CatSkill: Artificial Intelligence-Based Metrics for the Assessment of Surgical Skill Level from Intraoperative Cataract Surgery Video Recordings

IF 3.2 Q1 OPHTHALMOLOGY
Binh Duong Giap PhD , Dena Ballouz MD , Karthik Srinivasan MD, MS , Jefferson Lustre BS , Keely Likosky BS , Ossama Mahmoud MD , Shahzad I. Mian MD , Bradford L. Tannen MD, JD , Nambi Nallasamy MD
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引用次数: 0

Abstract

Purpose

To develop and validate a novel artificial intelligence (AI)–powered video analysis system to assess surgeon proficiency in maintaining (1) eye neutrality, (2) eye centration, and (3) adequate focus of the operating microscope in cataract surgery and evaluate differences in these metrics between attending cataract surgeons and ophthalmology residents.

Design

A retrospective surgical video analysis.

Subjects

Six hundred twenty complete surgical video recordings of 620 cataract surgeries performed by either attending surgeons or ophthalmology residents.

Main Outcome Measures

Performance of the proposed AI-powered video analysis system (CatSkill) for cataract surgery was evaluated at multiple stages. Anatomy and surgical landmark segmentation were reported as Dice coefficients. The proposed cataract surgery assessment metrics (CSAMs) were compared between attending and resident surgeons on a phase-wise basis. Surgery-level classification performance (attending vs. resident) of a machine learning (ML) algorithm trained on the CSAMs was assessed using area under the receiver operating characteristic curve (AUC).

Methods

An automated system involving video preprocessing, deep learning–based segmentation with limbus obstruction detection and compensation, and CSAM computation was designed to assess surgeon performance based on surgical videos. Three CSAMs were computed to analyze 430 cataract surgeries (254 attendings and 176 residents). An ML algorithm was developed to predict surgeon training level using only CSAMs.

Results

The CatSkill system using FPN (VGG16) achieved a Dice coefficient of 94.03% for segmentation of palpebral fissure, limbus, and Purkinje image 1. The phase-wise mean CSAM scores were higher for attendings than residents across all surgical phases. Residents struggled with stability/centration during the Main Wound, Cortical Removal, Lens Insertion, and Wound Closure phases, and had difficulty maintaining adequate microscope focus during later phases of surgery. A random forest model using CSAMs achieved an AUC of 0.865 in predicting the skill level (attending or resident) of the surgeon.

Conclusions

The proposed AI-derived CSAMs provide a high level of reliability in assessing the ability of surgeons to maintain eye neutrality, centration, and focus level during cataract surgery. Furthermore, downstream analysis using an ML model for surgical-level classification indicates that the proposed CSAMs provide significant predictive value for assessing the overall training level of the surgeon.

Financial Disclosure(s)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
CatSkill:基于人工智能的评估白内障手术录像中手术技能水平的指标
目的:开发并验证一种新的人工智能(AI)视频分析系统,以评估外科医生在维持(1)眼睛中性、(2)眼睛集中和(3)白内障手术中操作显微镜足够聚焦方面的熟练程度,并评估白内障外科医生和眼科住院医生在这些指标上的差异。设计回顾性手术录像分析。研究对象620例白内障手术的620个完整的手术录像,由主治医生或眼科住院医师进行。本研究对人工智能驱动的白内障手术视频分析系统(CatSkill)的性能进行了多个阶段的评估。解剖和外科标志分割报告为Dice系数。提出的白内障手术评估指标(CSAMs)在主治和住院医生之间进行阶段性比较。在csam上训练的机器学习(ML)算法的手术级别分类性能(主治与住院)使用接受者工作特征曲线下的面积(AUC)进行评估。方法设计一个基于视频预处理、边缘阻塞检测与补偿的深度学习分割和CSAM计算的自动化系统,基于手术视频对外科医生的表现进行评估。计算了3个csam,分析了430例白内障手术(254名主治医生和176名住院医生)。开发了一种ML算法,仅使用csam来预测外科医生的培训水平。结果基于FPN (VGG16)的CatSkill系统对睑裂、睑缘和浦肯野图像的分割准确率达到94.03%。在所有手术阶段,主治医生的平均CSAM评分高于住院医生。住院医师在主要创面、皮质移除、晶状体植入和创面闭合阶段难以保持稳定/集中,并且在手术后期难以保持足够的显微镜聚焦。使用csam的随机森林模型在预测外科医生的技能水平(主治或住院医师)方面的AUC为0.865。结论人工智能衍生的csam在评估外科医生在白内障手术中维持眼睛中立、集中和聚焦水平的能力方面提供了高水平的可靠性。此外,使用ML模型进行外科水平分类的下游分析表明,所提出的csam对评估外科医生的整体培训水平具有重要的预测价值。财务披露专有或商业披露可在本文末尾的脚注和披露中找到。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Ophthalmology science
Ophthalmology science Ophthalmology
CiteScore
3.40
自引率
0.00%
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0
审稿时长
89 days
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