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Multi-label classification of pelvic organ prolapse using stress magnetic resonance imaging with deep learning

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Abstract

Introduction and hypothesis

We aimed to develop a deep learning-based multi-label classification model to simultaneously diagnose three types of pelvic organ prolapse using stress magnetic resonance imaging (MRI).

Methods

Our dataset consisted of 213 midsagittal labeled MR images at maximum Valsalva. For each MR image, the two endpoints of the sacrococcygeal inferior-pubic point line were auto-localized. Based on this line, a region of interest was automatically selected as input to a modified deep learning model, ResNet-50, for diagnosis. An unlabeled MRI dataset, a public dataset, and a synthetic dataset were used along with the labeled image dataset to train the model through a novel training strategy. We conducted a fivefold cross-validation and evaluated the classification results using precision, recall, F1 score, and area under the curve (AUC).

Results

The average precision, recall, F1 score, and AUC of our proposed multi-label classification model for the three types of prolapse were 0.84, 0.72, 0.77, and 0.91 respectively, which were improved from 0.64, 0.53, 0.57, and 0.83 from the original ResNet-50. Classification took 0.18 s to diagnose one patient.

Conclusions

The proposed deep learning-based model were demonstrated feasible and fast in simultaneously diagnosing three types of prolapse based on pelvic floor stress MRI, which could facilitate computer-aided prolapse diagnosis and treatment planning.

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Acknowledgements

We gratefully acknowledge support from NSFC General Program grant 31870942, Peking University Clinical Medicine Plus X—Young Scholars Project PKU2020LCXQ017 and PKU2021LCXQ028, PKU-Baidu Fund 2020BD039, NIH R01 HD038665, and P50 HD044406.

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Authors and Affiliations

  1. University of Michigan–Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai, 200240, China

    Xinyi Wang, Da He & Fei Feng

  2. Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA

    James A. Ashton-Miller

  3. Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, MI, 48109, USA

    John O. L. DeLancey

  4. Biomedical Engineering Department, Peking University, Beijing, 100191, China

    Jiajia Luo

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  1. Xinyi Wang
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  2. Da He
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  3. Fei Feng
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  4. James A. Ashton-Miller
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  5. John O. L. DeLancey
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  6. Jiajia Luo
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Contributions

X.Y. Wang: protocol/project development, data analysis, manuscript writing/editing; D. He: protocol/project development, data analysis, manuscript writing/editing; F. Feng: data analysis, manuscript editing; J.A. Ashton-Miller: protocol/project development, manuscript editing. J.O.L. DeLancey: protocol/project development, data collection or management, data analysis, manuscript editing; J.J. Luo: protocol/project development, data collection or management, data analysis, manuscript writing/editing

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Correspondence to Jiajia Luo.

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Wang, X., He, D., Feng, F. et al. Multi-label classification of pelvic organ prolapse using stress magnetic resonance imaging with deep learning. Int Urogynecol J 33, 2869–2877 (2022). https://doi.org/10.1007/s00192-021-05064-7

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  • DOI: https://doi.org/10.1007/s00192-021-05064-7

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