Machine-learning and artificial neural network technologies in the classification of postkeratotomic corneal deformity

Abstract

BACKGROUND: A thorough analysis of both optical and anatomical properties of the cornea in patients after anterior radial keratotomy is important in choosing the optical power of an intraocular lens in the surgical treatment of cataracts and other types of optical correction. Improving the classification of postkeratotomic corneal deformity is crucial in modern ophthalmology due to its diverse clinical presentation.

AIM: To develop an automated classification system for postkeratotomic corneal deformity using machine learning and artificial neural networks based on the analysis of topographic maps of the cornea.

MATERIALS AND METHODS: Depersonalized data from medical records of 250 patients aged 46–76 (mean, 59.63±5.95) years were analyzed. Moreover, 500 topographic maps of the anterior and posterior surfaces of the cornea were analyzed, and three stages of machine learning for postkeratotomic corneal deformity classification were performed.

RESULTS: Stage I, which involved topography analysis of the anterior and posterior surfaces of the cornea, allowed for the measurement of anterior and posterior corneal elevation in three ring-shaped zones. At stage II, a direct distribution neural network was selected and created during deep machine learning. Eight auxiliary parameters describing the shape of the anterior and posterior surfaces of the cornea were established. In Stage III, classification algorithms for postkeratotomic corneal deformity were developed based on the test-to-training sample ratio, which ranged from 75% to 91%.

CONCLUSION: The proposed artificial neural network classifies postkeratotomic corneal deformity types with an accuracy of 91%. The potential for further improving the training quality of this artificial neural network has been established. Neural network algorithms can become a useful tool for the automatic classification of postkeratotomic corneal deformity in patients after radial keratotomy.

Keywords

anterior radial keratotomy / artificial intelligence / machine learning / corneal topography

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