Improving the diagnosis of postkeratotomic corneal deformity using artificial intelligence methods to optimize the calculation of intraocular lenses

Postkeratotomy corneal deformation (PKCD) has significant complexity in the calculation of intraocular lenses (IOL). Traditional methods of corneal topography assessment have errors, which dictates the need to develop automated solutions based on artificial intelligence (AI).

Purpose: to develop and validate a neural network model for automated analysis of corneal topographic data in order to improve the accuracy of IOL calculation in patients with PKRD.

Materials and methods. Anonymized results of medical records of 450  patients (aged 45 to 78 years) in the late period after radial keratotomy (RK) were used (95 patients underwent cataract surgery). In addition to the standard ophthalmological examination, all patients underwent Scheimpflug-imaging (Pentacam HR, Oculus, Germany). Multivariate analysis methods were carried out, a mathematical classification algorithm was developed.

Results. The developed prototype of the neural network model is able to automatically classify corneal topographic data into six types. Based on the postoperative refractive data, the predicted refractive result and correction factors for calculating the IOL using various formulas were calculated.

Conclusion. AI technologies and the correction factor database can become the basis for optimized calculation of the IOL optical power in patients with PCDR.

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