Clinical evaluation of AI-assisted screening for diabetic retinopathy in rural areas of midwest China
<h4>Background</h4> Although numerous studies have described the application of artificial intelligence (AI) in diabetic retinopathy (DR) screening among diabetic populations, studies among populations in rural areas are rare. The purpose of this study was to evaluate the application val...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9560484/?tool=EBI |
| _version_ | 1798030075307753472 |
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| author | Shaofeng Hao Changyan Liu Na Li Yanrong Wu Dongdong Li Qingyue Gao Ziyou Yuan Guanyan Li Huilin Li Jianzhou Yang Shengfu Fan |
| author_facet | Shaofeng Hao Changyan Liu Na Li Yanrong Wu Dongdong Li Qingyue Gao Ziyou Yuan Guanyan Li Huilin Li Jianzhou Yang Shengfu Fan |
| author_sort | Shaofeng Hao |
| collection | DOAJ |
| description | <h4>Background</h4> Although numerous studies have described the application of artificial intelligence (AI) in diabetic retinopathy (DR) screening among diabetic populations, studies among populations in rural areas are rare. The purpose of this study was to evaluate the application value of an AI-based diagnostic system for DR screening in rural areas of midwest China. <h4>Methods</h4> In this diagnostic accuracy study, diabetes mellitus (DM) patients in the National Basic Public Health Information Systems of Licheng County and Lucheng County of Changzhi city from July to December 2020 were selected as the target population. A total of 7824 eyes of 3933 DM patients were enrolled in this screening; the patients included 1395 males and 2401 females, with an average age of 19–87 years (63±8.735 years). All fundus photographs were collected by a professional ophthalmologist under natural pupil conditions in a darkroom using the Zhiyuan Huitu fundus image AI analysis software EyeWisdom. The AI-based diagnostic system and ophthalmologists were tasked with diagnosing the photos independently, and the consistency rate, sensitivity and specificity of the two methods in diagnosing DR were calculated and compared. <h4>Results</h4> The prevalence rates of DR according to the ophthalmologist and AI diagnoses were 22.7% and 22.5%, respectively; the consistency rate was 81.6%. The sensitivity and specificity of the AI system relative to the ophthalmologists’ grades were 81.2% (95% confidence interval [CI]: 80.3% 82.1%) and 94.3% (95% CI: 93.7% 94.8%), respectively. There was no significant difference in diagnostic outcomes between the methods (χ2 = 0.329, P = 0.566, P>0.05), and the AI-based diagnostic system had high consistency with the ophthalmologists’ diagnostic results (κ = 0.752). <h4>Conclusion</h4> Our research demonstrated that DR patients in rural area hospitals can be screened feasibly. Compared with that of the ophthalmologists, however, the accuracy of the AI system must be improved. The results of this study might lend support to the large-scale application of AI in DR screening among different populations. |
| first_indexed | 2024-04-11T19:34:25Z |
| format | Article |
| id | doaj.art-b0b701bb33c340e88511fe17bb7a9a49 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-11T19:34:25Z |
| publishDate | 2022-01-01 |
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| series | PLoS ONE |
| spelling | doaj.art-b0b701bb33c340e88511fe17bb7a9a492022-12-22T04:06:54ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-011710Clinical evaluation of AI-assisted screening for diabetic retinopathy in rural areas of midwest ChinaShaofeng HaoChangyan LiuNa LiYanrong WuDongdong LiQingyue GaoZiyou YuanGuanyan LiHuilin LiJianzhou YangShengfu Fan<h4>Background</h4> Although numerous studies have described the application of artificial intelligence (AI) in diabetic retinopathy (DR) screening among diabetic populations, studies among populations in rural areas are rare. The purpose of this study was to evaluate the application value of an AI-based diagnostic system for DR screening in rural areas of midwest China. <h4>Methods</h4> In this diagnostic accuracy study, diabetes mellitus (DM) patients in the National Basic Public Health Information Systems of Licheng County and Lucheng County of Changzhi city from July to December 2020 were selected as the target population. A total of 7824 eyes of 3933 DM patients were enrolled in this screening; the patients included 1395 males and 2401 females, with an average age of 19–87 years (63±8.735 years). All fundus photographs were collected by a professional ophthalmologist under natural pupil conditions in a darkroom using the Zhiyuan Huitu fundus image AI analysis software EyeWisdom. The AI-based diagnostic system and ophthalmologists were tasked with diagnosing the photos independently, and the consistency rate, sensitivity and specificity of the two methods in diagnosing DR were calculated and compared. <h4>Results</h4> The prevalence rates of DR according to the ophthalmologist and AI diagnoses were 22.7% and 22.5%, respectively; the consistency rate was 81.6%. The sensitivity and specificity of the AI system relative to the ophthalmologists’ grades were 81.2% (95% confidence interval [CI]: 80.3% 82.1%) and 94.3% (95% CI: 93.7% 94.8%), respectively. There was no significant difference in diagnostic outcomes between the methods (χ2 = 0.329, P = 0.566, P>0.05), and the AI-based diagnostic system had high consistency with the ophthalmologists’ diagnostic results (κ = 0.752). <h4>Conclusion</h4> Our research demonstrated that DR patients in rural area hospitals can be screened feasibly. Compared with that of the ophthalmologists, however, the accuracy of the AI system must be improved. The results of this study might lend support to the large-scale application of AI in DR screening among different populations.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9560484/?tool=EBI |
| spellingShingle | Shaofeng Hao Changyan Liu Na Li Yanrong Wu Dongdong Li Qingyue Gao Ziyou Yuan Guanyan Li Huilin Li Jianzhou Yang Shengfu Fan Clinical evaluation of AI-assisted screening for diabetic retinopathy in rural areas of midwest China PLoS ONE |
| title | Clinical evaluation of AI-assisted screening for diabetic retinopathy in rural areas of midwest China |
| title_full | Clinical evaluation of AI-assisted screening for diabetic retinopathy in rural areas of midwest China |
| title_fullStr | Clinical evaluation of AI-assisted screening for diabetic retinopathy in rural areas of midwest China |
| title_full_unstemmed | Clinical evaluation of AI-assisted screening for diabetic retinopathy in rural areas of midwest China |
| title_short | Clinical evaluation of AI-assisted screening for diabetic retinopathy in rural areas of midwest China |
| title_sort | clinical evaluation of ai assisted screening for diabetic retinopathy in rural areas of midwest china |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9560484/?tool=EBI |
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