핵심 개념
RLPeri optimizes visual perimetry testing using reinforcement learning and convolutional feature extraction to reduce examination time while maintaining accuracy.
초록
Visual perimetry is crucial for assessing visual abilities and detecting eye conditions.
RLPeri uses reinforcement learning to optimize testing sequence and initial stimulus values.
Reward shaping techniques enhance testing performance.
Experimental results show a 10-20% reduction in examination time with maintained accuracy.
RLPeri outperforms existing methods in terms of speed and accuracy.
통계
RLPeri는 시각 페리미터 검사를 최적화하기 위해 강화 학습과 합성곱 특징 추출을 사용합니다.
실험 결과, RLPeri는 시험 시간을 10-20% 줄이고 정확도를 유지하는 데 성공했습니다.