Surgical Data Repository for Collaborative Guidance and Case Sharing
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Solution Overview
Problem
Surgeons often lack comprehensive knowledge of complex surgical cases due to partial information sharing during peer consultations, and preparing materials for presentations is time-consuming.
Innovation Solution
A cloud-based system enables collaboration by creating a repository of annotated surgical data, using neural networks to process and generate surgical recommendations and templates, and providing real-time surgical guidance through video overlays and 3D modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If surgeons share case details during peer consultations, then collaborative feedback is obtained, but comprehensive knowledge of the case is not achieved
Solution Approach 1:
The system creates a digital copy of the surgical case through annotated surgical reports that capture comprehensive case details, allowing peer surgeons to access complete information without requiring direct observation of the actual surgery. The annotated reports serve as accurate replicas of the surgical procedure that can be shared and reviewed remotely.
Solution Approach 2:
The system introduces an intermediary platform (cloud-based repository with neural networks) that facilitates information transfer between surgeons. This intermediary processes and organizes surgical data, making comprehensive case knowledge accessible to peers who cannot physically observe the surgery, thereby resolving the contradiction between information completeness and collaboration effectiveness.
2Loss of information
If surgeons prepare materials for presentations, then comprehensive case information is available, but preparation time is consumed
Solution Approach 1:
The system performs preliminary processing of surgical data by automatically generating annotated surgical reports and creating a structured repository before the surgeon needs to present or collaborate. Neural networks pre-process the data to extract key information, generate recommendations, and organize materials, eliminating the need for surgeons to manually prepare presentation materials from raw surgical data.
Solution Approach 2:
The system enables self-service by allowing the neural network to automatically process surgical data, generate annotated reports, and create presentation materials without requiring surgeon intervention. The system serves itself by extracting insights, organizing information, and preparing collaborative materials autonomously, freeing surgeons from time-consuming preparation tasks.
3Loss of information
If surgical data is shared with peers, then collaborative feedback is received, but patient identifying data may be exposed
Solution Approach 1:
The system extracts only the necessary surgical information from the complete patient record, separating clinically relevant case details from patient identifying data. The neural network processes and extracts only the surgical procedure information needed for collaborative feedback, while automatically removing or redacting personal identifiers, thereby enabling safe data sharing without privacy risks.
Solution Approach 2:
The system introduces an intermediary processing layer (neural network-based system) between the raw surgical data and the peer surgeons. This intermediary acts as a filter that processes the data to remove patient identifying information while preserving the surgical case details, allowing safe transmission of case information to peers without exposing patient privacy.
Data Source
AI summary
Apparatuses, systems, and methods are disclosed to manage and process surgical data, and enable collaboration between two or more surgeons or other clinicians. A repository may be created that includes surgical data (including video data and/or 2024/030683 or radiological image data) that may be selectively shared between medical professionals. One or more trained neural networks may process the annotated surgical reports in order to populate the repository. Additional trained neural networks may generate surgical recommendations in response to user requests, based on contents of the repository. Other trained neural networks may generate surgical templates to guide a surgeon during an operation.