Artificial Intelligence (AI) Reveals Ethnic Disparities in Cataract Detection and Treatment

Abstract

INTRODUCTION: The aim of this work is to identify patients at risk of limited access to healthcare through artificial intelligence using a name-ethnicity classifier (NEC) analyzing the clinical stage of cataract at diagnosis and preoperative visual acuity. METHODS: This retrospective, cross-sectional study includes patients seen in the cataract clinic of a tertiary care hospital between September 2017 and February 2020 with subsequent cataract surgery in at least one eye. We analyzed 4971 patients and 8542 eyes undergoing surgery. RESULTS: The NEC identified 360 patients with names classified as 'non-German' compared to 4611 classified as 'German'. Advanced cataract (7 vs. 5%; p = 0.025) was significantly associated with group 'non-German'. Mean best-corrected visual acuity in group 'non-German' was 0.464 ± 0.406 (LogMAR), and in group 'German' was 0.420 ± 0.334 (p = 0.009). This difference remained significant after exclusion of patients with non-lenticular ocular comorbidities. Surgical time and intraoperative complications did not differ between the groups. Retrobulbar or general anesthesia was chosen significantly more frequently over topical anesthesia in group 'non-German' compared to group 'German' (24 vs. 18% respectively; p < 0.001). CONCLUSIONS: This study shows that artificial intelligence is able to uncover health disparities between people with German compared to non-German names using NECs. Patients with non-German names, possibly facing various social barriers to healthcare access such as language barriers, have more advanced cataracts and worse visual acuity upon presentation. Artificial intelligence may prove useful for healthcare providers to discover and counteract such inequalities and establish tailored preventive measures to decrease morbidity in vulnerable population subgroups.

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