ArticleInternational journal of ophthalmology2023
Prediction of SMILE surgical cutting formula based on back propagation neural network.
Article in International journal of ophthalmology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Artificial intelligence applications in refractive error management: A systematic review and meta-analysis.PLOS digital health · 2025Article
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8 authors.
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Abstract
aimTo predict cutting formula of small incision lenticule extraction (SMILE) surgery and assist clinicians in identifying candidates by deep learning of back propagation (BP) neural network.
methodsA prediction program was developed by a BP neural network. There were 13 188 pieces of data selected as training validation. Another 840 eye samples from 425 patients were recruited for reverse verification of training results. Precision of prediction by BP neural network and lenticule thickness error between machine learning and the actual lenticule thickness in the patient data were measured.
resultsAfter training 2313 epochs, the predictive SMILE cutting formula BP neural network models performed best. The values of mean squared error and gradient are 0.248 and 4.23, respectively. The scatterplot with linear regression analysis showed that the regression coefficient in all samples is 0.99994. The final error accuracy of the BP neural network is -0.003791±0.4221102 µm.
conclusionWith the help of the BP neural network, the program can calculate the lenticule thickness and residual stromal thickness of SMILE surgery accurately. Combined with corneal parameters and refraction of patients, the program can intelligently and conveniently integrate medical information to identify candidates for SMILE surgery.
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