S2S: A deep learning method for the radiological diagnosis of fine-grained diseases spanning screening to subtyping.

Abstract

Radiographic images play a critical role in disease diagnosis, but accurately interpreting them requires considerable expertise and workload. Recent research has advanced artificial intelligence-based medical image analysis, but such advancements remain limited in real clinical practice where multistage diagnosis is required for fine-grained diseases. This study proposes a screening-to-subtyping (S2S) AI paradigm specifically designed for accurate radiological diagnosis of fine-grained diseases, encompassing the entire diagnostic process from initial screening to final subtyping. The S2S framework integrates information from multiple diagnostic phases, radiological viewpoints, lesion dimensions, and imaging modalities to address complex diagnostic challenges. Evaluation using a large-scale, multi-center radiography dataset of fine-grained thoracic cancer subtypes demonstrates the system's robust performance. Furthermore, this investigation offers novel insights into human-AI collaboration for diagnosing intricate fine-grained pathologies. Our results highlight the substantial clinical potential of S2S AI across varied healthcare environments and disease entities, facilitating deeper integration of artificial intelligence in radiological diagnostics.