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

Autor: Christoph Palme, Franziska Sofia Hafner, Lena Hafner, Theodor Peter Peifer, Anna Lena Huber, Bernhard Steger
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Ophthalmology and Therapy, Vol 13, Iss 6, Pp 1683-1692 (2024)
Druh dokumentu: article
ISSN: 2193-8245
2193-6528
DOI: 10.1007/s40123-024-00945-8
Popis: 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
Databáze: Directory of Open Access Journals
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje