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.

翻译：光学相干断层扫描（OCT）凭借其高分辨率与三维成像特性，已彻底改变了视网膜疾病的诊断模式。然而，其在临床实践中实现全流程自动化诊断仍受限于多阶段工作流程及传统的单切片单任务人工智能模型。本研究提出基于全流程OCT的临床实用系统（FOCUS），这是一个由基础模型驱动的框架，能够实现三维OCT视网膜疾病诊断的端到端自动化。FOCUS依次执行以下流程：首先采用EfficientNetV2-S进行图像质量评估，随后通过微调的视觉基础模型完成异常检测与多疾病分类。尤为关键的是，FOCUS采用统一的自适应聚合方法，将二维切片层级的预测结果智能整合为全面的三维患者层级诊断。该系统在包含3,300名患者（40,672张切片）的数据集上进行训练与测试，并在涵盖四个不同层级医疗中心、多种OCT设备的1,345名患者（18,498张切片）外部验证集中表现出色：质量评估F1分数达99.01%，异常检测达97.46%，患者层级诊断达94.39%。多中心真实世界验证显示其性能保持稳定（F1分数范围：90.22%-95.24%）。在人机对比实验中，FOCUS在异常检测（F1分数：95.47% vs 90.91%）与多疾病诊断（F1分数：93.49% vs 91.35%）方面达到专家水平，同时展现出更优的运算效率。FOCUS实现了从图像到诊断的全流程自动化，标志着向无人值守眼科诊疗迈出关键一步，其经过验证的自主筛查框架为提升人群级视网膜健康服务的可及性与效率提供了重要蓝图。