Video Surgical Report Generation via Machine Learning Pipeline
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Solution Overview
Problem
Generating surgical reports is a time-consuming and inaccurate process as it relies on surgeons recalling events post-surgery, leading to potential omission of key details and inefficiencies in report creation.
Innovation Solution
A machine learning pipeline is developed to automatically generate video surgical reports by analyzing surgical video feeds, identifying key events, and curating content, including text, audio, and video, with minimal manual input from surgeons, utilizing models for information distillation, extraction, language generation, and video creation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If surgeons manually recall and document surgical events after surgery, then the report can be generated with surgeon's professional judgment, but the process is time-consuming and may omit key details
Solution Approach 1:
The system performs preliminary action by automatically generating the surgical report during or immediately after the procedure using real-time video analysis, eliminating the need for delayed manual recall. The machine learning models process surgical video feeds contemporaneously to extract key events and generate report content, ensuring both accuracy and time efficiency.
Solution Approach 2:
The system creates a digital copy of the surgical procedure through video analysis, using machine learning models to automatically transcribe and structure key events from video feeds into report format. This copying approach preserves surgical details objectively without relying on surgeon memory, while the automated process significantly reduces documentation time.
2Loss of information
If surgeons manually document all surgical events, then comprehensive coverage is achieved, but the process becomes excessively time-consuming
Solution Approach 1:
The system extracts only the most relevant surgical events from video feeds using machine learning models trained to identify key procedural steps and anomalies. Instead of documenting everything, the system selectively extracts critical information such as incisions, sutures, complications, and notable anatomical observations, achieving comprehensive coverage of important events while maintaining high productivity.
Solution Approach 2:
The system applies local quality by differentiating between various types of surgical events and applying appropriate levels of documentation detail. Critical events receive more thorough analysis and documentation, while routine procedural steps are captured more efficiently. The machine learning models adjust their attention and processing depth based on the significance of each surgical event.
3Measurement precision
If surgeons recall surgical events after the procedure, then professional judgment can be applied, but memory limitations cause omission of details
Solution Approach 1:
The system creates an objective digital copy of the surgical procedure through video analysis, eliminating memory-dependent recall. Machine learning models process video feeds to automatically capture and structure surgical events with high precision, preserving all visual details without the degradation that occurs with human memory over time.
Solution Approach 2:
The system implements feedback by continuously analyzing video feeds in real-time and comparing detected events against expected surgical patterns. This ongoing feedback loop allows the machine learning models to adjust their detection accuracy and maintain high measurement precision throughout the procedure, capturing events with greater reliability than post-procedure recall.
Data Source
AI summary
Disclosed herein are methods for generating a video surgical report using a machine learning pipeline. The machine learning pipeline may include one or more machine learning models, each of which may support a particular aspect of a video surgical report generation process. For example, one or more images of a surgical procedure may be obtained. Using one or more machine learning models, a set of images from the one or more images may be selected based on the surgical procedure. A video surgical report may be generated for the surgical procedure, which may include at least some of the set of images. The machine learning pipeline can offload work typically performed by a user (e.g., surgeon, medical staff, etc.) to create the video surgical report, thereby saving significant time and/or resources.