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Transforming Non-Digital, Clinical Workflows to Detect and Track Vision-Threatening Diabetic Retinopathy via a Digital Platform Integrating Artificial Intelligence: Implementation Research.

作者信息

Chotcomwongse Peranut, Ruamviboonsuk Paisan, Karavapitayakul Chaiwat, Thongthong Koblarp, Amornpetchsathaporn Anyarak, Chainakul Methaphon, Triprachanath Malee, Lerdpanyawattananukul Eckachai, Arjkongharn Niracha, Ruamviboonsuk Varis, Vongsa Nattaporn, Pakaymaskul Pawin, Waiwaree Turean, Ruampunpong Hathaiphan, Tiwari Richa, Tangcharoensathien Viroj

机构信息

Department of Ophthalmology, College of Medicine, Rajavithi Hospital, Rangsit University, Bangkok, Thailand.

Department of Medical Services (DMS), Ministry of Public Health, Nonthaburi, Thailand.

出版信息

Ophthalmol Ther. 2025 Feb;14(2):447-460. doi: 10.1007/s40123-024-01086-8. Epub 2025 Jan 10.


DOI:10.1007/s40123-024-01086-8
PMID:39792334
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11754548/
Abstract

INTRODUCTION: Screening diabetic retinopathy (DR) for timely management can reduce global blindness. Many existing DR screening programs worldwide are non-digital, standalone, and deployed with grading retinal photographs by trained personnel. To integrate the screening programs, with or without artificial intelligence (AI), into hospital information systems to improve their effectiveness, the non-digital workflow must be transformed into digital. We developed a cloud-based digital platform and implemented it in an existing DR screening program. METHODS: We conducted the following processes in the platform for prospective DR screening at a community hospital: capturing patients' retinal photographs, uploading them for grading by AI or trained personnel on alternate weeks for 32 weeks, and referring vision-threatening DR to a referral center. At this center, the platform was applied for the assessment of potential missed referrals via remote over-reading by a retinal specialist and tracking referrals. Implementational outcomes, such as detecting positive cases, agreement between AI and over-reading, and referral adherence were assessed. RESULTS: Of 645 patients screened by AI, 201 (31.2%) were referrals, 129 (64.2%) of which were true positives agreeable by over-reading; 115 of these true positives (89.1%) had referral adherence. False negatives judged by over-reading were 1.1% (5/444). Of 730 patients in manual screening, 175 (24.0%) were potential referrals, 11 (6.3%) of which were referred at the point-of-screening; eight of these (72.7%) adhered to referral. The remaining 164 cases were appointed for later examination by a visiting general ophthalmologist; 11 of these 116 examined (9.5%) were referred for non-DR-related eye conditions with 81.8% (9/11) referral adherence. No system failure or interruption was found. CONCLUSIONS: The digital platform can be practically integrated into the existing non-digital DR screening programs to implement AI and monitor previously unknown but important indicators, such as referral adherence, to improve the effectiveness of the programs. TRIAL REGISTRATION: ClinicalTrials.gov. (registration number: NCT05166122).

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6fc8/11754548/cfd5c1bba2be/40123_2024_1086_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6fc8/11754548/cfd5c1bba2be/40123_2024_1086_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6fc8/11754548/cfd5c1bba2be/40123_2024_1086_Fig1_HTML.jpg

相似文献

[1]
Transforming Non-Digital, Clinical Workflows to Detect and Track Vision-Threatening Diabetic Retinopathy via a Digital Platform Integrating Artificial Intelligence: Implementation Research.

Ophthalmol Ther. 2025-2

[2]
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[3]
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[4]
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[5]
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[6]
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[7]
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[8]
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[9]
Pivotal Evaluation of an Artificial Intelligence System for Autonomous Detection of Referrable and Vision-Threatening Diabetic Retinopathy.

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[10]
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Ophthalmol Sci. 2023-5-12

本文引用的文献

[1]
Diagnostic Accuracy of Automated Diabetic Retinopathy Image Assessment Softwares: IDx-DR and Medios Artificial Intelligence.

Ophthalmic Res. 2023

[2]
Performance of the AIDRScreening system in detecting diabetic retinopathy in the fundus photographs of Chinese patients: a prospective, multicenter, clinical study.

Ann Transl Med. 2022-10

[3]
Risk of acute angle-closure and changes in intraocular pressure after pupillary dilation in patients with diabetes.

Eye (Lond). 2023-6

[4]
Artificial intelligence deployment in diabetic retinopathy: the last step of the translation continuum.

Lancet Digit Health. 2022-4

[5]
Real-time diabetic retinopathy screening by deep learning in a multisite national screening programme: a prospective interventional cohort study.

Lancet Digit Health. 2022-4

[6]
Pivotal Evaluation of an Artificial Intelligence System for Autonomous Detection of Referrable and Vision-Threatening Diabetic Retinopathy.

JAMA Netw Open. 2021-11-1

[7]
Capacity building in screening and treatment of diabetic retinopathy in Asia-Pacific region.

Indian J Ophthalmol. 2021-11

[8]
Real-world artificial intelligence-based opportunistic screening for diabetic retinopathy in endocrinology and indigenous healthcare settings in Australia.

Sci Rep. 2021-8-4

[9]
Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study.

Lancet Digit Health. 2019-5

[10]
Causes of blindness and vision impairment in 2020 and trends over 30 years, and prevalence of avoidable blindness in relation to VISION 2020: the Right to Sight: an analysis for the Global Burden of Disease Study.

Lancet Glob Health. 2021-2

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