3D Surgical Model Interface for Real-Time Body Structure Assessment
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Solution Overview
Problem
Current methods for reviewing surgical procedures, particularly those involving internal body structures, are inefficient and ineffective due to the need for manual analysis of extensive raw video data, which can lead to missed events and misinterpretation, and are not well-suited for real-time feedback during surgeries.
Innovation Solution
A computer-based system that automates the review process by generating three-dimensional models of internal body structures from camera data, using neural networks to enhance depth estimation and pose determination, and provides a graphical user interface for real-time and offline analysis, highlighting important regions and lacunae in the examination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of raw surgical video is performed, then reviewers can examine the procedure in detail, but the review process becomes time-consuming and inefficient
Solution Approach 1:
The patent introduces an automated analysis system that acts as an intermediary between the raw surgical video and human reviewers. This system processes the video footage, generates structured data about surgical events and anatomical structures, and presents highlighted summaries to reviewers, thereby reducing their time burden while maintaining review accuracy
Solution Approach 2:
The patent replaces the mechanical process of manual video review with an automated computational system using machine learning models. The system automatically detects surgical events, segments video footage, generates three-dimensional models of internal structures, and identifies key moments, substituting human effort with automated algorithms
2Speed
If real-time review of surgical video is performed, then feedback can be provided during surgery, but it may distract team members from their responsibilities
Solution Approach 1:
The patent creates a parallel virtual representation of the surgical field through automated three-dimensional model generation from video footage. This virtual copy provides real-time feedback about anatomical structures and surgical progress without requiring team members to divert attention from actual surgical tasks, as the feedback is generated and displayed separately
3Reliability
If extensive raw video data is reviewed manually, then comprehensive assessment is possible, but reviewers may miss events or misinterpret them due to limited attention spans
Solution Approach 1:
The patent automatically segments the continuous surgical video into discrete events and anatomical structures using machine learning detection. This segmentation organizes the complex video data into manageable, labeled segments that highlight key moments and structures, making comprehensive assessment reliable without requiring reviewers to maintain attention throughout the entire procedure
4Measurement precision
If offline review of surgical video is performed, then thorough analysis is possible, but it requires ignoring many irrelevant sequences
Solution Approach 1:
The patent performs preliminary automated analysis of the surgical video before human review, detecting events, segmenting footage, generating three-dimensional models, and identifying relevant sequences in advance. This preliminary action filters out irrelevant content and prepares structured data, allowing reviewers to focus only on significant portions while maintaining assessment quality
Data Source
AI summary
Various of the disclosed embodiments relate to systems and methods for determining and for presenting surgical examination data of an internal body structure, such as a large intestine. For example, various of the disclosed methods may create a three-dimensional computer model using the examination data and then coordinate playback of the examination data based upon the reviewer's interaction with the model. In some embodiments, the model's rendering may be adjusted to reflect various aspects of the examination, including scoring metrics, identified landmarks, and lacunae in the surgical examination review.