Blood Presence Detection via Machine Learning in Surgery
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Solution Overview
Problem
Intraoperative bleeding during minimally invasive surgery is difficult to detect due to limited visibility and mobility, leading to potential complications and increased risk of additional procedures or surgeries.
Innovation Solution
A system utilizing a camera and machine-learned model to analyze images from the surgical site, calculating a blood presence score and providing feedback to surgeons, which can highlight bleeding locations and suggest improvements in surgical techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Strength
If minimally invasive surgery techniques are used to reduce tissue damage, then tissue damage is reduced, but the ability to detect bleeding is worsened due to limited visibility and mobility
Solution Approach 1:
The patent introduces an intermediary detection system consisting of a camera and machine-learned model that mediates between the surgical site and the surgeon's observation. The camera captures images at the surgical site, and the machine-learned model processes these images to detect and quantify bleeding, thereby resolving the visibility limitation imposed by minimally invasive techniques without requiring larger incisions
Solution Approach 2:
The patent replaces the mechanical/physical observation system (surgeon directly viewing the surgical site through incisions) with an automated image processing system. The machine-learned model automatically analyzes camera images to detect blood presence and calculate bleeding scores, substituting human visual inspection with an automated computational system that overcomes the visibility constraints of minimally invasive surgery
2Measurement precision
If real-time bleeding detection is implemented to improve patient safety, then detection accuracy is improved, but device complexity increases due to machine-learned models and image processing systems
Solution Approach 1:
The system employs a self-service mechanism where the machine-learned model automatically processes images and generates bleeding assessments without requiring complex external analysis systems. The model self-evaluates the captured images to produce quantified bleeding scores, reducing the need for additional complex diagnostic equipment or manual analysis procedures
Solution Approach 2:
The patent transforms the complex task of bleeding detection into a quantified parameter (blood presence score) that can be directly calculated from image data. By converting visual bleeding assessment into a numerical score based on machine-learned model output, the system simplifies the interpretation of complex image data into actionable quantitative metrics
Data Source
AI summary
A surgery assessment system includes a camera, a processor or controller, and a display. The processor or controller is configured to receive at least one image collected at a surgery site by the camera, analyze the at least one image with a machine-learned model, and calculate a blood presence score for the surgery site based on the analysis of the at least one image. The display is configured to output the blood presence score in association with the surgery site.