人工智慧可準確檢測眼底視神經乳頭水腫
作者：
小柯機器人發布時間：2020/4/18 9:52:20
新加坡國家眼科中心Tien Y. Wong小組取得一項新突破。他們研究了人工智慧檢測視神經乳頭水腫的效果。 該成果發表在2020年4月14日出版的《新英格蘭醫學雜誌》上。
非眼科醫生無法自信地直接進行檢眼鏡檢查。利用人工智慧是否能從眼底圖像中檢出視神經乳頭水腫和其他視盤異常尚未明確。
研究組對深度學習系統進行了培訓、驗證和外部測試，從15846例回顧性收集的眼底照片中，將視盤分類為正常或視神經乳頭水腫或其他異常。在這些照片中，來自11個國家/地區的19個站點的14341張照片用於培訓和驗證，來自其他5個站點的1505張照片用於外部測試。通過計算接收工作特性曲線下面積（AUC）、靈敏度和特異性來評估視盤外觀分類的性能，並與神經眼科醫生的臨床診斷參考標準進行比較。
培訓和驗證數據集包括來自6779名患者的14341張照片：9156張正常視盤，2148張視神經乳頭水腫和3037張其他異常視盤。各站點歸類為正常視盤的百分比為9.8-100%，歸類為視神經乳頭水腫的百分比為0-59.5%。
在驗證數據集中，系統可將視神經乳頭水腫從正常視盤和非乳頭水腫異常視盤中區分開來，AUC為0.99；亦可將正常視盤從異常視盤中辨別出來，AUC為0.99。在外部測試數據集的1505張照片中，系統檢出視神經乳頭水腫的AUC為0.96，靈敏度為96.4%，特異性為84.7%。
總之，一種深度學習系統，使用眼底照片和藥理擴張的瞳孔，可準確辨別視神經乳頭水腫、正常視盤和非乳頭水腫異常視盤。
附：英文原文
Title: Artificial Intelligence to Detect Papilledema from Ocular Fundus Photographs | NEJM
Author: Dan Milea, M.D., Ph.D.,, Raymond P. Najjar, Ph.D.,, Jiang Zhubo, M.Sc.,, Daniel Ting, M.D., Ph.D.,, Caroline Vasseneix, M.D.,, Xinxing Xu, Ph.D.,, Masoud Aghsaei Fard, M.D.,, Pedro Fonseca, M.D.,, Kavin Vanikieti, M.D.,, Wolf A. Lagrèze, M.D.,, Chiara La Morgia, M.D., Ph.D.,, Carol Y. Cheung, Ph.D.,, Steffen Hamann, M.D., Ph.D.,, Christophe Chiquet, M.D., Ph.D.,, Nicolae Sanda, M.D., Ph.D.,, Hui Yang, M.D., Ph.D.,, Luis J. Mejico, M.D.,, Marie-Bénédicte Rougier, M.D.,, Richard Kho, M.D.,, Tran Thi Ha Chau, M.D.,, Shweta Singhal, M.B., B.S., Ph.D.,, Philippe Gohier, M.D.,, Catherine Clermont-Vignal, M.D.,, Ching-Yu Cheng, M.D., Ph.D., M.P.H.,, Jost B. Jonas, M.D.,, Patrick Yu-Wai-Man, M.B., B.S., Ph.D.,, Clare L. Fraser, M.B., B.S., M.Med.,, John J. Chen, M.D., Ph.D.,, Selvakumar Ambika, D.O., D.N.B.,, Neil R. Miller, M.D.,, Yong Liu, Ph.D.,, Nancy J. Newman, M.D.,, Tien Y. Wong, M.D., Ph.D.,, and Valérie Biousse, M.D.
Issue&Volume: 2020-04-14
Abstract: Abstract
Background
Nonophthalmologist physicians do not confidently perform direct ophthalmoscopy. The use of artificial intelligence to detect papilledema and other optic-disk abnormalities from fundus photographs has not been well studied.
Methods
We trained, validated, and externally tested a deep-learning system to classify optic disks as being normal or having papilledema or other abnormalities from 15,846 retrospectively collected ocular fundus photographs that had been obtained with pharmacologic pupillary dilation and various digital cameras in persons from multiple ethnic populations. Of these photographs, 14,341 from 19 sites in 11 countries were used for training and validation, and 1505 photographs from 5 other sites were used for external testing. Performance at classifying the optic-disk appearance was evaluated by calculating the area under the receiver-operating-characteristic curve (AUC), sensitivity, and specificity, as compared with a reference standard of clinical diagnoses by neuro-ophthalmologists.
Results
The training and validation data sets from 6779 patients included 14,341 photographs: 9156 of normal disks, 2148 of disks with papilledema, and 3037 of disks with other abnormalities. The percentage classified as being normal ranged across sites from 9.8 to 100%; the percentage classified as having papilledema ranged across sites from zero to 59.5%. In the validation set, the system discriminated disks with papilledema from normal disks and disks with nonpapilledema abnormalities with an AUC of 0.99 (95% confidence interval [CI], 0.98 to 0.99) and normal from abnormal disks with an AUC of 0.99 (95% CI, 0.99 to 0.99). In the external-testing data set of 1505 photographs, the system had an AUC for the detection of papilledema of 0.96 (95% CI, 0.95 to 0.97), a sensitivity of 96.4% (95% CI, 93.9 to 98.3), and a specificity of 84.7% (95% CI, 82.3 to 87.1).
Conclusions
A deep-learning system using fundus photographs with pharmacologically dilated pupils differentiated among optic disks with papilledema, normal disks, and disks with nonpapilledema abnormalities.
DOI: 10.1056/NEJMoa1917130
Source: https://www.nejm.org/doi/full/10.1056/NEJMoa1917130