Full end-to-end diagnostic workflow automation of 3D OCT via foundation model-driven AI for retinal diseases

• URL: http://arxiv.org/abs/2602.03302v1
• Date: Tue, 03 Feb 2026 09:28:51 GMT
• Title: Full end-to-end diagnostic workflow automation of 3D OCT via foundation model-driven AI for retinal diseases
• Authors: Jinze Zhang, Jian Zhong, Li Lin, Jiaxiong Li, Ke Ma, Naiyang Li, Meng Li, Yuan Pan, Zeyu Meng, Mengyun Zhou, Shang Huang, Shilong Yu, Zhengyu Duan, Sutong Li, Honghui Xia, Juping Liu, Dan Liang, Yantao Wei, Xiaoying Tang, Jin Yuan, Peng Xiao,
• Abstract summary: We present Full-process OCT-based Clinical Utility System (FOCUS)<n>FOCUS is a foundation model-driven framework enabling end-to-end automation of 3D OCT retinal disease diagnosis.<n>We trained and tested on 3,300 patients (40,672 slices), and externally validated on 1,345 patients (18,498 slices) across four different-tier centers and diverse OCT devices.
• Score: 14.269884626099625
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Optical coherence tomography (OCT) has revolutionized retinal disease diagnosis with its high-resolution and three-dimensional imaging nature, yet its full diagnostic automation in clinical practices remains constrained by multi-stage workflows and conventional single-slice single-task AI models. We present Full-process OCT-based Clinical Utility System (FOCUS), a foundation model-driven framework enabling end-to-end automation of 3D OCT retinal disease diagnosis. FOCUS sequentially performs image quality assessment with EfficientNetV2-S, followed by abnormality detection and multi-disease classification using a fine-tuned Vision Foundation Model. Crucially, FOCUS leverages a unified adaptive aggregation method to intelligently integrate 2D slices-level predictions into comprehensive 3D patient-level diagnosis. Trained and tested on 3,300 patients (40,672 slices), and externally validated on 1,345 patients (18,498 slices) across four different-tier centers and diverse OCT devices, FOCUS achieved high F1 scores for quality assessment (99.01%), abnormally detection (97.46%), and patient-level diagnosis (94.39%). Real-world validation across centers also showed stable performance (F1: 90.22%-95.24%). In human-machine comparisons, FOCUS matched expert performance in abnormality detection (F1: 95.47% vs 90.91%) and multi-disease diagnosis (F1: 93.49% vs 91.35%), while demonstrating better efficiency. FOCUS automates the image-to-diagnosis pipeline, representing a critical advance towards unmanned ophthalmology with a validated blueprint for autonomous screening to enhance population scale retinal care accessibility and efficiency.

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