Abstract
The limited number of specialists and diseases' long-tail distribution create challenges in diagnosing oral tumors. Health care facilities with sole practicing pathologists face difficulties when encountering the rare cases. Such specialists may lack prior exposure to uncommon presentations, needing external reference materials to formulate accurate diagnoses. An image search or content-based image retrieval (CBIR) system may help diagnose rare tumors by providing histologically similar reference images, thus reducing the pathologists' workload. However, the effectiveness of CBIR systems in aiding pathologists' diagnoses through interactive use has not been evaluated. We conducted a remote evaluation in a near-clinical environment using Luigi-Oral, an interactive patch-based CBIR system that uses deep learning to diagnose oral tumors. The database comprised 54,676 image patches at multiple magnifications from 603 cases across 85 oral tumor categories. We recruited 15 general pathologists and 13 oral pathologists with varied experience to evaluate 10 retrospective test cases from 2 institutions using this dedicated system. At top-1 and top-3 differential diagnoses, the overall diagnostic accuracy among the 2 groups was significantly higher with Luigi-Oral than without (12.05% and 21.61% increase, P = 0.002 and P < 0.001, respectively). Improvements were more evident for tumor cases in which the category was underrepresented in the database, benefiting novice and experienced pathologists. Misdiagnoses using Luigi-Oral could be due to inappropriate query input, poor retrieval performance in cases with a rare morphologic type, the difficulty of diagnosis without elaborate clinical information, or the system's inability to retrieve accurate categories with convincing images. This study proves the clinical usability of an interactive CBIR system and highlights areas for improvement to ensure adequate assistance for pathologists, which potentially reduces pathologists' workload and provides accessible specialist-level histopathology diagnosis.