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Artificial intelligence for the diagnosis of retinopathy of prematurity: A systematic review of current algorithms
Ashwin Ramanathan1, Sam Ebenezer Athikarisamy2,3, Geoffrey C Lam4,5
1Department of Paediatrics, Perth Children's Hospital, Perth, Australia.
Insights
Artificial intelligence (AI) shows promise in diagnosing retinopathy of prematurity (ROP) using wide field retinal imaging. While AI is comparable to ophthalmologists, more evidence is needed before it can be used as a standalone diagnostic tool.
Area of Science:
- Ophthalmology
- Medical Imaging
- Artificial Intelligence
Background:
- Increasing survival rates of premature infants necessitate improved retinopathy of prematurity (ROP) screening.
- Wide field retinal imaging (WFDRI) and AI offer potential to enhance ROP diagnostic accuracy and reduce ophthalmologist workload.
Conclusions:
- AI is a rapidly advancing field for ROP diagnosis, with identified utilities in detecting plus disease, staging, and severity scoring.
- AI can serve as an adjunct to clinical assessment in ROP screening.
- Current evidence is insufficient to support AI as a sole diagnostic tool for ROP.
Background/Objectives:
With the increasing survival of premature infants, there is an increased demand to provide adequate retinopathy of prematurity (ROP) services. Wide field retinal imaging (WFDRI) and artificial intelligence (AI) have shown promise in the field of ROP and have the potential to improve the diagnostic performance and reduce the workload for screening ophthalmologists. The aim of this review is to systematically review and provide a summary of the diagnostic characteristics of existing deep learning algorithms.
Subject/Methods:
Two authors independently searched the literature, and studies using a deep learning system from retinal imaging were included. Data were extracted, assessed and reported using PRISMA guidelines.
Results:
Twenty-seven studies were included in this review. Nineteen studies used AI systems to diagnose ROP, classify the staging of ROP, diagnose the presence of pre-plus or plus disease, or assess the quality of retinal images. The included studies reported a sensitivity of 71%-100%, specificity of 74-99% and area under the curve of 91-99% for the primary outcome of the study. AI techniques were comparable to the assessment of ophthalmologists in terms of overall accuracy and sensitivity. Eight studies evaluated vascular severity scores and were able to accurately differentiate severity using an automated classification score.
Conclusion:
Artificial intelligence for ROP diagnosis is a growing field, and many potential utilities have already been identified, including the presence of plus disease, staging of disease and a new automated severity score. AI has a role as an adjunct to clinical assessment; however, there is insufficient evidence to support its use as a sole diagnostic tool currently.
