Using Apple Machine Learning Algorithms to Detect and Subclassify Non-Small Cell Lung Cancer
This addresses the need for accurate lung cancer diagnosis and subclassification to guide targeted therapy, particularly in understaffed areas, though it is incremental as it applies an existing method to a new medical dataset.
The study tackled the problem of detecting and subclassifying non-small cell lung cancer from histopathological images using Apple's Create ML module, achieving 100% detection and successful subclassification of most images.
Lung cancer continues to be a major healthcare challenge with high morbidity and mortality rates among both men and women worldwide. The majority of lung cancer cases are of non-small cell lung cancer type. With the advent of targeted cancer therapy, it is imperative not only to properly diagnose but also sub-classify non-small cell lung cancer. In our study, we evaluated the utility of using Apple Create ML module to detect and sub-classify non-small cell carcinomas based on histopathological images. After module optimization, the program detected 100% of non-small cell lung cancer images and successfully subclassified the majority of the images. Trained modules, such as ours, can be utilized in diagnostic smartphone-based applications, augmenting diagnostic services in understaffed areas of the world.