Johns Hopkins University, 3400 N. Charles Street, Malone Hall 340, Baltimore, MD, 21218, USA.
Wilmer Eye Institute, Johns Hopkins University, 600 N. Wolfe Street, Baltimore, MD, 21287, USA.
Int J Comput Assist Radiol Surg. 2019 Jun;14(6):1097-1105. doi: 10.1007/s11548-019-01956-8. Epub 2019 Apr 11.
Objective assessment of intraoperative technical skill is necessary for technology to improve patient care through surgical training. Our objective in this study was to develop and validate deep learning techniques for technical skill assessment using videos of the surgical field.
We used a data set of 99 videos of capsulorhexis, a critical step in cataract surgery. One expert surgeon annotated each video for technical skill using a standard structured rating scale, the International Council of Ophthalmology's Ophthalmology Surgical Competency Assessment Rubric:phacoemulsification (ICO-OSCAR:phaco). Using two capsulorhexis indices in this scale (commencement of flap and follow-through, formation and completion), we specified an expert performance when at least one of the indices was 5 and the other index was at least 4, and novice otherwise. In addition, we used scores for capsulorhexis commencement and capsulorhexis formation as separate ground truths (Likert scale of 2 to 5; analyzed as 2/3, 4 and 5). We crowdsourced annotations of instrument tips. We separately modeled instrument trajectories and optical flow using temporal convolutional neural networks to predict a skill class (expert/novice) and score on each item for capsulorhexis in ICO-OSCAR:phaco. We evaluated the algorithms in a five-fold cross-validation and computed accuracy and area under the receiver operating characteristics curve (AUC).
The accuracy and AUC were 0.848 and 0.863 for instrument tip velocities, and 0.634 and 0.803 for optical flow fields, respectively.
Deep neural networks effectively model surgical technical skill in capsulorhexis given structured representation of intraoperative data such as optical flow fields extracted from video or crowdsourced tool localization information.
客观评估术中技术技能对于通过手术培训改善患者护理的技术至关重要。我们本研究的目的是开发和验证使用手术现场视频进行技术技能评估的深度学习技术。
我们使用了 99 个白内障囊外切除术视频数据集，这是白内障手术的关键步骤。一位专家外科医生使用标准的结构化评分量表，即国际眼科理事会眼科手术能力评估量表：白内障超声乳化术（ICO-OSCAR:phaco），对每个视频进行技术技能注释。我们在该量表中的两个白内障囊外切除术指数（瓣开始和跟进、形成和完成）中指定了一个专家表现，当至少一个指数为 5 且另一个指数至少为 4 时，并且否则为新手。此外，我们将白内障囊外切除术开始和白内障囊外切除术形成的分数作为单独的地面真相（2 到 5 的李克特量表；分析为 2/3、4 和 5）。我们众包了器械尖端的注释。我们分别使用时间卷积神经网络对器械轨迹和光流进行建模，以预测技能等级（专家/新手）并对 ICO-OSCAR:phaco 中的每个白内障囊外切除术项目进行评分。我们在五重交叉验证中评估了算法，并计算了准确性和接收器操作特性曲线下的面积（AUC）。
器械尖端速度的准确性和 AUC 分别为 0.848 和 0.863，光流场分别为 0.634 和 0.803。
深度学习网络有效地对白内障囊外切除术中的手术技术技能进行建模，给出了术中数据的结构化表示，例如从视频或众包工具定位信息中提取的光流场。
