Corneal Curvature Measurements Utilizing a New Swept-source Optical Coherence Tomography Tomey OA-2000® and Comparison With IOL Master® 500 in Pterygium Patients

Hilmi, Mohd Radzi; Kamal, Khairidzan Mohd; Azemin, Mohd Zulfaezal Che; Ithnin, Mohd. Hafidz; Mustafa, Md Muziman Syah Md; Yusof@Alias, Firdaus; Ahmad, Naseem

doi:10.30659/sainsmed.v9i1.2918

Cited by 2 publications

(2 citation statements)

References 29 publications

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“…All participants provided informed consent. A Tomey corneal topographer (Takaratomy, Japan) was used to measure the corneal topography ( 29 ). The data of 1,302 myopic children and adolescents, aged 6–18 years, in the Tianjin Eye Hospital Optometry Center from 2013 to 2021 were collected retrospectively (2,604 eyes in total).…”

Section: Methodsmentioning

confidence: 99%

Evaluation of the corneal topography based on deep learning

Xu,

Yang,

Zhang

et al. 2024

Front. Med.

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PurposeThe current study designed a unique type of corneal topography evaluation method based on deep learning and traditional image processing algorithms. The type of corneal topography of patients was evaluated through the segmentation of important medical zones and the calculation of relevant medical indicators of orthokeratology (OK) lenses.MethodsThe clinical data of 1,302 myopic subjects was collected retrospectively. A series of neural network-based U-Net was used to segment the pupil and the treatment zone in the corneal topography, and the decentration, effective defocusing contact range, and other indicators were calculated according to the image processing algorithm. The type of corneal topography was evaluated according to the evaluation criteria given by the optometrist. Finally, the method described in this article was used to evaluate the type of corneal topography and compare it with the type classified by the optometrist.ResultsWhen the important medical zones in the corneal topography were segmented, the precision and recall of the treatment zone reached 0.9587 and 0.9459, respectively, and the precision and recall of the pupil reached 0.9771 and 0.9712. Finally, the method described in this article was used to evaluate the type of corneal topography. When the reviewed findings based on deep learning and image processing algorithms were compared to the type of corneal topography marked by the professional optometrist, they demonstrated high accuracy with more than 98%.ConclusionThe current study provided an effective and accurate deep learning algorithm to evaluate the type of corneal topography. The deep learning algorithm played an auxiliary role in the OK lens fitting, which could help optometrists select the parameters of OK lenses effectively.

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Section: Methodsmentioning

confidence: 99%

Evaluation of the corneal topography based on deep learning

Xu,

Yang,

Zhang

et al. 2024

Front. Med.

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“…A Tomey TMS-4A (tomey usa.com/) was used to measure corneal topography. [17][18][19] The automation script was written specifically based on the corneal topography software (TMS-4A SW) and used to convert the collected results into a tangent map for export. Tesseract (tesse ract.proje ctnap tha.com/) was used for optical character recognition; the corneal topographic data were then extracted and analysed.…”

Section: Data Acquisitionmentioning

confidence: 99%

Machine learning models for orthokeratology lens fitting and axial length prediction

Yang

Zhang

et al. 2023

Ophthalmic Physiologic Optic

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PurposeIn order to improve the efficiency of orthokeratology (OK) lens fitting and predict the axial length after 1 year of OK lens wear, machine learning models were proposed.MethodsClinical data from 1302 myopic subjects were collected retrospectively, and two machine learning models were implemented. Demographic and corneal topographic data were collected as input variables. The output variables were the parameters of the OK lens and the axial length after 1 year. Eighty percent of input variables was used as the training set and the remaining 20% was used as the validation set. The first alignment curve (AC1) of the OK lenses, deduced using machine learning models and formula calculation, were compared. Multiple regression models (support vector machine, Gaussian process, decision tree and random forest) were used to predict the axial length after 1 year. In addition, we classified data based on lens brand, and carried out more detailed parameter fitting and analysis for spherical and toric OK lenses.ResultsThe OK lens fitting model showed higher (R2 = 0.93) and lower errors (mean absolute error [MAE] = 0.19, mean square error [MSE] = 0.09) when predicting AC1, compared with the formula calculation (R2 = 0.66, MAE = 0.44, MSE = 0.25). The machine learning model still had high R2 values ranging from 0.91 to 0.96 when considering the brand and design of the OK lenses. Further, the R2 value for the axial length prediction model was 0.94, which indicated that the machine learning model had high accuracy and good robustness.ConclusionThe OK lens fitting model and the axial length prediction model played an important role in guiding OK lens fitting, with high accuracy and robustness in prediction performance.

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Corneal Curvature Measurements Utilizing a New Swept-source Optical Coherence Tomography Tomey OA-2000® and Comparison With IOL Master® 500 in Pterygium Patients

Cited by 2 publications

References 29 publications

Evaluation of the corneal topography based on deep learning

Evaluation of the corneal topography based on deep learning

Machine learning models for orthokeratology lens fitting and axial length prediction

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