Integration of Scheimpflug-Based Corneal Tomography and Biomechanical Assessments for Enhancing Ectasia Detection

• Autor(a) principal: Ambrosio, Renato, Jr. [UNIFESP]
• Data de Publicação: 2017
• Outros Autores: Lopes, Bernardo T. [UNIFESP], Faria-Correia, Fernando, Salomao, Marcella Q. [UNIFESP], Buhren, Jens, Roberts, Cynthia J., Elsheikh, Ahmed, Vinciguerra, Riccardo, Vinciguerra, Paolo
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNIFESP
• dARK ID: ark:/48912/001300000sdc0
• DOI: 10.3928/1081597X-20170426-02
• Texto Completo: http://dx.doi.org/10.3928/1081597X-20170426-02; https://repositorio.unifesp.br/handle/11600/53585

PURPOSE: To present the Tomographic and Biomechanical Index (TBI), which combines Scheimpflugbased corneal tomography and biomechanics for enhancing ectasia detection. METHODS: Patients from different continents were retrospectively studied. The normal group included 1 eye randomly selected from 480 patients with normal corneas and the keratoconus group included 1 eye randomly selected from 204 patients with keratoconus. There were two groups: 72 ectatic eyes with no surgery from 94 patients with very asymmetric ectasia (VAE-E group) and the fellow eyes of these patients with normal topography (VAE-NT group). Pentacam HR and Corvis ST (Oculus Optikgerate GmbH, Wetzlar, Germany) parameters were analyzed and combined using different artificial intelligence methods. The accuracies for detecting ectasia of the Belin/Ambresio Deviation (BAD-D) and Corvis Biomechanical Index (CBI) were compared to the TBI, considering the areas under receiver operating characteristic curves (AUROCs). RESULTS: The random forest method with leave-oneout cross-validation (RF/LOOCV) provided the best artificial intelligence model. The AUROC for detecting ectasia (keratoconus, VAE-E, and VAE-NT groups) of the TBI was 0.996, which was statistically higher (DeLong et al., P < .001) than the BAD-D (0.956) and CBI (0.936). The TBI cut-off value of 0.79 provided 100% sensitivity for detecting clinical ectasia (keratoconus and VAE-E groups) with 100% specificity. The AUROCs for the TBI, BAD-D, and CBI were 0.985, 0.839, and 0.822 in the VAE-NT group (DeLong et al., P <.001). An optimized TBI cut-off value of 0.29 provided 90.4% sensitivity with 96% specificity in the VAE-NT group. CONCLUSIONS: The TBI generated by the RF/LOOCV provided greater accuracy for detecting ectasia than other techniques. The TBI was sensitive for detecting subclinical (fruste) ectasia among eyes with normal topography in very asymmetric patients. The TBI may also confirm unilateral ectasia, potentially characterizing the inherent ectasia susceptibility of the cornea, which should be the subject of future studies.