A multi center prospective evaluation of THEIA to detect diabetic retinopathy (DR) and diabetic macular edema (DME) in the New Zealand screening program
Purpose: to assess the efficacy of THEIATM, an artificial intelligence for screening diabetic retinopathy in a multi center prospective study. To validate the potential application of THEIATM as clinical decision making assistant in a national screening program. Methods: 902 patients were recruited from either an urban large eye hospital, or a semi rural optometrist led screening provider, as they were attending their appointment as part of New Zealand Diabetic Screening programme. These clinics used a variety of retinal cameras and a range of operators. The de identified images were then graded independently by three senior retinal specialists, and final results were aggregated using New Zealand grading scheme, which is then converted to referable\non referable and Healthy\mild\more than mild\vision threatening categories. Results: compared to "ground truth" THEIATM achieved 100% sensitivity and [95.35%97.44% specificity, and negative predictive value of 100% THEIATM also did not miss any patients with "more than mild" or "vision threatening" disease. The level of agreement between the clinicians and the aggregated results was (k value: 0.9881, 0.9557, and 0.9175) and the level of agreement between THEIATM and the aggregated labels was (k value: 0.9515) Conclusion: Our multi centre prospective trial showed that THEIATM does not miss referable disease when screening for diabetic retinopathy and maculopathy. It also has a very high level of granularity in reporting the disease level. Since THEIATM is being tested on a variety of cameras, operating in a range of clinics (rural\urban, ophthalmologist led\optometrist led) we believe that it will be a suitable addition to a public diabetic screening program.