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KMID : 0360220230640080734
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Journal of the Korean Ophthalmological Society
2023 Volume.64 No. 8 p.734 ~ p.742
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Machine Learning-based Auto-merge Program for Nine-directional Ocular Photography
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Park Shin-Hyeong
Lee Woo-Hyuk
Kang Tae-Seen
Cho Hyun-Kyung
Han Yong-Seop
Kim Ji-Hye
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Abstract
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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.
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KEYWORD
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Eye movement, Eye tracking technology, Machine learning' Nine-directional ocular photography, Strabismus
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