A Deep Learning Framework for the Detection and Quantification of Reticular Pseudodrusen and Drusen on Optical Coherence Tomography.
A Deep Learning Framework for the Detection and Quantification of Reticular Pseudodrusen and Drusen on Optical Coherence Tomography.
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DOI：
10.1167/tvst.11.12.3
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发表时间：
2022-12-01
影响因子：
3
通讯作者：
Tufail, Adnan
中科院分区：
文献类型：
--
作者：
Schwartz, Roy;Khalid, Hagar;Liakopoulos, Sandra;Ouyang, Yanling;de Vente, Coen;Gonzalez-Gonzalo, Cristina;Lee, Aaron Y.;Guymer, Robyn;Chew, Emily Y.;Egan, Catherine;Wu, Zhichao;Kumar, Himeesh;Farrington, Joseph;Mueller, Philipp L.;Sanchez, Clara I.;Tufail, Adnan
关键词：
The purpose of this study was to develop and validate a deep learning (DL) framework for the detection and quantification of reticular pseudodrusen (RPD) and drusen on optical coherence tomography (OCT) scans. A DL framework was developed consisting of a classification model and an out-of-distribution (OOD) detection model for the identification of ungradable scans; a classification model to identify scans with drusen or RPD; and an image segmentation model to independently segment lesions as RPD or drusen. Data were obtained from 1284 participants in the UK Biobank (UKBB) with a self-reported diagnosis of age-related macular degeneration (AMD) and 250 UKBB controls. Drusen and RPD were manually delineated by five retina specialists. The main outcome measures were sensitivity, specificity, area under the receiver operating characteristic (ROC) curve (AUC), kappa, accuracy, intraclass correlation coefficient (ICC), and free-response receiver operating characteristic (FROC) curves. The classification models performed strongly at their respective tasks (0.95, 0.93, and 0.99 AUC, respectively, for the ungradable scans classifier, the OOD model, and the drusen and RPD classification models). The mean ICC for the drusen and RPD area versus graders was 0.74 and 0.61, respectively, compared with 0.69 and 0.68 for intergrader agreement. FROC curves showed that the model's sensitivity was close to human performance. The models achieved high classification and segmentation performance, similar to human performance. Application of this robust framework will further our understanding of RPD as a separate entity from drusen in both research and clinical settings.
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影响因子：
5
作者：
Fry A;Littlejohns TJ;Sudlow C;Doherty N;Adamska L;Sprosen T;Collins R;Allen NE
通讯作者：
Allen NE
影响因子：
13.7
作者：
Guymer, Robyn H.;Wu, Zhichao;Luu, Chi D.
通讯作者：
Luu, Chi D.
影响因子：
13.7
作者：
Domalpally, Amitha;Agron, Elvira;Chew, Emily Y.
通讯作者：
Chew, Emily Y.
影响因子：
13.7
作者：
Hogg, Ruth E.;Silva, Rufino;Chakravarthy, Usha
通讯作者：
Chakravarthy, Usha
影响因子：
3.9
作者：
Bui, Patricia T. A.;Reiter, Gregor S.;Schmidt-Erfurth, Ursula
通讯作者：
Schmidt-Erfurth, Ursula