Machine Learning-based Auto-merge Program for Nine-directional Ocular Photography
10.3341/jkos.2023.64.8.734
- Author:
Shin Hyeong PARK
1
;
Woo Hyuk LEE
;
Tae Seen KANG
;
Hyun Kyung CHO
;
Yong Seop HAN
;
Ji Hye KIM
Author Information
1. Department of Ophthalmology, Gyeongsang National University Changwon Hospital, Changwon, Korea
- Publication Type:Original Article
- From:Journal of the Korean Ophthalmological Society
2023;64(8):734-742
- CountryRepublic of Korea
- Language:Korean
-
Abstract:
Purpose:This study introduces a new machine learning-based auto-merge program (HydraVersion) that automatically combines multiple ocular photographs into single nine-directional ocular photographs. We compared the accuracy and time required to generate ocular photographs between HydraVersion and PowerPoint.
Methods:This was a retrospective study of 2,524 sets of 250 nine-directional ocular photographs (134 patients) between March 2016 and June 2022. The test dataset comprised 74 sets of 728 photographs (38 patients). We measured the time taken to generate nine-directional ocular photographs using HydraVersion and PowerPoint, and compared their accuracy.
Results:HydraVersion correctly combined 71 (95.95%) of the 74 sets of nine-directional ocular photographs. The average working time for HydraVersion and PowerPoint was 2.40 ± 0.43 and 255.9 ± 26.7 seconds, respectively; HydraVersion was significantly faster than PowerPoint (p < 0.001).
Conclusions:Strabismus and neuro-ophthalmology centers are often unable to combine and store photographs, except those of clinically significant cases, because of a lack of time and manpower. This study demonstrated that HydraVersion may facilitate treatment and research because it can quickly and conveniently generate nine-directional ocular photographs.
