Prediction of CW chord after cataract surgery from IOL Master 700 measurement data based on SVR algorithm and BP neural network

Abstract

AIM: To observe the changes in the Chang-Warning chord(CW chord)before and after cataract surgery using the IOL Master 700 and predict the CW chord using an artificial intelligence prediction model and preoperative measurement data.METHODS: The analysis was conducted on the preoperative and postoperative IOL Master 700 measurements of 304 cataract patients. This included astigmatism vector value, average keratometry, axial length, anterior chamber depth, lens thickness, corneal central thickness, white-to-white, the position of the Purkinje reflex I image relative to the corneal center and pupil center, and the CW chord. A prediction model based on the SVR algorithm and the BP neural network algorithm was established to predict the postoperative CW chord using the preoperative CW chord and ocular biological parameters.RESULTS: The X component of the CW chord showed a slight shift in the temporal direction in both the left and right eyes after cataract surgery, while the Y component changed little. The SVR model, using the preoperative CW chord and other preoperative biometric parameters as input data, was able to predict the X and Y components of the CW chord more accurately than the BP neural network.CONCLUSION: The CW chord can be directly measured with a coaxial fixation light using various biometers, corneal topographers, or tomographers. The use of the SVR algorithm can accurately predict the postoperative CW chord before cataract surgery.

Keywords

• alpha angle
• kappa angle
• chang-warning chord(cw chord)
• deep learning
• cataract
• svr algorithm
• bp neural network
• iol master 700