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Development and validation of a deep learning model to predict axial length from ultra-wide field images

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Abstract

Background

To validate the feasibility of building a deep learning model to predict axial length (AL) for moderate to high myopic patients from ultra-wide field (UWF) images.

Methods

This study included 6174 UWF images from 3134 myopic patients during 2014 to 2020 in Eye and ENT Hospital of Fudan University. Of 6174 images, 4939 were used for training, 617 for validation, and 618 for testing. The coefficient of determination (R2), mean absolute error (MAE), and mean squared error (MSE) were used for model performance evaluation.

Results

The model predicted AL with high accuracy. Evaluating performance of R2, MSE and MAE were 0.579, 1.419 and 0.9043, respectively. Prediction bias of 64.88% of the tests was under 1-mm error, 76.90% of tests was within the range of 5% error and 97.57% within 10% error. The prediction bias had a strong negative correlation with true AL values and showed significant difference between male and female (P < 0.001). Generated heatmaps demonstrated that the model focused on posterior atrophy changes in pathological fundus and peri-optic zone in normal fundus. In sex-specific models, R2, MSE, and MAE results of the female AL model were 0.411, 1.357, and 0.911 in female dataset and 0.343, 2.428, and 1.264 in male dataset. The corresponding metrics of male AL models were 0.216, 2.900, and 1.352 in male dataset and 0.083, 2.112, and 1.154 in female dataset.

Conclusions

It is feasible to utilize deep learning models to predict AL for moderate to high myopic patients with UWF images.

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Fig. 1: The pipeline of model construction. Images were resized to 1000 × 1000 pixels and applied to a linear mapping.
Fig. 2: Performance evaluation of AL prediction model.
Fig. 3
Fig. 4: Heatmap samples for AL prediction models.

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Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors appreciated all the colleagues involved in the whole process of the research.

Funding

1) National Natural Science Foundation of China (Grant No. 82301251). 2) Joint research project of new frontier technology in municipal hospitals (SHDC12018103). 3) Project of Shanghai Science and Technology (Grant No.20410710100). 4) Clinical Research Plan of SHDC (SHDC2020CR1043B). 5) Project of Shanghai Xuhui District Science and Technology (2020-015). 6) Shanghai Rising-Star Program (21QA1401500). 7) Shanghai Yangfan Project (23YF1445300).

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Author notes

  1. These authors contributed equally: Yunzhe Wang, Ruoyan Wei, Danjuan Yang.

Authors and Affiliations

  1. Eye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, China

    Yunzhe Wang, Ruoyan Wei, Danjuan Yang, Yang Shen, Lingling Niu, Meiyan Li & Xingtao Zhou

  2. NHC Key Laboratory of Myopia (Fudan University); Key Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai, China

    Yunzhe Wang, Ruoyan Wei, Danjuan Yang, Yang Shen, Lingling Niu, Meiyan Li & Xingtao Zhou

  3. Shanghai Research Center of Ophthalmology and Optometry, Shanghai, China

    Yunzhe Wang, Ruoyan Wei, Danjuan Yang, Yang Shen, Lingling Niu, Meiyan Li & Xingtao Zhou

  4. Shanghai Engineering Research Center of Laser and Autostereoscopic 3D for Vision Care, Shanghai, China

    Yunzhe Wang, Ruoyan Wei, Danjuan Yang, Yang Shen, Lingling Niu, Meiyan Li & Xingtao Zhou

  5. Shanghai Medical College and Zhongshan Hospital Immunotherapy Translational Research Center, Shanghai, China

    Ruoyan Wei

  6. Beijing Airdoc Technology Co., Ltd, Beijing, China

    Kaimin Song

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Contributions

YW: conceptualization, formal analysis, writing original draft; RW, DY: data acquisition, formal analysis, visualization; KS: model development; YS, LN: review and editing; ML, XZ: conceptualization, supervision, funding acquisition.

Corresponding authors

Correspondence to Meiyan Li or Xingtao Zhou.

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Wang, Y., Wei, R., Yang, D. et al. Development and validation of a deep learning model to predict axial length from ultra-wide field images. Eye 38, 1296–1300 (2024). https://doi.org/10.1038/s41433-023-02885-2

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