ADF-OCT: An advanced Assistive Diagnosis Framework for study-level macular optical coherence tomography

Optical coherence tomography (OCT) is an advanced retinal imaging technique that enables non-invasive cross-sectional visualization of the retina, playing a crucial role in ophthalmology for detecting various macular lesions. While deep learning has shown promise in OCT image analysis, existing studies have primarily focused on broad, image-level disease diagnosis. This study introduces the Assistive Diagnosis Framework for OCT (ADF-OCT), which utilizes a dataset of over one million macular OCT images to construct a multi-label diagnostic model for common macular lesions and a medical report generation module. Our innovative Multi-frame Medical Images Distillation method effectively translates study-level multi-label annotations into image-level annotations, thereby enhancing diagnostic performance without additional annotation information. This approach significantly improves diagnostic accuracy for multi-label classification, achieving an impressive AUROC of 0.9891 with best performance macro F1 of 0.8533 and accuracy of 0.9411. By refining the feature fusion strategy in multi-frame medical imaging, our framework substantially enhances the generation of medical reports for OCT B-scans, surpassing current solutions. This research presents an advanced development pipeline that utilizes existing clinical datasets to provide more accurate and comprehensive artificial intelligence-assisted diagnoses for macular OCT. • Collected one million OCT B-scan images of macular region with multi-label diagnoses and reports. • Multi-frame distillation method improves multi-label classification accuracy without extra annotations. • Advanced feature fusion techniques enhance multi-frame image medical report generation.