A Machine Learning-Based Method for Diagnosing Keratoconus Cases

Abstract

The invention relates to a method for diagnosing keratoconus cases based on machine learning. For the first time, support vector machine-recursive feature screening algorithm (SVM-REF) and gradient boosting tree (GBDT) algorithm in machine learning are applied to the accurate diagnosis of keratoconus cases. Diagnose, and carry out effective overall scheme design, process design and algorithm parameter setting for specific application examples. Through a large number of clinical case tests, the diagnostic accuracy of this method has been significantly improved and has basically met the clinical application.

Description

Technical field [0001] The present invention belongs to the field of ophthalmology medical diagnostics, involving machine learning techniques, especially a method based on machine learning to diagnose conical corneal cases. Background technique [0002] The cone angle refers to the cornea of ​​the center or the central or secondary center of the center, and the corneal expanded disease that is cone-launched, most of the youths of 20 years old, especially young male patients, often cause high irregularities Radiating and varying degrees of vision, the prognosis is not good, and it will eventually need the treatment of corneal transplantation. Because of a large vision and optimization of persistence, early screening and early treatment intervention is critical; but earlier intervention, bringing more diagnostic challenges, how to accurately identify early corneal expansion changes than determining the middle and advanced diseases challenge. The early screening of the conical corne...

AI Technical Summary

Problems solved by technology

Because keratoconus has a great impact on vision and visual function, early screening and early treatment intervention are the key; however, earlier intervention brings greater diagnostic challenges. How to accurately identify early corneal dilated changes is more important than determining advanced disease challenge

At present, there is no unified standard for early screening of keratoconus, and there is a lot of controversy. The difference in different parameters has caused great confusion to clinicians. A relatively accurate diagnosis can only be given after detailed consultation and analysis by experienced experts; At the same time, a large number of cases, limited experts, and complicated corneal parameters have added great difficulty to the early diagnosis of keratoconus.

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