Deep Learning Algorithms May be Beneficial in Screening Candidates for Refractive Surgery
In this diagnostic, cross-sectional study, 6465 corneal tomographic images from 1385 patients were examined. Pentacam InceptionResNetV2 Screening System (PIRSS), a tomographic-based screening tool, was developed to screen potential patients for refractive surgery. On the validation data set, the model achieved an overall detection accuracy of 94.7%. On the independent test data set, the model achieved an overall detection accuracy of 95%, which is on par with a senior ophthalmologist who specializes in refractive surgery (92.8%).
The model also performed better than classifiers in identifying corneas with contraindications for refractive surgery (95% vs 81%; P < 0.001) in this population.
The authors concluded that the PRISS model appears to be useful in providing corneal information and identifying at-risk patients and may provide guidance to refractive surgeon in screening candidates.
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Xie Y, Zhao L, Yang X, et al. Screening candidates for refractive surgery with corneal tomographic–based deep learning. JAMA Ophthalmol. Published online March 26, 2020. doi:10.1001/jamaophthalmol.2020.0507