J Korean Ophthalmol Soc.  2023 Aug;64(8):734-742. 10.3341/jkos.2023.64.8.734.

Machine Learning-based Auto-merge Program for Nine-directional Ocular Photography

• Park SH
• Lee WH
• Kang TS
• Cho HK
• Han YS
• Kim JH

• KMID: 2545001
• DOI: http://doi.org/10.3341/jkos.2023.64.8.734

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.

Keyword

Eye movement, Eye tracking technology, Machine learning, Nine-directional ocular photography, Strabismus