인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
IOL power selection is a key determinant of refractive outcomes after cataract surgery. Numerous formulas exist to aid in this process; some are derived from geometric-optical principles (e.g., SRK/T, Barrett) while others are based on data-driven and machine learning approaches (e.g., Nallasamy, Pearl-DGS). Given differences in ocular biometry and environmental stimuli, population-specific factors may impact the generalizability of certain formulas. This study compares clinical and biometric characteristics and evaluates the prediction accuracy of seven IOL power formulas, including machine learning–based approaches, in two distinct cataract surgery populations from South India and the Midwestern United States. In this retrospective cross-sectional comparative study, data were collected from two tertiary care eye centers: University of Michigan’s Kellogg Eye Center (Ann Arbor, MI, USA) and Aravind Eye Hospital (Chennai, Tamil Nadu, India). The dataset included demographics, biometry power of the surgically implanted intraocular lens (IOL), and 1-month postoperative refraction. Seven IOL formulas were applied to predict postoperative refraction, and performance was assessed by comparing mean absolute errors both before and after population-specific A-constant optimization. A total of 985 eyes from Aravind (mean age 60.5 ± 9.5 years) and 1003 from UMich (mean age 70.7 ± 9.5) were analyzed. Aravind patients had significantly lower age, axial length, lens thickness, and central corneal thickness, while UMich patients had lower K measurements, IOL power, and postoperative refraction. Overall, formulas performed better in Aravind for the SN60WF lens. Before A-constant optimization on the Aravind dataset, one formula (Nallasamy) achieved mean absolute error under 0.25 diopters compared to four formulas (Nallasamy, Pearl-DGS, SRK/T, Barrett) afterwards. Substantial clinical and biometric differences exist between South Indian and Midwestern US cataract populations. Machine learning-based IOL refraction prediction formulas performed the best on the South Indian dataset both before and after population-specific parameter optimization. Understanding population level differences and creating methods to integrate these factors into IOL formulas may help improve refractive outcomes in cataract surgery.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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