Surgical Support Device for Loose Connective Tissue Recognition
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Solution Overview
Problem
Identifying loose connective tissue in laparoscopic surgery is challenging due to the presence of blood vessels, nerves, and various cells surrounding the fibers, making it difficult for surgeons to accurately locate and resect during procedures.
Innovation Solution
A surgical support device equipped with a learning model that recognizes loose connective tissue in surgical field images using a neural network-based learning model, such as SegNet, to output recognition results and provide support information for laparoscopic surgery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a surgeon visually inspects the surgical field image to identify loose connective tissue, then the surgeon can perform the surgery, but the identification accuracy is low due to the complex surrounding structures
Solution Approach 1:
A learning model serves as an intermediary between the surgical field image and the surgeon's identification process. The learning model automatically detects and highlights loose connective tissue regions, bridging the gap between the complex visual information and accurate identification, thereby improving detection accuracy without increasing surgeon workload
Solution Approach 2:
The manual visual inspection process is supplemented by an automated image recognition system using a learning model. This replaces the mechanical/visual search process with an automated computational approach that can accurately identify loose connective tissue based on learned patterns from training data
2Productivity
If the surgeon manually searches for loose connective tissue among blood vessels, nerves, and cells, then the surgery can proceed, but the time consumption increases
Solution Approach 1:
The learning model is pre-trained on surgical field images to automatically identify loose connective tissue before the surgeon needs to locate it during the procedure. This preliminary computational analysis prepares the information in advance, allowing the surgeon to quickly access identified regions without manual searching during the time-critical surgical procedure
3Measurement precision
If the surgeon relies on visual inspection alone, then no additional equipment is needed, but the recognition accuracy of loose connective tissue is insufficient
Solution Approach 1:
The learning model system is designed to be integrated into existing surgical workflows and can be implemented on various computing platforms. The same core learning model can recognize loose connective tissue across different surgical contexts and image types, providing universal functionality that improves recognition accuracy without requiring entirely new specialized equipment for each scenario
Data Source
AI summary
A computer readable non-transitory recording medium storing a computer program causing a computer to execute processing of: acquiring an operation field image obtained by shooting an operation field of an endoscopic surgery; and recognizing a loose connective tissue part included in the surgical field image by inputting the surgical field image acquired to a learning model so trained as to output information related to loose connective tissue when the operation field image is input.