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In surgical skill assessment, the Objective Structured Assessments of Technical Skills (OSATS) and Global Rating Scale (GRS) are well-established tools for evaluating surgeons during training.
Martin, J.A., Regehr, G., Reznick, R., Macrae, H., Murnaghan, J., Hutchison, C., Brown, M.: Objective structured assessment of technical skill (osats) for surgical residents: Objective structured assessment of technical skill. British Journal of Surgery 84
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Khairy, G.: Challenges in creating the educated surgeon in the 21st century: Where do we stand? Annals of Saudi Medicine 24
2004
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Gao, Y., Vedula, S.S., Reiley, C.E., Ahmidi, N., Varadarajan, B., Lin, H.C., Tao, L., Zappella, L., B’ejar, B., Yuh, D.D., et al.: Jhu-isi gesture and skill assessment working set (jigsaws): A surgical activity dataset for human motion modeling. In: MICCAI workshop: M2cai. vol. 3, p. 3 (2014)
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Singh, P., Aggarwal, R., Pucher, P., Duisberg, A., Arora, S., Darzi, A.: Defining quality in surgical training: perceptions of the profession. The American Journal Of Surgery 207
2014
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Zia, A., Essa, I.: Automated surgical skill assessment in rmis training. International Journal of Computer Assisted Radiology and Surgery 13
2018
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Chen, R., Rodrigues Armijo, P., Krause, C., Siu, K.C., Oleynikov, D.: A comprehensive review of robotic surgery curriculum and training for residents, fellows, and postgraduate surgical education. Surgical Endoscopy 34
2019
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Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., Muller, P.A.: Accurate and interpretable evaluation of surgical skills from kinematic data using fully convolutional neural networks. International Journal of Computer Assisted Radiology and Surgery 14
2019
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Kelly, C.J., Karthikesalingam, A., Suleyman, M., Corrado, G., King, D.: Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine 17
2019
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Pan, J.H., Gao, J., Zheng, W.S.: Action assessment by joint relation graphs. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV). pp. 6330–6339 (2019)
2019
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Zago, M., Sforza, C., Mariani, D., Marconi, M., Biloslavo, A., Greca, A.L., Kurihara, H., Casamassima, A., Bozzo, S., Caputo, F., Galli, M., Zago, M.: Educational impact of hand motion analysis in the evaluation of fast examination skills. European Journal of Trauma and Emergency Surgery 46
2019
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Gao, J., Zheng, W.S., Pan, J.H., Gao, C., Wang, Y., Zeng, W., Lai, J.: An Asymmetric Modeling for Action Assessment, p. 222–238. Springer International Publishing (2020)
2020
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Khalid, S., Goldenberg, M., Grantcharov, T., Taati, B., Rudzicz, F.: Evaluation of deep learning models for identifying surgical actions and measuring performance. JAMA Network Open 3
2020
Cited alongside, same era.
Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15
2020
Cited alongside, same era.
Liu, D., Jiang, T., Wang, Y., Miao, R., Shan, F., Li, Z.: Surgical skill assessment on in-vivo clinical data via the clearness of operating field (2020)
2020
Cited alongside, same era.
Liu, R., Holden, M.S.: Kinematics Data Representations for Skills Assessment in Ultrasound-Guided Needle Insertion, p. 189–198. Springer International Publishing (2020)
2020
Cited alongside, same era.
Yanik, E., Intes, X., Kruger, U., Yan, P., Miller, D., Voorst, B.V., Makled, B., Norfleet, J., De, S.: Deep neural networks for the assessment of surgical skills: A systematic review (2021)
2021
Later among the works it cites.
Kasa, K., Burns, D., Goldenberg, M.G., Selim, O., Whyne, C., Hardisty, M.: Multi-modal deep learning for assessing surgeon technical skill. Sensors 22
2022
Later among the works it cites.
Li, Z., Gu, L., Wang, W., Nakamura, R., Sato, Y.: Surgical skill assessment via video semantic aggregation (2022)
2022
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Maynou, L., Pearson, G., McGuire, A., Serra-Sastre, V.: The diffusion of robotic surgery: Examining technology use in the english nhs. Health Policy 126
2022
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Anastasiou, D., Jin, Y., Stoyanov, D., Mazomenos, E.: Keep your eye on the best: Contrastive regression transformer for skill assessment in robotic surgery. IEEE Robotics and Automation Letters 8
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Wang, T., Wang, Y., Li, M.: Towards accurate and interpretable surgical skill assessment: A video-based method incorporating recognized surgical gestures and skill levels. Medical Image Computing And Computer Assisted Intervention – MICCAI 2020 pp. 668–678 (2020)
2020
Cited alongside, same era.
Wang, T., Wang, Y., Li, M.: Towards Accurate and Interpretable Surgical Skill Assessment: A Video-Based Method Incorporating Recognized Surgical Gestures and Skill Levels, p. 668–678. Springer International Publishing (2020)
2020
Cited alongside, same era.
Liu, D., Li, Q., Jiang, T., Wang, Y., Miao, R., Shan, F., Li, Z.: Towards unified surgical skill assessment (2021)
2021
Cited alongside, same era.
Markus, A.F., Kors, J.A., Rijnbeek, P.R.: The role of explainability in creating trustworthy artificial intelligence for health care: A comprehensive survey of the terminology, design choices, and evaluation strategies. Journal of Biomedical Informatics 113
2021
Cited alongside, same era.
Ravi, K., Anyamele, U.A., Korch, M., Badwi, N., Daoud, H.A., Shah, S.S.N.H.: Undergraduate surgical education: a global perspective. Indian Journal of Surgery 84
2021
Cited alongside, same era.
Wang, Y., Dai, J., Morgan, T.N., Elsaied, M., Garbens, A., Qu, X., Steinberg, R., Gahan, J., Larson, E.C.: Evaluating robotic-assisted surgery training videos with multi-task convolutional neural networks. Journal of Robotic Surgery 16
2021
Cited alongside, same era.
2023
Later among the works it cites.
Benmansour, M., Malti, A., Jannin, P.: Deep neural network architecture for automated soft surgical skills evaluation using objective structured assessment of technical skills criteria. International Journal Of Computer Assisted Radiology And Surgery 18
2023
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Hackney, L., O’Neill, S., O’Donnell, M., Spence, R.: A scoping review of assessment methods of competence of general surgical trainees. The Surgeon 21
2023
Later among the works it cites.
Kulik, D., Bell, C.R., Holden, M.S.: Fast skill assessment from kinematics data using convolutional neural networks. International Journal of Computer Assisted Radiology and Surgery 19
2023
Later among the works it cites.
Quarez, J., Li, Y., Irzan, H., Elliot, M., MacCormac, O., Knigth, J., Huber, M., Mahmoodi, T., Dasgupta, P., Ourselin, S., et al.: Mutual: Towards holistic sensing and inference in the operating room. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 178–188. Springer (2024)
2024
Closest in time.
Reyzabal, M.D.I., Chen, M., Huang, W., Ourselin, S., Liu, H.: Dafoes: Mixing datasets towards the generalization of vision-state deep-learning force estimation in minimally invasive robotic surgery (2024)
2024
Closest in time.