force safety status up to 500 milliseconds in the future. The system is implemented using an existing &#x27;steady hand&#x27; eye robot. A vessel following manipulation task is designed and performed on a dry eye phantom to emulate the retinal surgery and to analyze the proposed method. Finally, preliminary validation experiments are performed by five users, the results of which indicate that the proposed early warning system could help to reduce the number of unsafe manipulation events.},\nauthor = {He, Changyan and Patel, Niravkumar and Iordachita, Iulian and Kobilarov, Marin},\nbooktitle = {Proceedings - IEEE International Conference on Robotics and Automation},\ndoi = {10.1109/ICRA.2019.8794427},\nisbn = {9781538660263},\nissn = {10504729},\norganization = {IEEE},\npages = {3889--3894},\ntitle = {{Enabling technology for safe robot-assisted retinal surgery: Early warning for unsafe scleral force}},\nvolume = {2019-May},\nyear = {2019}\n}\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Retinal microsurgery is technically demanding and requires high surgical skill with very little room for manipulation error. During surgery the tool needs to be inserted into the eyeball while maintaining constant contact with the sclera. Any unexpected manipulation could cause extreme tool-sclera contact force (scleral force) thus damage the sclera. The introduction of robotic assistance could enhance and expand the surgeon's manipulation capabilities during surgery. However, the potential intra-operative danger from surgeon's mis-operations remains difficult to detect and prevent by existing robotic systems. Therefore, we propose a method to predict imminent unsafe manipulation in robot-assisted retinal surgery and generate feedback to the surgeon via auditory substitution. The surgeon could then react to the possible unsafe events in advance. This work specifically focuses on minimizing sclera damage using a force-sensing tool calibrated to measure small scleral forces. A recurrent neural network is designed and trained to predict the force safety status up to 500 milliseconds in the future. The system is implemented using an existing 'steady hand' eye robot. A vessel following manipulation task is designed and performed on a dry eye phantom to emulate the retinal surgery and to analyze the proposed method. Finally, preliminary validation experiments are performed by five users, the results of which indicate that the proposed early warning system could help to reduce the number of unsafe manipulation events.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n\n\n\n  </div>\n\n\n  <!-- Modal -->\n\n  <!-- <iframe id=\"bibbase_network_frame\" -->\n  <!--         src=\"//bibbase.org/network/control\" -->\n  <!--         style=\"display: none;\"> -->\n  <!-- </iframe> -->\n\n</div>\n"}; document.write(bibbase_data.data);