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一种深度学习方法,用于在 fundus 照片上准确地区分光盘和乳头之间
Kanchalika Sathianvichitr1, Raymond P Najjar, Tang Zhiqun
1Singapore Eye Research Institute (KS, RPN, TZ, DM), Singapore, Singapore; Duke-NUS Medical School (RPN, MJAG, DM), National University of Singapore, Singapore, Singapore; Department of Ophthalmology (RPN), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Departments of Clinical Neurological Sciences and Ophthalmology (JAF), Western University, London, Canada; Department of Neuro-Ophthalmology (CWLY, DM), Singapore National Eye Centre, Singapore, Singapore; Ophthalmic Engineering & Innovation Laboratory (MJAG), Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore; Institute for Molecular and Clinical Ophthalmology (MJAG), Basel, Switzerland; Departments of Clinical Neurosciences and Surgery (FC), University of Calgary, Calgary, Canada; Department of Medicine (MYL), Emory University School of Medicine, Atlanta, Georgia; Department of Ophthalmology (MYL, NJN, VB), Emory Eye Center, Emory University School of Medicine, Atlanta, Georgia; Eye Center (WAL), Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; Save Sight Institute (CLF), Faculty of Health and Medicine, The University of Sydney, New South Wales, Australia; Department of Ophthalmology (SH, DM), Rigshospitalet, University of Copenhagen, Copenhagen, Denmark; Department of Neurology (NJN, VB), Emory University School of Medicine, Atlanta, Georgia; Department of Neurological Surgery (NJN), Emory University School of Medicine, Atlanta, Georgia; and Rothschild Foundation Hospital (CV-C, DM), Paris, France.
一个深度学习系统使用 fundus 图像准确地区分光盘干 (ODD) 和 papilledema. 这种人工智能工具有助于诊断模仿内高血压的疾病.
科学领域:
- 眼科医生 眼科 眼科
- 人工智能的人工智能
- 神经眼科 神经眼科
背景情况:
- 光盘干燥 (ODD) 是 papilledema 的关键差异诊断,经常带来诊断挑战.
- 区分ODD和 papilledema对于准确诊断和管理内高血压至关重要.
研究的目的:
- 开发和验证一个深度学习系统 (DLS) 来分类ODD与 papilledema.
- 通过对眼底部照片的大型,国际,多民族数据集来评估DLS的性能.
主要方法:
- 对来自30个国际神经眼科中心的2,180名患者的4,508张彩色眼底图像进行了回顾性分析.
- 在培训和内部验证中,使用了857张ODD和3230张乳腺瘤图像.
- 外部测试涉及207个ODD和214个乳腺瘤图像的独立数据集.
主要成果:
- DLS在区分ODD和 papilledema方面取得了很高的准确性 (AUC为0.97,准确率为90.5%).
- 在区分埋藏的ODD和轻度至中度的乳头瘤 (AUC 0.93,准确率84.2%) 方面,表现仍然强.
- 在整体分类任务中观察到高灵敏度 (86.0%) 和特异性 (94.9%).
结论:
- 一个专门的DLS可以可靠地区分ODD和 papilledema.
- 即使在具有挑战性的病例中,DLS也表现出有效性,例如区分埋藏的ODD和轻度至中度的乳头炎.

