Clinical Science
Establishment and application of diabetic retinopathy intelligent assisted diagnostic technology evaluation system based on fundus photography
Bo Zheng, Weihua Yang, Maonian Wu, Shaojun Zhu, Ming Weng, Xian Zhang, Minjun Zhang
Published 2019-08-10
Cite as Chin J Exp Ophthalmol, 2019, 37(8): 674-679. DOI: 10.3760/cma.j.issn.2095-0160.2019.08.017
Abstract
ObjectiveTo propose a new evaluation system and evaluate the application value of diabetic retinopathy (DR) intelligence assisted diagnostic technology based on fundus photography.
MethodsBy using the diagnostic test method, an evaluation system of DR intelligent diagnostic technology based on fundus photography was established.The fundus photographs of 331 diabetic patients (662 eyes) with DR screening were collected in the First Affiliated Hospital of Huzhou University from January 2017 to October 2018.The results of experts' diagnosis and intelligence assisted diagnosis were compared and evaluated.The evaluation system includes primary evaluation, intermediate evaluation and advanced evaluation.The primary evaluation is the consistency of non-DR (NDR) in all diabetic patients receiving DR-assisted diagnostic techniques; the intermediate evaluation is the diagnosis consistency of DR lesion degree in patients diagnosed with DR (grade 1-4); the advanced evaluation is the diagnosis consistency of DR classification (grade 0-4) in all diabetic patients receiving DR-assisted diagnostic techniques.The intermediate evaluation includes two evaluation methods.The main evaluation indicators include sensitivity, specificity and Kappa value.
ResultsBased on experts' diagnosis, NDR accounted for 22.7%; mild non-proliferative DR(NPDR), moderate NPDR, and severe NPDR accounted for 19.9%, 18.7% and 25.7%, respectively; proliferative DR(PDR) accounted for 13.0%.Based on intelligence diagnostic system, NDR accounted for 25.8%; mild NPDR, moderate NPDR and severe NPDR accounted for 19.7%, 19.3% and 22.8%, respectively; proliferative DR(PDR) accounted for 12.4%.Based on evaluation system in the paper, the sensitivity, specificity and Kappa value in primary evaluation were 91.4%, 84.7% and 0.72; the sensitivity, specificity and Kappa value in intermediate evaluation method one were 88.4%, 91.1% and 0.79; the sensitivity, specificity and Kappa value in intermediate evaluation method two were 80.5%, 93.3% and 0.75; the Kappa value in advanced evaluation was 0.62.
ConclusionsThe evaluation system can be applied to the evaluation of DR intelligent diagnostic technology, and the evaluation result can be used as the basis for the selection of DR intelligent diagnosis application scene.
Key words:
Diabetic retinopathy; Artificial intelligence; Deep learning; Diagnostic imaging; Neural networks; Evaluation system; Intelligent diagnostic technology
Contributor Information
Bo Zheng
Key Laboratory of Medical Artificial Intelligence of Huzhou University, The Information Engineering College of Huzhou University, Huzhou 313000, China
Weihua Yang
Department of Ophthalmology, the First Affiliated Hospital of Huzhou University, Huzhou 313000, China
Maonian Wu
Key Laboratory of Medical Artificial Intelligence of Huzhou University, The Information Engineering College of Huzhou University, Huzhou 313000, China
Shaojun Zhu
Key Laboratory of Medical Artificial Intelligence of Huzhou University, The Information Engineering College of Huzhou University, Huzhou 313000, China
Ming Weng
Department of Ophthalmology, Wuxi Third People's Hospital, Wuxi 214041, China
Xian Zhang
Department of Ophthalmology, Ningbo Medical Center Lihuili Eastern Hospital, Ningbo 315000, China
Minjun Zhang
Department of Ophthalmology, Huzhou Aier Eye Hospital, Huzhou 313000, China
