Can AI Help Ophthalmologists Diagnose Corneal Infections? Study Offers Perspective
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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, Dr Darren Ting from the University of Birmingham conducted a review with a global team of researchers analysing 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. 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.
The AI models also proved effective at differentiating between healthy eyes, infected corneas, and the various underlying causes of infectious keratitis, such as bacterial or fungal infections. While these results highlight the potential of Deep Learning 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.
Reference: Diagnostic performance of deep learning for infectious keratitis: a systematic review and meta-analysis Ong, Zun Zheng et al. eClinicalMedicine, Volume 77, 102887
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