TY - JOUR
T1 - Enhanced Tomographic Assessment to Detect Corneal Ectasia Based on Artificial Intelligence
AU - Lopes, Bernardo T.
AU - Ramos, Isaac C.
AU - Salomão, Marcella Q.
AU - Guerra, Frederico P.
AU - Schallhorn, Steve C.
AU - Schallhorn, Julie M.
AU - Vinciguerra, Riccardo
AU - Vinciguerra, Paolo
AU - Price, Francis W.
AU - Price, Marianne O.
AU - Reinstein, Dan Z.
AU - Archer, Timothy J.
AU - Belin, Michael W.
AU - Machado, Aydano P.
AU - Ambrósio, Renato
N1 - Publisher Copyright:
© 2018 Elsevier Inc.
PY - 2018/11
Y1 - 2018/11
N2 - Purpose: To improve the detection of corneal ectasia susceptibility using tomographic data. Design: Multicenter case-control study. Methods: Data from patients from 5 different clinics from South America, the United States, and Europe were evaluated. Artificial intelligence (AI) models were generated using Pentacam HR (Oculus, Wetzlar, Germany) parameters to discriminate the preoperative data of 3 groups: stable laser-assisted in situ keratomileusis (LASIK) cases (2980 patients with minimum follow-up of 7 years), ectasia susceptibility (71 eyes of 45 patients that developed post-LASIK ectasia [PLE]), and clinical keratoconus (KC; 182 patients). Model accuracy was independently tested in a different set of stable LASIK cases (298 patients with minimum follow-up of 4 years) and in 188 unoperated patients with very asymmetric ectasia (VAE); these patients presented normal topography (VAE-NT) in 1 eye and clinically diagnosed ectasia in the other (VAE-E). Accuracy was evaluated with ROC curves. Results: The random forest (RF) provided highest accuracy among AI models in this sample with 100% sensitivity for clinical ectasia (KC+VAE-E; cutoff 0.52), being named Pentacam Random Forest Index (PRFI). Considering all cases, the PRFI had an area under the curve (AUC) of 0.992 (94.2% sensitivity, 98.8% specificity; cutoff 0.216), being statistically higher than the Belin/Ambrósio deviation (BAD-D; AUC = 0.960, 87.3% sensitivity, 97.5% specificity; P =.006, DeLong's test). The optimized cutoff of 0.125 provided sensitivity of 85.2% for VAE-NT and 80% for PLE, with 96.6% specificity. Conclusion: The PRFI enhances ectasia diagnosis. Further integrations with corneal biomechanical parameters and with the corneal impact from laser vision correction are needed for assessing ectasia risk.
AB - Purpose: To improve the detection of corneal ectasia susceptibility using tomographic data. Design: Multicenter case-control study. Methods: Data from patients from 5 different clinics from South America, the United States, and Europe were evaluated. Artificial intelligence (AI) models were generated using Pentacam HR (Oculus, Wetzlar, Germany) parameters to discriminate the preoperative data of 3 groups: stable laser-assisted in situ keratomileusis (LASIK) cases (2980 patients with minimum follow-up of 7 years), ectasia susceptibility (71 eyes of 45 patients that developed post-LASIK ectasia [PLE]), and clinical keratoconus (KC; 182 patients). Model accuracy was independently tested in a different set of stable LASIK cases (298 patients with minimum follow-up of 4 years) and in 188 unoperated patients with very asymmetric ectasia (VAE); these patients presented normal topography (VAE-NT) in 1 eye and clinically diagnosed ectasia in the other (VAE-E). Accuracy was evaluated with ROC curves. Results: The random forest (RF) provided highest accuracy among AI models in this sample with 100% sensitivity for clinical ectasia (KC+VAE-E; cutoff 0.52), being named Pentacam Random Forest Index (PRFI). Considering all cases, the PRFI had an area under the curve (AUC) of 0.992 (94.2% sensitivity, 98.8% specificity; cutoff 0.216), being statistically higher than the Belin/Ambrósio deviation (BAD-D; AUC = 0.960, 87.3% sensitivity, 97.5% specificity; P =.006, DeLong's test). The optimized cutoff of 0.125 provided sensitivity of 85.2% for VAE-NT and 80% for PLE, with 96.6% specificity. Conclusion: The PRFI enhances ectasia diagnosis. Further integrations with corneal biomechanical parameters and with the corneal impact from laser vision correction are needed for assessing ectasia risk.
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U2 - 10.1016/j.ajo.2018.08.005
DO - 10.1016/j.ajo.2018.08.005
M3 - Article
C2 - 30098348
AN - SCOPUS:85053808778
SN - 0002-9394
VL - 195
SP - 223
EP - 232
JO - American Journal of Ophthalmology
JF - American Journal of Ophthalmology
ER -