Diagnosis of Irritant Dermatitis in Colorectal Cancer Postoperative Stoma Patients Using Smartphone Photographs: A Deep Learning Approach

Background: Irritant dermatitis is a common complication among stoma patients, significantly impacting their quality of life. Early diagnosis is essential, but limited access to healthcare and poor self-management skills often delay treatment. This study aimed to assess the effectiveness of two advanced convolutional neural networks (CNNs), ConvNeXt and MobileViT, for the intelligent diagnosis of irritant dermatitis using smartphone-acquired stoma images. Methods: A retrospective observational study was conducted, collecting 825 stoma complication images from five tertiary hospitals in China. Data preprocessing techniques such as resampling and enhancement were used to prepare the dataset. The ConvNeXt and MobileViT models were trained and evaluated based on accuracy, precision, recall, and F1 scores. Optimizers and learning rates were also adjusted to assess model performance. Results: ConvNeXt demonstrated superior performance, achieving an accuracy of 71.4%, precision of 73.6%, recall of 67.1%, and an F1 score of 70.2% with the Adam optimizer and a 0.001 learning rate. MobileViT, despite being more lightweight, did not surpass ConvNeXt, with a maximum accuracy of 64.4%. ConvNeXt excelled in diagnosing irritant dermatitis and normal stoma conditions but showed limitations in recognizing other complications. Conclusion: The ConvNeXt model outperformed MobileViT, indicating that advanced CNNs can effectively assist in the early diagnosis of irritant dermatitis among stoma patients. This could help alleviate the burden on healthcare resources and improve patient outcomes through accessible mobile-based diagnostic tools.