Enhancing Post-Surgical Wound Care in Anorectal Diseases: A Comparative Study of Advanced Convolutional Neural...

Anorectal diseases, often requiring surgical intervention and careful post-operative wound management, pose substantial challenges in healthcare. This study presents a novel application of artificial intelligence, specifically machine learning, aimed at improving the classification and analysis of post-surgical wound images. By doing so, it seeks to enhance patient outcomes through personalized and optimized wound care strategies.

This research utilizes convolutional neural networks (CNNs) and employs three advanced architectures-MobileNet, ResNet50, and Inception-v4-to detect and classify key characteristics of post-surgical wounds, including size, location, severity, and tissue type involved. Additionally, the study integrates Gradient-weighted Class Activation Mapping (Grad-CAM) technology to provide interpretative insights into the decision-making processes of these algorithms, offering a deeper understanding of model predictions.

The effectiveness of the employed CNN architectures was assessed based on accuracy, precision, and recall metrics. The findings demonstrate that Inception-v4, in particular, exhibits superior performance across all evaluated metrics, underscoring its potential in clinical applications. Grad-CAM visualizations further clarified the rationale behind the model's decisions, enhancing the interpretability of the results.

The integration of machine learning technologies in the classification and analysis of wound images represents a significant advancement in medical image analysis and AI-driven healthcare solutions. This research not only enhances the technical capabilities of AI applications in healthcare but also improves the precision of post-operative care in anorectal surgery, ultimately contributing to better treatment outcomes.