arXiv 2602.033022026-02-03cs.CVcs.AI

通过基于基础模型的AI实现3D OCT的全流程诊断工作流程自动化用于视网膜疾病

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

Sun Yat-sen University（中山大学）；Southern University of Science and Technology（南方科技大学）；The University of Hong Kong（香港大学）；The Hong Kong Polytechnic University（香港理工大学）；Zhongshan City People's Hospital（中山市人民医院）；Guangdong Medical University（广东医科大学）；The Third Affiliated Hospital of Sun Yat-sen University（中山大学附属第三医院）；Zhaoqing Gaoyao People’s Hospital（肇庆高要人民医院）；Tianjin Medical University Eye Hospital（天津医科大学眼科医院）；Capital Medical University（首都医科大学）；Beijing Tongren Hospital（北京同仁医院）

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, H… 展开作者

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

DOI：: 10.1038/s41746-026-03151-x

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AI总结：

FOCUS通过基于基础模型的AI实现3D OCT视网膜疾病诊断的端到端自动化，提高了诊断效率和准确性。

摘要：

光学相干断层扫描（OCT）凭借其高分辨率和三维成像特性，已彻底改变了视网膜疾病诊断，但其在临床实践中实现全面诊断自动化仍受到多阶段工作流程和传统单切片单任务AI模型的限制。我们提出了基于OCT的全流程临床效用系统（FOCUS），一种基于基础模型的框架，能够实现3D OCT视网膜疾病诊断的端到端自动化。FOCUS依次使用EfficientNetV2-S进行图像质量评估，然后使用微调的视觉基础模型进行异常检测和多疾病分类。关键的是，FOCUS利用统一的自适应聚合方法，智能地将2D切片级预测整合到综合的3D患者级诊断中。在3300名患者（40672个切片）上进行训练和测试，并在1345名患者（18498个切片）上进行外部验证，这些患者来自四个不同层级的中心和多种OCT设备。FOCUS在图像质量评估（F1分数99.01%）、异常检测（F1分数97.46%）和患者级诊断（F1分数94.39%）方面均取得了高分数。在不同中心的现实世界验证中，也显示了稳定的性能（F1：90.22%-95.24%）。在人机比较中，FOCUS在异常检测（F1：95.47% vs 90.91%）和多疾病诊断（F1：93.49% vs 91.35%）方面与专家表现相当，同时在效率上表现出优势。FOCUS自动化了图像到诊断的流程，代表了迈向无人化眼科的重大进展，提供了一个经过验证的蓝图，用于自主筛查以增强人群规模视网膜护理的可及性和效率。

英文摘要：

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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