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2. Experience matters for robotic assistance: an analysis of case data

Author

Brian, Riley, Oh, Daniel, Ifuku, Kelli Ann, Sarin, Ankit, O’Sullivan, Patricia, and Chern, Hueylan

Subjects
Reproductive Medicine, Biomedical and Clinical Sciences, Clinical Research, Humans, Robotic Surgical Procedures, Internship and Residency, Retrospective Studies, Robotics, Education, Medical, Graduate, Clinical Competence, Robotic assistance, Bedside assistance, Learning curve, Resident training, Artificial Intelligence and Image Processing, Clinical Sciences, Surgery, Clinical sciences
Abstract

Many robotic procedures require active participation by assistants. Most prior work on assistants' effect on outcomes has been limited in procedural focus and scope, with studies reporting differing results. Knowing how assistant experience affects operating room time could inform operating room case scheduling and provide an impetus for additional assistant training. As such, this retrospective cohort study aimed to determine the association between assistant experience and operating room time for 2291 robotic-assisted operations performed from 2016 to 2022 at our institution. Linear regression showed a significant association between the presence of a junior resident and increased case length differential with an increase of 26.9 min (p = 0.01). There were no significant associations between the presence of a senior resident (p = 0.52), presence of a fellow (p = 0.20), or presence of a physician assistant (p = 0.43) and case length differential. The finding of increased operating room time in the presence of a junior resident during robotic cases supports consideration of the adoption of formal assistant training programs for residents to improve efficiency.

Published
2023

4. Droplet microfluidics

Author

Reynolds, David E., primary, Lim, Jianhua, additional, Roh, Yoon Ho, additional, Chintapula, Uday, additional, Morales, Renee-Tyler, additional, Oh, Daniel, additional, and Ko, Jina, additional

Published
2024
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5. Contributors

Author

Chen, Zhengwei, primary, Chintapula, Uday, additional, Dudala, Sohan, additional, Goel, Sanket, additional, Gopinathan, Kaustav, additional, Gupta, Kshitiz, additional, Jain, Vandana, additional, Jakkam, Leela Kishan, additional, Javed, Arshad, additional, Kalambe, Jayu, additional, Karuppusamy, Jayaprakash, additional, Kishori, Kajal, additional, Ko, Jina, additional, Kolluri, Rajesh, additional, Lim, Jianhua, additional, Mishra, Avanish, additional, Mondal, Pranab K., additional, Morales, Renee-Tyler, additional, Oh, Daniel, additional, Patrikar, Rajendra M., additional, Pyne, Sumanta, additional, Reynolds, David E., additional, Roh, Yoon Ho, additional, Shyam, Sudip, additional, Swain, Jyotiranjan, additional, Wankawala, Dhruvkumar H., additional, and Wereley, Steven T., additional

Published
2024
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14. Automatic Operating Room Surgical Activity Recognition for Robot-Assisted Surgery

Author

Sharghi, Aidean, Haugerud, Helene, Oh, Daniel, and Mohareri, Omid

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Automatic recognition of surgical activities in the operating room (OR) is a key technology for creating next generation intelligent surgical devices and workflow monitoring/support systems. Such systems can potentially enhance efficiency in the OR, resulting in lower costs and improved care delivery to the patients. In this paper, we investigate automatic surgical activity recognition in robot-assisted operations. We collect the first large-scale dataset including 400 full-length multi-perspective videos from a variety of robotic surgery cases captured using Time-of-Flight cameras. We densely annotate the videos with 10 most recognized and clinically relevant classes of activities. Furthermore, we investigate state-of-the-art computer vision action recognition techniques and adapt them for the OR environment and the dataset. First, we fine-tune the Inflated 3D ConvNet (I3D) for clip-level activity recognition on our dataset and use it to extract features from the videos. These features are then fed to a stack of 3 Temporal Gaussian Mixture layers which extracts context from neighboring clips, and eventually go through a Long Short Term Memory network to learn the order of activities in full-length videos. We extensively assess the model and reach a peak performance of 88% mean Average Precision., Comment: International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI'20)

Published
2020

18. Retinopathy of Prematurity

Author

Oh, Daniel, Chee, Ru-Ik, Tsai, Andrew, Tan, Gavin, Wu, Wei-Chi, Chan, R. V. Paul, Saxena, Sandeep, Series Editor, Spaide, Richard F., Series Editor, Souied, Eric H., Series Editor, Lai, Timothy Y.Y., Series Editor, and Chan, R. V. Paul, editor

Published
2022
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22. Multi-view Surgical Video Action Detection via Mixed Global View Attention

Author

Schmidt, Adam, Sharghi, Aidean, Haugerud, Helene, Oh, Daniel, Mohareri, Omid, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, de Bruijne, Marleen, editor, Cattin, Philippe C., editor, Cotin, Stéphane, editor, Padoy, Nicolas, editor, Speidel, Stefanie, editor, Zheng, Yefeng, editor, and Essert, Caroline, editor

Published
2021
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25. Retinopathy of Prematurity

Author

Oh, Daniel, primary, Chee, Ru-Ik, additional, Tsai, Andrew, additional, Tan, Gavin, additional, Wu, Wei-Chi, additional, and Chan, R. V. Paul, additional

Published
2022
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28. Machine Learning-Based Hand Pose Generation Using a Haptic Controller.

Author

Choi, Jongin, Lee, Jaehong, Oh, Daniel, and Lee, Eung-Joo

Subjects
HAPTIC devices, SUPERVISED learning, FINGER joint, MACHINE learning, MOTION capture (Human mechanics), DATA integration
Abstract

In this study, we present a novel approach to derive hand poses from data input via a haptic controller, leveraging machine learning techniques. The input values received from the haptic controller correspond to the movement of five fingers, each assigned a value between 0.0 and 1.0 based on the applied pressure. The wide array of possible finger movements requires a substantial amount of motion capture data, making manual data integration difficult. This challenge is primary due to the need to process and incorporate large volumes of diverse movement information. To tackle this challenge, our proposed method automates the process by utilizing machine learning algorithms to convert haptic controller inputs into hand poses. This involves training a machine learning model using supervised learning, where hand poses are matched with their corresponding input values, and subsequently utilizing this trained model to generate hand poses in response to user input. In our experiments, we assessed the accuracy of the generated hand poses by analyzing the angles and positions of finger joints. As the quantity of training data increased, the margin of error decreased, resulting in generated poses that closely emulated real-world hand movements. [ABSTRACT FROM AUTHOR]

Published
2024
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32. Reconstruction of the Maxillary Sinus After Previous Major Sinus Elevation Failure: A Study with a 3-Year Follow-up.

Author

Messina, Antonello Maria, Oh, Daniel S., Marini, Luca, and Marini, Ettore

Subjects
MAXILLARY sinus surgery, DENTAL implants, PLASTIC surgery, GRAFT survival, TREATMENT failure, TREATMENT effectiveness, LONGITUDINAL method
Abstract

Fourteen maxillary sinuses were reconstructed in 12 patients who presented with a previous sinus elevation failure. In all cases, large perforations of the sinus membrane occurred during removal of the failed graft from the sinus; the perforations were sealed with fibrin glue, then the site was grafted using homologous fibrin glue (HFG) mixed with a calcium phosphate scaffold (CPS). Histologic analyses revealed that the CPS-HFG graft was followed by an ossification process, with the formation of spongy bone similar to that of the normal skeleton. Twenty-four endosseous implants were successfully placed into the newly regenerated bone. All implants were successfully restored with ceramic crowns 6 months after placement. At the 3-year follow-up, no infections or implant failures were reported. The described technique offers several clinical advantages, as the removal of the failed graft, the sinus perforation repair, and the sinus elevation can be achieved in the same surgery without needing to postpone the regenerative surgery phase. [ABSTRACT FROM AUTHOR]

Published
2022
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35. Experience matters for robotic assistance: an analysis of case data.

Author

Oh, Daniel, Oh, Daniel, Ifuku, Kelli, OSullivan, Patricia, Chern, Hueylan, Sarin, Ankit, Brian, Riley, Oh, Daniel, Oh, Daniel, Ifuku, Kelli, OSullivan, Patricia, Chern, Hueylan, Sarin, Ankit, and Brian, Riley

Abstract

Many robotic procedures require active participation by assistants. Most prior work on assistants effect on outcomes has been limited in procedural focus and scope, with studies reporting differing results. Knowing how assistant experience affects operating room time could inform operating room case scheduling and provide an impetus for additional assistant training. As such, this retrospective cohort study aimed to determine the association between assistant experience and operating room time for 2291 robotic-assisted operations performed from 2016 to 2022 at our institution. Linear regression showed a significant association between the presence of a junior resident and increased case length differential with an increase of 26.9 min (p = 0.01). There were no significant associations between the presence of a senior resident (p = 0.52), presence of a fellow (p = 0.20), or presence of a physician assistant (p = 0.43) and case length differential. The finding of increased operating room time in the presence of a junior resident during robotic cases supports consideration of the adoption of formal assistant training programs for residents to improve efficiency.

Published
2023

42. List of Contributors

Author

Abbott, Jake J., primary, Abedin-Nasab, Mohammad H., additional, Abiri, Ahmad, additional, Afshari, Elnaz, additional, Alamdar, Alireza, additional, Alazmani, Ali, additional, Anderson, Oliver, additional, Andres, Axel, additional, Antico, Maria, additional, Arulampalam, Tan, additional, Azizian, Mahdi, additional, Bergeles, Christos, additional, Bergman, Per, additional, Bisley, James, additional, Blacker, Steven J., additional, Boni, Andrea, additional, Buchs, Nicolas Christian, additional, Cengiz, Turgut Bora, additional, Chan, Danny Tat-Ming, additional, Chiu, Philip Wai Yan, additional, Choi, Hyouk Ryeol, additional, Chudy, Darko, additional, Cochetti, Giovanni, additional, Crawford, Ross, additional, Cross, William, additional, Culmer, Peter, additional, Daimios, Simon, additional, De Mathelin, Michel, additional, De Momi, Elena, additional, De Vermandois, Jacopo Adolfo Rossi, additional, Dlaka, Domagoj, additional, Dooley, John R., additional, Drobilo, Luka, additional, Dutson, Erik, additional, Erchinger, Thomas, additional, Fan, Zhencheng, additional, Fanson, Richard, additional, Farahmand, Farzam, additional, Faraji-Dana, Zahra, additional, Faridpooya, Koorosh, additional, Fernando, Anthony, additional, Fontanarosa, Davide, additional, Gan, Chee Wee, additional, Garayt, Mathieu, additional, Gaudio, Gianluca, additional, Gorgun, Emre, additional, Gould, Jon C., additional, Groenhuis, Vincent, additional, Grundfest, Warren, additional, Guo, Ziyan, additional, Gupta, Anjuli M., additional, Hagen, Monika, additional, Higgins, Rana M., additional, Hladio, Andre, additional, Hobeika, Joe, additional, Iordachita, Iulian, additional, Jaiprakash, Anjali, additional, Jaramaz, Branislav, additional, Jayne, David, additional, Jerbić, Bojan, additional, Jinnah, Alexander H., additional, Jinnah, Riyaz H., additional, Johnson, Kelly R., additional, Jonmohamadi, Yaqub, additional, Juo, Yen-Yi, additional, Kajtazi, Marin, additional, Kang, Jin U., additional, Keggi, John M., additional, Khalaji, Iman, additional, Kilby, Warren, additional, Kim, Uikyum, additional, Kim, Yong Bum, additional, Konan, Sujith, additional, Kottenstette, Nicholas, additional, Kwok, Ka-Wai, additional, Lau, Ka Chun, additional, Lawrence, Jeffrey M., additional, Leong, Martin Chun-Wing, additional, Leung, Michael K.K., additional, Leung, Yun Yee, additional, Li, Changsheng, additional, Liang, Wenyu, additional, Liao, Hongen, additional, Liao, Zhuxiu, additional, Lim, Chwee Ming, additional, Lim, Hsueh Yee, additional, Liu, May, additional, Ma, Longfei, additional, Maden, Carla, additional, Maggitti, Michael J., additional, Mariampillai, Adrian L.D., additional, Mattos, Leonardo S., additional, Maurer, Calvin R., additional, Mearini, Ettore, additional, Milas, Jamie, additional, Mirbagheri, Alireza, additional, Mitra, Riddhit, additional, Moccia, Sara, additional, Moradi, Mehdi, additional, Morel, Philippe, additional, Moustris, George, additional, Muir, Jeffrey, additional, Mushtaq, Faisal, additional, Nageotte, Florent, additional, Nasseri, M. Ali, additional, Nathan, Mohan, additional, Naylor, Michael, additional, Ndubizu, Gordian U., additional, Ng, Cailin, additional, Oh, Daniel, additional, Ohmura, Yasushi, additional, Oriot, Elena, additional, Pandey, Ajay K., additional, Pappas, Theodore, additional, Peloso, Andrea, additional, Pensa, Jake, additional, Penza, Veronica, additional, Plaskos, Christopher, additional, Poon, Wai-Sang, additional, Protyniak, Bogdan, additional, Qiu, Liang, additional, Razjigaev, Andrew, additional, Ren, Hongliang, additional, Riviere, Cameron N., additional, Roberts, Jonathan, additional, Russo, Sheila, additional, Saber, Omid, additional, Saeedi-Hosseiny, Marzieh S., additional, Saragaglia, Dominique, additional, Sarkar, Saeed, additional, Sasazawa, Fumio, additional, Sayeh, Sohail, additional, Šekoranja, Bojan, additional, Sellers, William J., additional, Seok, Dong-Yeop, additional, Shalhoub, Sami, additional, Siepel, Françoise J., additional, Sokhanvar, Saeed, additional, Sorger, Jonathan, additional, Standish, Beau A., additional, Steele, Scott R., additional, Stiperski, Ivan, additional, Stramigioli, Stefano, additional, Strydom, Mario, additional, Su, Hao, additional, Šuligoj, Filip, additional, Sun, Songping, additional, Švaco, Marko, additional, Sznitman, Raphael, additional, Takahashi, Masahiro, additional, Tan, Kok Kiong, additional, Tao, Anna, additional, Todorov, Alex, additional, Toso, Christian, additional, Turco, Morena, additional, Turković, Marija, additional, Tzafestas, Costas, additional, Vander Poorten, Emmanuel, additional, Vidaković, Josip, additional, Vitez, Nikola, additional, Volpin, Andrea, additional, Wu, Liao, additional, Yam, Yeung, additional, Yang, Victor X.D., additional, Zanne, Philippe, additional, Žgaljić, Adrian, additional, Zhang, Xinran, additional, Zorn, Lucile, additional, and Župančić, Ivan, additional

Published
2020
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44. The da Vinci Surgical System

Author

Azizian, Mahdi, primary, Liu, May, additional, Khalaji, Iman, additional, Sorger, Jonathan, additional, Oh, Daniel, additional, and Daimios, Simon, additional

Published
2020
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45. Treatment Failure Among Infected Periprosthetic Patients at a Highly Specialized Revision TKA Referral Practice.

Author

Schwarzkopf, Ran, Oh, Daniel, Wright, Elizabeth, Estok, Daniel M, and Katz, Jeffery N

Subjects
Total knee arthroplasty, antibiotic impregnated cement, periprosthetic joint infection, revision total knee arthroplasty, two-stage revision.
Abstract

Deep infection is a serious and costly complication of total knee arthroplasty (TKA), which can increase patient morbidity and compromise functional outcome and satisfaction. Two-stage revision with an interval of parental antibiotics has been shown to be the most successful treatment in eradicating deep infection following TKA. We report a large series by a single surgeon with a highly specialized revision TKA referral practice. We identified 84 patients treated by a two-stage revision. We defined "successful two-stage revision" as negative intraoperative cultures and no further infection-related procedure. We defined "eradication of infection" on the basis of negative cultures and clinical diagnosis. After a mean follow up of 25 months, eradication of the infection was documented in 90.5% of the patients; some had undergone further surgical intervention after the index two-stage procedure. Successful two-stage revision (e.g. no I&D, fusion, amputation) was documented only in 63.5% of the patients. We also observed a trend between presence of resistant staphylococcus (MRSA) (p=0.05) as well as pre-revision surgical procedures (p=0.08) and a lower likelihood of successfully two-stage revision. Factors affecting the high failure rate included multiple surgeries prior to the two-stage revision done at our institution, and high prevalence of MRSA present among failed cases. The relatively high rate of failure to achieve a successful two-stage revision observed in our series may be attributed to the highly specialized referral practice. Thus increasing the prevalence of patients with previous failed attempts at infection eradication and delayed care as well as more fragile and immune compromised hosts.

Published
2013

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