Developing artificial intelligence models for medical student suturing and knot-tying video-based assessment and coaching.
Developing artificial intelligence models for medical student suturing and knot-tying video-based assessment and coaching.
复制标题
DOI：
10.1007/s00464-022-09509-y
复制
发表时间：
2023-01
期刊：
影响因子：
--
通讯作者：
中科院分区：
文献类型：
--
作者：
早期引入和分布式学习已被证明可以提高学生对基本必要缝合技能的舒适度。然而，需要更频繁和更直接的反馈仍然是远程和现场培训的一个长期关切。由于COVID-19对社交距离的影响，我们之前为二年级医学生过渡到见习人员的面对面课程被改编为基于家庭视频的评估模型。我们的目标是开发一种人工智能（AI）模型来进行基于视频的评估。二年级医学生被要求提交一段视频，内容是在自我训练到熟练程度后，用器械打结技术在penrose引流管上打一个简单的中断结。熟练程度被定义为在两分钟内完成任务，没有严重错误。所有的视频首先被手工评定为合格-不合格等级，然后进行任务分割。我们开发并训练了两个基于卷积神经网络的人工智能模型，以识别错误（仪器持有和打结）并提供自动评级。总共审查了229个医学生视频（150个通过，79个不通过）。在失败的患者中，临界误差分布为15个打结，47个握住仪器，17个倍数。剔除低质量视频后，共使用216个视频对模型进行训练。采用k-fold交叉验证（k = 10）。仪器保持模型的准确度为89%，F-1评分为74%。对于打结模型，准确率为91%，F-1评分为54%。医学生需要评估和直接反馈，以更好地获得手术技能，但这往往是耗时和不充分的。人工智能技术可以用来执行自动手术视频分析。未来的工作将优化当前的模型，以识别离散的错误，以便用特定的反馈补充基于视频的评级。
Early introduction and distributed learning have been shown to improve student comfort with basic requisite suturing skills. The need for more frequent and directed feedback, however, remains an enduring concern for both remote and in-person training. A previous in-person curriculum for our second-year medical students transitioning to clerkships was adapted to an at-home video-based assessment model due to the social distancing implications of COVID-19. We aimed to develop an Artificial Intelligence (AI) model to perform video-based assessment. Second-year medical students were asked to submit a video of a simple interrupted knot on a penrose drain with instrument tying technique after self-training to proficiency. Proficiency was defined as performing the task under two minutes with no critical errors. All the videos were first manually rated with a pass-fail rating and then subsequently underwent task segmentation. We developed and trained two AI models based on convolutional neural networks to identify errors (instrument holding and knot-tying) and provide automated ratings. A total of 229 medical student videos were reviewed (150 pass, 79 fail). Of those who failed, the critical error distribution was 15 knot-tying, 47 instrument-holding, and 17 multiple. A total of 216 videos were used to train the models after excluding the low-quality videos. A k-fold cross-validation (k = 10) was used. The accuracy of the instrument holding model was 89% with an F-1 score of 74%. For the knot-tying model, the accuracy was 91% with an F-1 score of 54%. Medical students require assessment and directed feedback to better acquire surgical skill, but this is often time-consuming and inadequately done. AI techniques can instead be employed to perform automated surgical video analysis. Future work will optimize the current model to identify discrete errors in order to supplement video-based rating with specific feedback.
登录
查看更多内容
DOI：
10.1007/s00464-021-08336-x
发表时间：
2022-01
期刊：
Surgical endoscopy
影响因子：
--
作者：
Namazi B;Sankaranarayanan G;Devarajan V
通讯作者：
Devarajan V
DOI：
10.1007/s44186-022-00007-2
发表时间：
2022-02-21
期刊：
Global Surgical Education - Journal of the Association for Surgical Education
影响因子：
--
作者：
Nagaraj MB;Campbell KK;Rege RV;Mihalic AP;Scott DJ
通讯作者：
Scott DJ
影响因子：
8.1
作者：
Shorten, Connor;Khoshgoftaar, Taghi M.
通讯作者：
Khoshgoftaar, Taghi M.
影响因子：
7.3
作者：
Manning EP;Mishall PL;Weidmann MD;Flax H;Lan S;Erlich M;Burton WB;Olson TR;Downie SA
通讯作者：
Downie SA
影响因子：
10.9
作者：
Maier-Hein, Lena;Eisenmann, Matthias;Sarikaya, Duygu;Maerz, Keno;Collins, Toby;Malpani, Anand;Fallert, Johannes;Feussner, Hubertus;Giannarou, Stamatia;Mascagni, Pietro;Nakawala, Hirenkumar;Park, Adrian;Pugh, Carla;Stoyanov, Danail;Vedula, Swaroop S.;Cleary, Kevin;Fichtinger, Gabor;Forestier, Germain;Gibaud, Bernard;Grantcharov, Teodor;Hashizume, Makoto;Heckmann-Noetzel, Doreen;Kenngott, Hannes G.;Kikinis, Ron;Muendermann, Lars;Navab, Nassir;Onogur, Sinan;Ross, Tobias;Sznitman, Raphael;Taylor, Russell H.;Tizabi, Minu D.;Wagner, Martin;Hager, Gregory D.;Neumuth, Thomas;Padoy, Nicolas;Collins, Justin;Gockel, Ines;Goedeke, Jan;Hashimoto, Daniel A.;Joyeux, Luc;Lam, Kyle;Leff, Daniel R.;Madani, Amin;Marcus, Hani J.;Meireles, Ozanan;Seitel, Alexander;Teber, Dogu;Ueckert, Frank;Mueller-Stich, Beat P.;Jannin, Pierre;Speidel, Stefanie
通讯作者：
Speidel, Stefanie