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New artificial intelligence index based on Scheimpflug corneal tomography to distinguish subclinical keratoconus from
Gildásio Castello Almeida1, Rodrigo Capobianco Guido, Henrique Monteiro Balarin Silva
1From the Faculty of Medicine of São José do Rio Preto, São José do Rio Preto, São Paulo, Brazil (Almeida Junior, Brandão, de Mattos); Base Hospital of São José do Rio Preto, São José do Rio Preto, São Paulo, Brazil (Almeida Junior); Visum Eye Center, São José do Rio Preto, São Paulo, Brazil (Almeida Junior); Department of Computer Science and Statistics, Institute of Biosciences, Letters, and Exact Sciences, São Paulo State University at São José do Rio Preto, São Paulo, Brazil (Guido); Rio Claro Eye Institute, Rio Claro, São Paulo, Brazil (Balarin Silva); Department of Civil Engineering and Industrial Design, School of Engineering, University of Liverpool, Liverpool, United Kingdom (Lopes); Department of Ophthalmology, Federal University of São Paulo, São Paulo, Brazil (Lopes, Machado, Ambrósio); Computing Institute, Federal University of Alagoas, Maceió, Brazil (Machado); Department of Ophthalmology, Federal University the State of Rio de Janeiro, Rio de Janeiro, Brazil (Ambrósio).
Purpose:
To assess the efficiency of an index derived from multiple logistic regression analysis (MLRA) to measure differences in corneal tomography findings between subclinical keratoconus (KC) in 1 eye, corneal ectasia, and healthy corneas.
Setting:
2 private Brazilian ophthalmological centers.
Design:
Multicenter case-control study.
Methods:
This study included 187 eyes with very asymmetric ectasia and with normal corneal topography and tomography (VAE-NTT) in the VAE-NTT group, 2296 eyes with healthy corneas in the control group (CG), and 410 eyes with ectasia in the ectasia group. An index, termed as Boosted Ectasia Susceptibility Tomography Index (BESTi), was derived using MLRA to identify a cutoff point to distinguish patients in the 3 groups. The groups were divided into 2 subgroups with an equal number of patients: validation set and external validation (EV) set.
Results:
2893 patients with 2893 eyes were included. BESTi had an area under the curve (AUC) of 0.91 with 86.02% sensitivity (Se) and 83.97% specificity (Sp) between CG and the VAE-NTT group in the EV set, which was significantly greater than those of the Belin-Ambrósio Deviation Index (BAD-D) (AUC: 0.81; Se: 66.67%; Sp: 82.67%; P < .0001) and Pentacam random forest index (PRFI) (AUC: 0.87; Se: 78.49%; Sp: 79.88%; P = .021).
Conclusions:
BESTi facilitated early detection of ectasia in subclinical KC and demonstrated higher Se and Sp than PRFI and BAD-D for detecting subclinical KC.
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