머신러닝을 이용한 아이트래커의 사시 스크리닝
Strabismus Screening Using Eyetracker Combined with Machine Learning
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목적: Tobii pro glasses 2 (TPG2)로 측정한 안구편위 데이터를 머신러닝을 이용하여 수평사시를 진단하는 스크리닝 프로그램의 유효성을 알아보았다. 대상과 방법: TPG2는 동공 중심을 찾아 눈의 이동을 측정할 수 있는 웨어러블 아이트래커이다. TPG2를 착용한 정상 성인에서 왼쪽 눈을 가린 후 오른쪽 눈에 프리즘 양과 방향을 달리해서 수평안구편위를 유도하였다. 대상자마다 2, 3, 4, 5, 6, 8, 10, 12, 15, 20, 25, 30, 35, 40 프리즘 기저부를 안쪽, 바깥쪽으로 안구편위를 유도하여 28종류 세트를 얻었다. 각 세트에서 안구편위 전후 각 20개의 X축 좌표값을 오버샘플링기법으로 랜덤 추출하여 총 61,600개 안구편위세트를 만들었다. 56,000개는 훈련세트로 활용하였고, 5,600개는 테스트세트로 민감도, 특이도, area under the curve (AUC)를 조사하였다. 결과: 11명의 정상 성인(남자 5명)이 참여하였고 평균 연령은 34.8 ± 7.37세였다. 8 PD 기준으로 분류하였을 때 8 PD 이하는 민감도 1.00, 특이도 0.95, AUC 0.97으로 측정되었다. 8, 20 PD 기준으로 세 구간으로 나누었을 때, 8 PD 이하의 민감도 0.90, 특이도 0.95, 8-20 PD의 민감도 0.60, 특이도 1.00, 20 PD 초과의 민감도 1.00, 특이도 0.88로 측정되었다. 결론: 프리즘으로 유도한 안구편위를 TPG2로 측정하여 안구편위구간에 따른 머신러닝 프로그램을 개발하였다. 향후 이를 이용하여 사시 스크리닝 검사에 유용하게 사용할 수 있을 것이다.
Abstract
Purpose: To assess the effectiveness of an automated screening program that diagnoses horizontal strabismus using machine learning based on ocular deviation data captured by the wearable eyetracker, Tobii pro glasses 2 (TPG2). Methods: The TPG2 which locates the pupil center to measure ocular movement was used. In normal adults wearing TPG2, horizontal ocular deviation was induced by covering the left eye and applying prisms of varying strengths (2, 3, 4, 5, 6, 8, 10, 12, 15, 20, 25, 30, 35, and 40 PD base-in and out) to the right eye. TPG2 automatically recorded ocular deviation before and after prism induction generating 28 types of ocular deviation sets. From each set, 20 X-axis values before and after ocular deviation were randomly extracted using an oversampling technique creating a total of 61,600 ocular deviation sets. For training, 56,000 sets were used and 5,600 were evaluated for sensitivity, specificity, and area under the curve (AUC). Results: Eleven normal adults (5 males) participated with a mean age of 34.8 ± 7.37 years. Based on an 8 PD threshold, deviations of 8 PD or less demonstrated a sensitivity of 1.0, a specificity of 0.95, and an AUC of 0.97. When categorized into three groups based on 8 PD and 20 PD thresholds, the results were: sensitivity of 0.90 and specificity of 0.95 for ≤ 8 PD; sensitivity of 0.60 and specificity of 1.00 for 8-20 PD; sensitivity of 1.00 and specificity of 0.88 for > 20 PD. Conclusions: The machine learning program developed using induced ocular deviations measured with prisms and TPG2 shows promise for use in future strabismus screening tests.
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