AI Surgical Video Analysis for Objective Performance Assessment
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Solution Overview
Problem
Current methods for assessing surgical performance are manual, subjective, and often provide inconsistent and delayed feedback, lacking objectivity and timeliness, which can impact surgical outcomes and surgeon development.
Innovation Solution
A computer-implemented method using machine learning to analyze surgical videos, generating performance assessments through trained models that process surgical video data, including metrics on surgical instruments, phases, and anterior capsulotomy, providing immediate and objective feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review methods are used to assess surgical performance, then human expertise and judgment can be applied, but the process becomes subjective, inconsistent, and time-consuming
Solution Approach 1:
The patent replaces the manual mechanical review process with an automated computer-based system that uses machine learning models to analyze surgical videos. The system processes video data, identifies surgical instruments and phases, and generates performance metrics automatically, eliminating human subjectivity and time delays while maintaining assessment quality through algorithmic consistency.
Solution Approach 2:
The system enables self-assessment capabilities where the surgical performance evaluation is performed autonomously by the computer system without requiring external human reviewers. The machine learning models independently analyze the video data and generate comprehensive performance reports, allowing the assessment process to serve itself without human intervention.
2Productivity
If manual assessment is performed by reviewers, then detailed feedback can be provided, but reviewer burnout and inconsistency occur due to manual workload
Solution Approach 1:
The patent substitutes the manual reviewer workload with an automated computer-based analysis system. The machine learning models perform all assessment tasks automatically, eliminating reviewer burnout and ensuring consistent application of evaluation criteria across all surgical cases without variation due to human fatigue or subjectivity.
Solution Approach 2:
The system introduces an intermediary computational layer between the surgical video and the final assessment. The machine learning models act as mediators that process video data through multiple analysis stages (instrument detection, phase identification, metric generation) to produce consistent, standardized feedback, removing the direct human element that causes inconsistency.
3Loss of time
If feedback is provided after surgery completion, then surgeon and patient are not interrupted, but the feedback loses timeliness and immediate applicability
Solution Approach 1:
The system performs assessment analysis during or immediately after the surgical procedure while the video data is still being captured or recently recorded. By processing the video data in near-real-time, the system generates feedback before the surgeon leaves the operating room, allowing immediate review and learning while the surgical experience is still fresh in the surgeon's mind.
Solution Approach 2:
The system maintains continuous operation from video capture through analysis to feedback delivery without interruption. The automated processing pipeline runs continuously, analyzing surgical videos as they are recorded and providing seamless, uninterrupted feedback that maintains the momentum of surgical training and improvement.
Data Source
AI summary
Systems and methods for using machine learning to analyze a surgical video and assess performance of a surgeon conducting a surgical procedure which may include receiving surgical video data including one or more images capturing at least a portion of an ophthalmic surgical procedure from a user device. Processing the surgical video data using one or more trained assessment machine learning models to generate one or more assessment metrics, wherein the one or more trained assessment machine learning models are trained using historical ophthalmic surgery data and the one or more assessment metrics include one or more of a surgical instrument metric, a surgical phase metric, or an anterior capsulotomy metric. Generating a performance assessment of the surgeon based upon at least the one or more assessment metrics and providing the performance assessment of the surgeon to the user device.