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AI eye to eye with ophthalmologists in diagnosing corneal infections, study finds

Woman undergoing an eye exam

Research theme

Data, diagnostics and decision tools

People involved

Dr Xiaoxuan Liu

Senior Clinician Scientist in AI and Digital Health Technologies

Professor Yemisi Takwoingi

Professor of Test Evaluation and Evidence Synthesis

Eye care specialists could see artificial intelligence help in diagnosing infectious keratitis (IK), a leading cause of corneal blindness worldwide, as a new study finds that deep learning models showed similar levels of accuracy in identifying infection.

In a meta-analysis study published in eClinicalMedicine, a global team of researchers led by University of Birmingham’s Dr Darren Ting and featuring Dr Xiaoxuan Liu and Professor Yemisi Takwoingi from the NIHR Birmingham Biomedical Research Centre’s Data, Diagnostics and Decision Tools theme, analysed 35 studies that utilised Deep Learning (DL) models to diagnose infectious keratitis.

AI models in the study matched the diagnostic accuracy of ophthalmologists, exhibiting a sensitivity of 89.2% and specificity of 93.2%, compared to ophthalmologists’ 82.2% sensitivity and 89.6% specificity.

“AI has the potential to provide fast, reliable diagnoses, which could revolutionise how we manage corneal infections globally.”

Dr Darren Ting, senior author

The models in the study had analysed over 136,000 corneal images combined, and the authors say that the results further demonstrate the potential use of artificial intelligence in clinical settings.

Dr Darren Ting, Senior author of the study, Birmingham Health Partners (BHP) Fellow and Consultant Ophthalmologist, University of Birmingham said:

“Our study shows that AI has the potential to provide fast, reliable diagnoses, which could revolutionise how we manage corneal infections globally. This is particularly promising for regions where access to specialist eye care is limited, and can help to reduce the burden of preventable blindness worldwide.”

The AI models also proved effective at differentiating between healthy eyes, infected corneas, and the various underlying causes of IK, such as bacterial or fungal infections.

While these results highlight the potential of DL in healthcare, the study’s authors emphasised the need for more diverse data and further external validation to increase the reliability of these models for clinical use.

Infectious keratitis, an inflammation of the cornea, affects millions, particularly in low- and middle-income countries where access to specialist eye care is limited. As AI technology continues to grow and play a pivotal role in medicine, it may soon become a key tool in preventing corneal blindness globally.