Negiloni Kalpa, Baskaran Prabu, Rao Divya Parthasarathy, Maitray Aditya, Savoy Florian M, Suresh Shruthi, Mahalingam Maanasi, Vighnesh M J, Rajendran Anand
Remidio Innovative Solutions Pvt Ltd, Bengaluru, India.
Aravind Eye Hospital, Chennai, India.
Eye (Lond). 2025 Jul 11. doi: 10.1038/s41433-025-03902-2.
This study evaluated a novel offline, AI-driven age-related macular degeneration (AMD) screening algorithm against fundus image-only grading and the standard of care (combined Spectral Domain-Optical Coherence Tomography (SD-OCT) and fundus image grading).
Conducted prospectively at a South Asian tertiary eye hospital, this study utilized a validated smartphone-based non-mydriatic fundus camera to capture macula-centred images. The Medios AI's ability to detect referable AMD was compared to a reference standard image grading, using fundus images from the Zeiss Clarus 700 table-top camera and SD-OCT line scan across fovea. Three retina specialists provided blinded AMD diagnoses based on: (1) Zeiss Clarus 700 fundus images alone, and (2) combined SD-OCT and fundus images (standard of care). Referable AMD was defined as intermediate or advanced AMD.
Among 984 eyes from 492 patients (mean age 61.8 ± 9.9 years), 52% had referable AMD. Inter-grader agreement was strong, with Cohen's Kappa scores of 0.81-0.84. The Medios AI's sensitivity and specificity for detecting referable AMD against fundus-only grading (n = 492) were 88.48% (95% CI: 84.04-92.03%) and 87% (95% CI: 81.86-91.11%), respectively. Against combined grading (n = 489), AI sensitivity was 90.62% (95% CI: 86.37-93.90%), and specificity was 85.41% (95% CI: 80.21-89.68%). False negatives were primarily intermediate AMD (71%), while 59% of false positives were early AMD.
The novel, automated, offline AMD AI integrated on a smartphone fundus camera demonstrated robust performance in identifying referable forms of AMD, supporting its potential as an affordable and accessible screening solution.
本研究评估了一种新型的离线人工智能驱动的年龄相关性黄斑变性（AMD）筛查算法，将其与仅基于眼底图像的分级以及护理标准（联合光谱域光学相干断层扫描（SD - OCT）和眼底图像分级）进行比较。
本研究在一家南亚三级眼科医院前瞻性开展，利用经过验证的基于智能手机的免散瞳眼底相机拍摄以黄斑为中心的图像。将Medios人工智能检测可转诊性AMD的能力与参考标准图像分级进行比较，参考标准图像分级使用来自蔡司Clarus 700桌面相机的眼底图像和穿过中央凹的SD - OCT线扫描。三位视网膜专家基于以下内容提供盲法AMD诊断：（1）仅蔡司Clarus 700眼底图像，以及（2）联合SD - OCT和眼底图像（护理标准）。可转诊性AMD被定义为中度或重度AMD。
在492例患者的984只眼中（平均年龄61.8±9.9岁），52%患有可转诊性AMD。分级者间一致性较强，科恩kappa系数为0.81 - 0.84。Medios人工智能针对仅眼底图像分级（n = 492）检测可转诊性AMD的灵敏度和特异度分别为88.48%（95%置信区间：84.04 - 92.03%）和87%（95%置信区间：81.86 - 91.11%）。针对联合分级（n = 489），人工智能灵敏度为90.62%（95%置信区间：86.37 - 93.90%），特异度为85.41%（95%置信区间：80.21 - 89.68%）。假阴性主要为中度AMD（71%），而59%的假阳性为早期AMD。
集成在智能手机眼底相机上的新型、自动化、离线AMD人工智能在识别可转诊性AMD形式方面表现出强大性能，支持其作为一种经济实惠且可及的筛查解决方案的潜力。
