Artificial Intelligence for the Detection of Diabetic Retinopathy

Abstract

Screening and timely treatment can avoid the majority of severe vision loss and blindness from diabetic retinopathy. Artificial intelligence (AI) algorithms that detect DR from retinal photographs without human assessment might reduce the challenges of systematic screening. The German National Care Guideline recommends that individuals with diabetes receive annual or biennial eye examinations to detect treatable DR. Efficient and comprehensive screening of the growing diabetic population requires more and more resources. Artificial intelligence (AI) algorithms that detect DR from retinal photographs without human assessment might help in coping with the immense screening burden. Many of these AI algorithms have achieved good sensitivity and specificity for detecting treatable DR, as compared to human graders; however, many important challenges remain, such as acceptance, cost-effectiveness, liability issues, IT security, and reimbursement. AI-supported DR screening has so far only been used to a limited extent, even in countries with a developed digital infrastructure. These questions must be addressed before AI-based DR screening can be implemented on a large scale into clinical practice. This overview presents key concepts in development and currently approved AI applications for DR screening.

Fazitbox

• Bei richtiger Anwendung kann ein KI-basiertes Screening-Tool dazu beitragen, unnötige medizinische Untersuchungen zu reduzieren und die Verfügbarkeit medizinischer Versorgung auf die Patienten zu konzentrieren, die sie am dringendsten benötigen.

• Die ordnungsgemäße Implementierung von KI-basiertem DR-Screening stellt vielerorts noch eine technische und logistische Herausforderung dar, hat jedoch enormes Potenzial, die Sehfähigkeit vieler Menschen zu erhalten. Die mangelnde Transparenz hinsichtlich der Entscheidungsfindung von KI-Modellen stellt jedoch eine essenzielle Limitation für deren praktische Nutzung dar.

• KI-Modelle können vielseitig eingesetzt werden und bieten insbesondere in Regionen mit einem Mangel an Fachärzten wertvolle Unterstützung zur Etablierung möglicher Triage-Systeme, um die Patientenversorgung zu strukturieren. Die Augenheilkunde als Disziplin mit einem starken Fokus auf optische Verfahren und stetiger Weiterentwicklung nicht invasiver Bildgebungstechniken bietet zahlreiche potenzielle Anwendungsbereiche für automatische oder semiautomatische KI-Modelle.

Conclusion Box

• When used properly, an AI-based screening tool can help reduce unneeded medical examinations and focus health care on the patients who need it most urgently.

• Implementing AI-based DR screening properly is still a technical and logistical challenge in many places, but it has huge potential to sustain the eyesight of many people. However, the lack of transparency in AI modelsʼ decision-making process represents a significant limitation for their practical use.

• AI models can be used in a variety of ways and offer valuable support for establishing possible triage systems to structure patient care, especially in regions with a shortage of specialists. Ophthalmology, a field with a strong focus on optic procedures and continuous development of non-invasive imaging techniques, offers numerous potential applications for automated or semi-automated AI modelling.

Keywords

history of medicine - retina - information technology - AI - diabetic retinopathy

Publication History

Received: 06 October 2024

Accepted: 12 February 2025

Article published online:
02 June 2025

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