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Evaluation of continuous curvilinear capsulorhexis based on a neural-network

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Continuous curvilinear capsulorhexis (CCC), as a prerequisite for successful cataract surgery, is one of the most important and difficult steps in phacoemulsification. In clinical practice, the size and circularity of the capsular tear and eccentricity with the lens are often employed as indicators to evaluate the effect of CCC.

Methods

We present a neural network-based model to improve the efficiency and accuracy of evaluation for capsulorhexis results. The capsulorhexis results evaluation model consists of the detection network based on U-Net and the nonlinear fitter built from fully connected layers. The detection network is responsible for detecting the positions of the round capsular tear and lens margin, and the nonlinear fitter is utilized to fit the outputs of the detection network and to compute the capsulorhexis results evaluation indicators. We evaluate the proposed model on an artificial eye phantom and compare its performance with the medical evaluation method.

Results

The experimental results show that the average detection error of the proposed evaluation model is within 0.04 mm. Compared with the medical method (the average detection error is 0.28 mm), the detection accuracy of the proposed evaluation model is more accurate and stable.

Conclusion

We propose a neural network-based capsulorhexis results evaluation model to improve the accuracy of evaluation for capsulorhexis results. The results of the evaluation experiments show that the proposed results evaluation model evaluates of the effect of capsulorhexis better than the medical evaluation method.

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Funding

This work is supported by the National Natural Science Foundation of China (Grant Nos. 51875011) and the National Key Research and Development Program of China (Grant Nos. 2017YFB1302700).

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Authors and Affiliations

  1. School of Mechanical Engineering and Automation, Beihang University, Beijing, 100083, China

    Chuang Lin, Yang Yang, Handa Gao & Chenhan Guang

  2. College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China

    Yu Zheng

  3. College of Mechanical and Storage and Transportation Engineering, China University of Petroleum-Beijing, Beijing, China

    Chenhan Guang

  4. Eye Center of Beijing Tongren Hospital, Capital Medical University, Beijing, 100005, China

    Ke Ma

Authors
  1. Chuang Lin
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  2. Yang Yang
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  3. Handa Gao
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  4. Yu Zheng
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  6. Chenhan Guang
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Correspondence to Chenhan Guang.

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All procedures performed in studies involving animals were in accordance with the protocol approved by the Laboratory Animal Ethics Committee of Capital Medical University.

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Lin, C., Yang, Y., Gao, H. et al. Evaluation of continuous curvilinear capsulorhexis based on a neural-network. Int J CARS 18, 2203–2212 (2023). https://doi.org/10.1007/s11548-023-02973-4

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  • DOI: https://doi.org/10.1007/s11548-023-02973-4

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