Background The ability of deep learning (DL) algorithms to identify eyes with neovascular age-related macular degeneration (nAMD) from optical coherence tomography (OCT) scans has been previously established. We herewith evaluate the ability of a DL model, showing excellent performance on a Korean data set, to generalse onto an American data set despite ethnic differences. In addition, expert graders were surveyed to verify if the DL model was appropriately identifying lesions indicative of nAMD on the OCT scans.
Methods Model development data set—12 247 OCT scans from South Korea; external validation data set—91 509 OCT scans from Washington, USA. In both data sets, normal eyes or eyes with nAMD were included. After internal testing, the algorithm was sent to the University of Washington, USA, for external validation. Area under the receiver operating characteristic curve (AUC) and precision–recall curve (AUPRC) were calculated. For model explanation, saliency maps were generated using Guided GradCAM.
Results On external validation, AUC and AUPRC remained high at 0.952 (95% CI 0.942 to 0.962) and 0.891 (95% CI 0.875 to 0.908) at the individual level. Saliency maps showed that in normal OCT scans, the fovea was the main area of interest; in nAMD OCT scans, the appropriate pathological features were areas of model interest. Survey of 10 retina specialists confirmed this.
Conclusion Our DL algorithm exhibited high performance for nAMD identification in a Korean population, and generalised well to an ethnically distinct, American population. The model correctly focused on the differences within the macular area to extract features associated with nAMD.
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THR and AYL contributed equally.
Contributors THR, AYL, SSK, C-YC, TYW and CMGC conceptualised the study. THR, AYL, DST, KYCT, BKB, TKY, TZL, GL, YK, AL, YCT and SEK reviewed the literature. THR, AYL, SSK, C-YC, TYW, and CMGC designed the study. THR, AYL, GL, YK and SSK collected the data. TR, GL and YK developed the algorithm. THR, AYL, GL, YK, YCT and AL analysed data. All authors contributed to data interpretation. THR, KYCT, BKB, TKY, TZL, YCT, C-YC, TYW and CMGC drafted the manuscript. YCT, DST, TYW and CMGC provided critical revision.
Funding This work was supported by the Agency for Science, Technology and Research of Singapore (A19D1b0095), the National Medical Research Council of Singapore (NMRC/OFLCG/004a/2018; NMRC/CIRG/1488/2018), the National Institutes of Health Grants NIH/NEI K23EY029246, and by an unrestricted grant from Research to Prevent Blindness. The sponsors or funding organisations had no role in the design or conduct of this research.
Competing interests THR was a scientific advisor to a start-up company called Medi Whale Inc AYL reports support from the US Food and Drug Administration, grants from Santen, Carl Zeiss Meditec, and Novartis, personal fees from Genentech, Topcon, and Verana Health, outside of the submitted work; This article does not reflect the opinions of the Food and Drug Administration. GL and YNK are working in Medi Whale Inc. TYW and DST hold a patent for a deep learning system developed for use in ophthalmology.
Provenance and peer review Not commissioned; externally peer reviewed.
Data availability statement Data cannot be shared publicly due to the violation of patient privacy and lack of informed consent for data sharing. Data are available from the Yonsei University, Department of Ophthalmology (contact Prof. Sung Soo Kim, ) for researchers who meet the criteria for access to confidential data.
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