Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2025
Deep learning-empowered low-cost portable automated refraction system: A solution to the inadequate effective correction rate of refractive errors in resource-limited areas.
СтатьяРандомизированное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Yan Huang, Yi Zhen, Qilin Sun, Hong Chang, Yan Huang, Wei Tang
Аннотация
AIMS: To develop and validate a low-cost, portable automated refraction system (Tongren Digital Sight, TRDS) using deep learning and infrared eccentric photorefraction to improve refractive error correction in resource-constrained settings.
METHODS: This randomised controlled crossover trial enrolled 282 participants (18-60 years of age) at Beijing Tongren Hospital. TRDS utilised low-cost components (complementary metal-oxide semiconductor (CMOS) detector, infrared light emitting diodes) and an end-to-end deep learning model trained on 362,000 images to analyse fundus reflection signals. Primary outcomes included agreement between TRDS and subjective refraction (gold standard) via Pearson correlation, Bland-Altman plots and intraclass correlation coefficients (ICC) for spherical equivalent (M), horizontal (J0) and oblique (J45) cylinder parameters. Secondary outcomes included corrected visual acuity.
RESULTS: TRDS showed strong correlations with subjective refraction: M (r = 0.96, ICC = 0.96), J0 (r = 0.74, ICC = 0.71) and J45 (r = 0.76, ICC = 0.75). Mean differences were 0.07 D (M), -0.06 D (J0) and 0.00 D (J45), with 95% limits of agreement within clinical tolerance. Corrected visual acuity was non-inferior to that of subjective refraction (mean difference = 0.03 logMAR, p < 0.01). Subgroup analysis showed consistent performance across myopia and hyperopia, except for moderate agreement in high myopia (M: r = 0.62). Subjective and TRDS refraction showed corrected visual acuity of 0.01 (SD = 0.13) and -0.02 (SD = 0.08) logMAR, respectively, with a significant mean difference of 0.03 logMAR (95% LoA: 0.01-0.04, p < 0.01). Corrected visual acuity >0.18 logMAR occurred in 13 (4.6%) and 14 (5.0%) participants for subjective and TRDS refraction, respectively.
CONCLUSIONS: The deep learning-empowered TRDS system demonstrated high accuracy and portability. It offers a scalable solution to improve refractive error correction rates, aligning with WHO goals to enhance global eye health.
METHODS: This randomised controlled crossover trial enrolled 282 participants (18-60 years of age) at Beijing Tongren Hospital. TRDS utilised low-cost components (complementary metal-oxide semiconductor (CMOS) detector, infrared light emitting diodes) and an end-to-end deep learning model trained on 362,000 images to analyse fundus reflection signals. Primary outcomes included agreement between TRDS and subjective refraction (gold standard) via Pearson correlation, Bland-Altman plots and intraclass correlation coefficients (ICC) for spherical equivalent (M), horizontal (J0) and oblique (J45) cylinder parameters. Secondary outcomes included corrected visual acuity.
RESULTS: TRDS showed strong correlations with subjective refraction: M (r = 0.96, ICC = 0.96), J0 (r = 0.74, ICC = 0.71) and J45 (r = 0.76, ICC = 0.75). Mean differences were 0.07 D (M), -0.06 D (J0) and 0.00 D (J45), with 95% limits of agreement within clinical tolerance. Corrected visual acuity was non-inferior to that of subjective refraction (mean difference = 0.03 logMAR, p < 0.01). Subgroup analysis showed consistent performance across myopia and hyperopia, except for moderate agreement in high myopia (M: r = 0.62). Subjective and TRDS refraction showed corrected visual acuity of 0.01 (SD = 0.13) and -0.02 (SD = 0.08) logMAR, respectively, with a significant mean difference of 0.03 logMAR (95% LoA: 0.01-0.04, p < 0.01). Corrected visual acuity >0.18 logMAR occurred in 13 (4.6%) and 14 (5.0%) participants for subjective and TRDS refraction, respectively.
CONCLUSIONS: The deep learning-empowered TRDS system demonstrated high accuracy and portability. It offers a scalable solution to improve refractive error correction rates, aligning with WHO goals to enhance global eye health.