Automated Skin Lesion Analysis for Surgical Precision
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Solution Overview
Problem
Conventional Mohs Surgery for cancerous skin lesions is time-consuming, costly, and uncomfortable for patients due to the need for multiple iterations of tissue removal and waiting for pathology lab results, which increases the risk of complications and expenses.
Innovation Solution
An automated computer-implemented method and system that uses cloud computing to analyze real-time data from skin lesions, distinguishing affected from unaffected regions, providing immediate feedback to clinicians for precise surgical planning and execution, reducing the need for repeated procedures and enhancing patient comfort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional Mohs Surgery is used to ensure complete removal of cancerous tissue, then reliability of cancerous tissue removal is improved, but loss of time and productivity deteriorate due to multiple iterations and waiting for pathology results
Solution Approach 1:
The system implements real-time feedback by analyzing images of the skin lesion during surgery and immediately providing information about the presence of cancerous tissue. This eliminates the delay of sending samples to a pathology lab and waiting for results, allowing the surgeon to adjust the excision boundaries on the spot and complete the procedure in one iteration while ensuring complete removal of cancerous tissue.
Solution Approach 2:
The system performs preliminary analysis of the skin lesion using machine learning models trained on historical pathology data before and during surgery. This preliminary action identifies the likely boundaries of cancerous tissue, allowing the surgeon to plan the excision more accurately from the start and reduce the need for multiple iterative procedures.
2Reliability
If conventional Mohs Surgery with multiple iterations is performed to ensure complete removal, then reliability of treatment is improved, but device complexity and operational complexity worsen due to coordination with pathology laboratory
Solution Approach 1:
The system performs self-service by incorporating an automated image analysis capability directly at the surgical site. The machine learning model analyzes images of the excised tissue in real-time and provides immediate feedback about the presence of cancerous cells, eliminating the need to send samples to an external pathology laboratory and reducing operational complexity while maintaining reliable detection.
Solution Approach 2:
The system combines multiple functions into a single integrated platform: image capture, real-time machine learning analysis, and surgical guidance. This multi-functional system replaces the need for separate coordination with a pathology laboratory, reducing device complexity while maintaining the reliability of cancerous tissue detection.
3Reliability
If conventional Mohs Surgery is used to ensure complete removal of cancerous tissue, then reliability of treatment is improved, but loss of time and cost worsen due to repeated procedures
Solution Approach 1:
The system provides real-time feedback during the surgical procedure by analyzing images of the excised tissue and immediately informing the surgeon whether cancerous tissue remains. This allows the surgeon to make immediate adjustments to the excision boundaries, eliminating the need for repeated surgical procedures and improving overall treatment efficiency while ensuring complete removal of cancerous tissue.
Solution Approach 2:
The system replaces the traditional mechanical process of sending physical tissue samples to a pathology laboratory with an automated digital image analysis system. The machine learning model processes images of the excised tissue in real-time, providing immediate results without the delays associated with physical sample transport and laboratory processing, thereby improving surgical productivity.
4Productivity
If real-time automated analysis is implemented to reduce surgical time, then productivity is improved, but measurement precision and detection difficulty may worsen
Solution Approach 1:
The machine learning models are trained in advance on extensive datasets of skin lesion images with known pathology outcomes. This preliminary training equips the models with the precision needed for accurate detection, allowing them to provide real-time analysis during surgery without sacrificing measurement precision. The models have already learned to distinguish between cancerous and benign tissue features before being deployed for rapid intraoperative decision-making.
Data Source
AI summary
Embodiments include methods, systems, and computer program products for treating skin lesions. Aspects include receiving an indication that a patient is oriented. Aspects also include acquiring data concerning an area of the patient, the area including a skin lesion. Aspects also include analyzing the data to distinguish between an affected region and an unaffected region of the area of the patient. Aspects also include excising the affected area.