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Artificial Intelligence for the Estimation of Visual Acuity Using Multi-Source Anterior Segment Optical Coherence Tomographic Images in Senile Cataract

Authors
 Hyunmin Ahn  ;  Ikhyun Jun  ;  Kyoung Yul Seo  ;  Eung Kweon Kim  ;  Tae-Im Kim 
Citation
 FRONTIERS IN MEDICINE, Vol.9 : 871382, 2022-05 
Journal Title
FRONTIERS IN MEDICINE
Issue Date
2022-05
Keywords
artificial intelligence ; cataract ; convolutional neural network ; optical coherence tomography ; visual acuity
Abstract
Purpose: To investigate an artificial intelligence (AI) model performance using multi-source anterior segment optical coherence tomographic (OCT) images in estimating the preoperative best-corrected visual acuity (BCVA) in patients with senile cataract.

Design: Retrospective, cross-instrument validation study.

Subjects: A total of 2,332 anterior segment images obtained using swept-source OCT, optical biometry for intraocular lens calculation, and a femtosecond laser platform in patients with senile cataract and postoperative BCVA ≥ 0.0 logMAR were included in the training/validation dataset. A total of 1,002 images obtained using optical biometry and another femtosecond laser platform in patients who underwent cataract surgery in 2021 were used for the test dataset.

Methods: AI modeling was based on an ensemble model of Inception-v4 and ResNet. The BCVA training/validation dataset was used for model training. The model performance was evaluated using the test dataset. Analysis of absolute error (AE) was performed by comparing the difference between true preoperative BCVA and estimated preoperative BCVA, as ≥0.1 logMAR (AE≥0.1) or <0.1 logMAR (AE <0.1). AE≥0.1 was classified into underestimation and overestimation groups based on the logMAR scale.

Outcome measurements: Mean absolute error (MAE), root mean square error (RMSE), mean percentage error (MPE), and correlation coefficient between true preoperative BCVA and estimated preoperative BCVA.

Results: The test dataset MAE, RMSE, and MPE were 0.050 ± 0.130 logMAR, 0.140 ± 0.134 logMAR, and 1.3 ± 13.9%, respectively. The correlation coefficient was 0.969 (p < 0.001). The percentage of cases with AE≥0.1 was 8.4%. The incidence of postoperative BCVA > 0.1 was 21.4% in the AE≥0.1 group, of which 88.9% were in the underestimation group. The incidence of vision-impairing disease in the underestimation group was 95.7%. Preoperative corneal astigmatism and lens thickness were higher, and nucleus cataract was more severe (p < 0.001, 0.007, and 0.024, respectively) in AE≥0.1 than that in AE <0.1. The longer the axial length and the more severe the cortical/posterior subcapsular opacity, the better the estimated BCVA than the true BCVA.

Conclusions: The AI model achieved high-level visual acuity estimation in patients with senile cataract. This quantification method encompassed both visual acuity and cataract severity of OCT image, which are the main indications for cataract surgery, showing the potential to objectively evaluate cataract severity.
Files in This Item:
T202202009.pdf Download
DOI
10.3389/fmed.2022.871382
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Ophthalmology (안과학교실) > 1. Journal Papers
Yonsei Authors
Kim, Eung Kweon(김응권) ORCID logo https://orcid.org/0000-0002-1453-8042
Kim, Tae-Im(김태임) ORCID logo https://orcid.org/0000-0001-6414-3842
Seo, Kyoung Yul(서경률) ORCID logo https://orcid.org/0000-0002-9855-1980
Ahn, Hyunmin(안현민)
Jun, Ik Hyun(전익현) ORCID logo https://orcid.org/0000-0002-2160-1679
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/188645
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