Artificial intelligence to detect referable diabetic retinopathy in a Chinese population in Hong Kong
Keywords:
Artificial intelligence, Diabetic retinopathy, East Asian people, Machine learning, Sensitivity and specificityAbstract
Objective: This study aimed to investigate the sensitivity and specificity of the Deep Fundus, an artificial intelligence program for diabetic retinopathy screening, in Chinese patients with diabetes.
Methods: Fundus photographs of Chinese patients with diabetes aged 50 to 90 years who attended Pamela Youde Nethersole Eastern Hospital, Hong Kong between December 2020 and June 2021 during the public diabetic retinopathy screening period were retrospectively retrieved in February 2022. Two ophthalmologists independently graded the fundus photographs for diabetic retinopathy (according to the recommendations in the 2017 International Council of Ophthalmology Guidelines for Diabetic Eye Care) and any concomitant eye pathology such as cataracts or macular diseases. On 27 February 2022, the photographs were uploaded to the Deep Fundus website. Fundus images were classified into referable or non-referable diabetic retinopathy in accordance with the abovementioned Guidelines. Sensitivity and specificity of the Deep Fundus were determined.
Results: Of 202 eyes screened, 96 eyes were included in the analysis. There was no discordance between the two ophthalmologists in the diabetic retinopathy gradings. 57 (59.4%) eyes were graded to have positive diabetic retinopathy findings. Using gradings of the two ophthalmologists as the gold standard, sensitivity and specificity of the Deep Fundus were 62.3% and 90.7%, respectively. The positive and negative predictive values were 89.2% and 66.1%, respectively.
Conclusions: The sensitivity and specificity of the Deep Fundus for diabetic retinopathy screening in a Chinese population were 62.3% and 90.7%, respectively. Further real-world validation studies are needed to assess the artificial intelligence model, particularly among Chinese patients with cataracts, myopia, diabetic macular edema, or proliferative diabetic retinopathy.
References
Marchiori C, Dykeman D, Girardi I, et al. Artificial intelligence decision support for medical triage. AMIA Annu Symp Proc 2021;2020:793-802.
Baker A, Perov Y, Middleton K, et al. A comparison of artificial intelligence and human doctors for the purpose of triage and diagnosis. Front Artif Intell 2020;3:543405.
Lee D, Yoon SN. Application of artificial intelligence-based technologies in the healthcare industry: opportunities and challenges. Int J Environ Res Public Health 2021;18:271.
Bali J, Bali O. Artificial intelligence in ophthalmology and healthcare: an updated review of the techniques in use. Indian J Ophthalmol 2021;69:8-13.
Goldhagen BE, Al-Khersan H. Diving deep into deep learning: an update on artificial intelligence in retina. Curr Ophthalmol Rep 2020;8:121-8.
Zapata MA, Royo-Fibla D, Font O, et al. Artificial intelligence to identify retinal fundus images, quality validation, laterality evaluation, macular degeneration, and suspected glaucoma. Clin Ophthalmol 2020;14:419-29.
Kapoor R, Whigham BT, Al-Aswad LA. Artificial intelligence and optical coherence tomography imaging. Asia Pac J Ophthalmol (Phila) 2019;8:187-94.
Lim G, Bellemo V, Xie Y, Lee XQ, Yip MYT, Ting DSW. Different fundus imaging modalities and technical factors in AI screening for diabetic retinopathy: a review. Eye Vis (Lond) 2020;7:21.
Saeedi P, Petersohn I, Salpea P, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res Clin Pract 2019;157:107843.
Diabetes care and research in Europe: the Saint Vincent declaration. Diabet Med 1990;7:360.
Gangwani RA, Lian JX, McGhee SM, Wong D, Li KK. Diabetic retinopathy screening: global and local perspective. Hong Kong Med J 2016;22:486-95.
Gangwani R, Lai WW, Sum R, et al. The incidental findings of age-related macular degeneration during diabetic retinopathy screening. Graefes Arch Clin Exp Ophthalmol 2014;252:723-9.
Deep Fundus. Available from: https://www.deepfundus.com/#
Wong TY, Sun J, Kawasaki R, et al. Guidelines on diabetic eye care: the international council of ophthalmology recommendations for screening, follow-up, referral, and treatment based on resource settings. Ophthalmology 2018;125:1608-22.
Eyetelligence. Available from: https://eyetelligence.ai/us/.
Kiburg KV, Turner A, He M. Telemedicine and delivery of ophthalmic care in rural and remote communities: drawing from Australian experience. Clin Exp Ophthalmol 2022;50:793-800.
Keel S, Li Z, Scheetz J, et al. Development and validation of a deep-learning algorithm for the detection of neovascular age-related macular degeneration from colour fundus photographs. Clin Exp Ophthalmol 2019;47:1009-18.
Li Z, He Y, Keel S, Meng W, Chang RT, He M. Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs. Ophthalmology 2018;125:1199-206.
Li Z, Keel S, Liu C, et al. An automated grading system for detection of vision-threatening referable diabetic retinopathy on the basis of color fundus photographs. Diabetes Care 2018;41:2509-16.
Negida A, Fahim NK, Negida Y. Sample size calculation guide - Part 4: how to calculate the sample size for a diagnostic test accuracy study based on sensitivity, specificity, and the area under the ROC curve. Adv J Emerg Med 2019;3:e33.
Buderer NM. Statistical methodology: I. Incorporating the prevalence of disease into the sample size calculation for sensitivity and specificity. Acad Emerg Med 1996;3:895-900.
Ohno-Matsui K, Kawasaki R, Jonas JB, et al. International photographic classification and grading system for myopic maculopathy. Am J Ophthalmol 2015;159:877-83.e7.
Pék A, Szabó D, Sándor GL, et al. Relationship between diabetes mellitus and cataract in Hungary. Int J Ophthalmol 2020;13:788-93.
Becker C, Schneider C, Aballéa S, et al. Cataract in patients with diabetes mellitus-incidence rates in the UK and risk factors. Eye (Lond) 2018;32:1028-35.
Lee AY, Yanagihara RT, Lee CS, et al. Multicenter, head-to-head, real-world validation study of seven automated artificial intelligence diabetic retinopathy screening systems. Diabetes Care 2021;44:1168-75.
van der Heijden AA, Abramoff MD, Verbraak F, van Hecke MV, Liem A, Nijpels G. Validation of automated screening for referable diabetic retinopathy with the IDx-DR device in the Hoorn Diabetes Care System. Acta Ophthalmol 2018;96:63-8.
Bhaskaranand M, Ramachandra C, Bhat S, et al. The value of automated diabetic retinopathy screening with the EyeArt system: a study of more than 100,000 consecutive encounters from people with diabetes. Diabetes Technol Ther 2019;21:635-43.
Ipp E, Liljenquist D, Bode B, et al. Pivotal evaluation of an artificial intelligence system for autonomous detection of referrable and vision-threatening diabetic retinopathy. JAMA Netw Open 2021;4:e2134254.
Li S, Zhao R, Zou H. Artificial intelligence for diabetic retinopathy. Chin Med J (Engl) 2021;135:253-60.
Tufail A, Kapetanakis VV, Salas-Vega S, et al. An observational study to assess if automated diabetic retinopathy image assessment software can replace one or more steps of manual imaging grading and to determine their cost-effectiveness. Health Technol Assess 2016;20:1-72.
Gu C, Wang Y, Jiang Y, et al. Application of artificial intelligence system for screening multiple fundus diseases in Chinese primary healthcare settings: a real-world, multicentre and cross-sectional study of 4795 cases. Br J Ophthalmol 2023:322940.
Cheung CY, Tang F, Ting DSW, Tan GSW, Wong TY. Artificial intelligence in diabetic eye disease screening. Asia Pac J Ophthalmol (Phila) 2019;8:158-64.
Mak CY, Yam JC, Chen LJ, Lee SM, Young AL. Epidemiology of myopia and prevention of myopia progression in children in East Asia: a review. Hong Kong Med J 2018;24:602-9.
Van Newkirk MR. The Hong Kong vision study: a pilot assessment of visual impairment in adults. Trans Am Ophthalmol Soc 1997;95:715-49.
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