Hao Zhaohu, Xu Rong, Huang Xiao, Ren Xinjun, Li Huanming, Shao Hailin
Department of Metabolic Disease Management Center, Tianjin 4th Central Hospital, Tianjin, China.
NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, China.
Ther Adv Chronic Dis. 2022 May 19;13:20406223221097335. doi: 10.1177/20406223221097335. eCollection 2022.
To observe the consistency of a preliminary report of artificial intelligence (AI) in the clinical practice of fundus screening for diabetic retinopathy (DR) using non-mydriatic fundus photography.
Patients who underwent DR screening in the Metabolic Disease Management Center (MMC) of our hospital were selected as research participants. The degree of coincidence of the AI preliminary report and the ophthalmic diagnosis was compared and analyzed, and the kappa value was calculated. Fundus fluorescein angiography (FFA) was performed in patients referred to the out-of-hospital ophthalmology department, and the consistency between fluorescein angiography and AI diagnosis was evaluated.
In total, 6146 patients (12,263 eyes) completed the non-mydriasis fundus examination. The positive DR screening rate was 24.3%. When considering moderate nonproliferative retinopathy as the cut-off point, the kappa coefficient was 0.75 ( < 0.001), the sensitivity was 0.973, and the precision was 0.642, which was shown in the precision-recall curve. Fifty-nine patients referred to receive FFA were compared with non-mydriatic AI diagnoses. The kappa coefficient was 0.53, and the coincidence rate was 66.9%.
Non-mydriasis fundus examination combined with AI has a medium-high consistency with ophthalmologists in DR diagnosis, conducive to early DR screening. Combining diagnosis and treatment modes with the Internet can promote the development of telemedicine, alleviate the shortage of ophthalmology resources, and promote the process of blindness prevention and treatment projects.
观察人工智能（AI）在免散瞳眼底照相筛查糖尿病视网膜病变（DR）临床实践中的初步报告的一致性。
选取在我院代谢病管理中心（MMC）接受DR筛查的患者作为研究对象。比较分析AI初步报告与眼科诊断的符合程度，并计算kappa值。对转诊至院外眼科的患者进行眼底荧光血管造影（FFA），评估荧光血管造影与AI诊断的一致性。
共有6146例患者（12263只眼）完成了免散瞳眼底检查。DR筛查阳性率为24.3%。以中度非增殖性视网膜病变为界值时，kappa系数为0.75（<0.001），灵敏度为0.973，特异度为0.642，在精确召回率曲线中显示。将59例接受FFA检查的患者与免散瞳AI诊断结果进行比较。kappa系数为0.53，符合率为66.9%。
免散瞳眼底检查联合AI在DR诊断中与眼科医生具有中高度一致性，有利于DR早期筛查。将诊断和治疗模式与互联网相结合可促进远程医疗发展，缓解眼科资源短缺，推动防盲治盲项目进程。
