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Computer-aided detection and abnormality score for the outer retinal layer in optical coherence tomography

 Tyler Hyungtaek Rim  ;  Aaron Yuntai Lee  ;  Daniel S Ting  ;  Kelvin Yi Chong Teo  ;  Hee Seung Yang  ;  Hyeonmin Kim  ;  Geunyoung Lee  ;  Zhen Ling Teo  ;  Alvin Teo Wei Jun  ;  Kengo Takahashi  ;  Tea Keun Yoo  ;  Sung Eun Kim  ;  Yasuo Yanagi  ;  Ching-Yu Cheng  ;  Sung Soo Kim  ;  Tien Yin Wong  ;  Chui Ming Gemmy Cheung 
 BRITISH JOURNAL OF OPHTHALMOLOGY, Vol.106(9) : 1301-1307, 2022-09 
Journal Title
Issue Date
Choroidal Neovascularization* / diagnostic imaging ; Computers ; Humans ; Retina ; Retinal Pigment Epithelium ; Retinitis Pigmentosa* / diagnosis ; Retrospective Studies ; Tomography, Optical Coherence / methods
epidemiology ; imaging ; retina ; telemedicine
Background: To develop computer-aided detection (CADe) of ORL abnormalities in the retinal pigmented epithelium, interdigitation zone and ellipsoid zone via optical coherence tomography (OCT).

Methods: In this retrospective study, healthy participants with normal ORL, and patients with abnormality of ORL including choroidal neovascularisation (CNV) or retinitis pigmentosa (RP) were included. First, an automatic segmentation deep learning (DL) algorithm, CADe, was developed for the three outer retinal layers using 120 handcraft masks of ORL. This automatic segmentation algorithm generated 4000 segmentations, which included 2000 images with normal ORL and 2000 (1000 CNV and 1000 RP) images with focal or wide defects in ORL. Second, based on the automatically generated segmentation images, a binary classifier (normal vs abnormal) was developed. Results were evaluated by area under the receiver operating characteristic curve (AUC).

Results: The DL algorithm achieved an AUC of 0.984 (95% CI 0.976 to 0.993) for individual image evaluation in the internal test set of 797 images. In addition, performance analysis of a publicly available external test set (n=968) had an AUC of 0.957 (95% CI 0.944 to 0.970) and a second clinical external test set (n=1124) had an AUC of 0.978 (95% CI 0.970 to 0.986). Moreover, the CADe highlighted well normal parts of ORL and omitted highlights in abnormal ORLs of CNV and RP.

Conclusion: The CADe can use OCT images to segment ORL and differentiate between normal ORL and abnormal ORL. The CADe classifier also performs visualisation and may aid future physician diagnosis and clinical applications.
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1. College of Medicine (의과대학) > Dept. of Ophthalmology (안과학교실) > 1. Journal Papers
Yonsei Authors
Kim, Sung Soo(김성수) ORCID logo https://orcid.org/0000-0002-0574-7993
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