Centro de Informática (CIn) - Universidade Federal de Pernambuco (UFPE) - v. Jornalista Aníbal Fernandes, Cidade Universitária, 50740-560, Recife, PE, Brazil; Universidade Estadual de Ciências da Saúde de Alagoas (UNCISAL), Brazil; Brazilian Study Group of Artificial Intelligence and Corneal Analysis (BrAIn), Brazil.
Centro de Informática (CIn) - Universidade Federal de Pernambuco (UFPE) - v. Jornalista Aníbal Fernandes, Cidade Universitária, 50740-560, Recife, PE, Brazil; Brazilian Study Group of Artificial Intelligence and Corneal Analysis (BrAIn), Brazil.
Comput Biol Med. 2019 Jun;109:263-271. doi: 10.1016/j.compbiomed.2019.04.019. Epub 2019 Apr 25.
The Corvis ST provides measurements of intraocular pressure (IOP) and a biomechanically-corrected IOP (bIOP). IOP influences corneal deflection amplitude (DA), which may affect the diagnosis of keratoconus. Compensating for IOP in DA values may improve the detection of keratoconus.
195 healthy eyes and 136 eyes with keratoconus were included for developing different approaches to distinguish normal and keratoconic corneas using attribute selection and discriminant function. The IOP compensation is proposed by dividing the DA by the IOP values. The first approaches include DA compensated for either IOP or bIOP and other parameters from the deformation corneal response (DCR). Another approach integrated the horizontal corneal thickness profile (HCTP). The best classifiers developed were applied in a validation database of 156 healthy eyes and 87 eyes with keratoconus. Results were compared with the current Corvis Biomechanical Index (CBI).
The best biomechanical approach used the DA values compensated by IOP (Approach 2) using a linear discriminant function and reached AUC 0.954, with a sensitivity of 88.2% and a specificity of 97.4%. When thickness horizontal profile data was integrated (Approach 4), the best function was the diagquadratic, resulting in an AUC of 0.960, with a sensitivity of 89.7% and a specificity of 96.4%. There was no significant difference in the results between approaches 2 and 4 with the CBI in the training and validation databases.
By compensating for the IOP, and with the horizontal thickness profile included or excluded, it was possible to generate a classifier based only on biomechanical information with a similar result to the CBI.
Corvis ST 可测量眼压 (IOP) 和经生物力学校正的眼压 (bIOP)。IOP 会影响角膜偏折幅度 (DA)，这可能会影响圆锥角膜的诊断。补偿 DA 值中的 IOP 可能会提高圆锥角膜的检出率。
纳入 195 只健康眼和 136 只圆锥角膜眼，通过属性选择和判别函数，开发了不同的方法来区分正常和圆锥角膜的角膜。通过将 DA 除以 IOP 值来实现对 DA 的 IOP 补偿。第一种方法包括对 IOP 或 bIOP 以及变形角膜反应 (DCR) 的其他参数进行 DA 补偿。另一种方法则整合了水平角膜厚度分布 (HCTP)。将开发的最佳分类器应用于包含 156 只健康眼和 87 只圆锥角膜眼的验证数据库中。并将结果与当前的 Corvis 生物力学指数 (CBI) 进行比较。
使用线性判别函数的最佳生物力学方法是使用 IOP 补偿的 DA 值 (方法 2)，其 AUC 为 0.954，灵敏度为 88.2%，特异性为 97.4%。当整合水平厚度分布数据时（方法 4），最佳函数为 diagquadratic，AUC 为 0.960，灵敏度为 89.7%，特异性为 96.4%。在训练和验证数据库中，方法 2 和 4 与 CBI 的结果没有显著差异。
通过补偿 IOP，并结合或不结合水平厚度分布，可以基于生物力学信息生成一个与 CBI 相似的分类器。
