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Deep convolutional neural network-based algorithm for muscle biopsy diagnosis

Abstract

Histopathologic evaluation of muscle biopsy samples is essential for classifying and diagnosing muscle diseases. However, the numbers of experienced specialists and pathologists are limited. Although new technologies such as artificial intelligence are expected to improve medical reach, their use with rare diseases, such as muscle diseases, is challenging because of the limited availability of training datasets. To address this gap, we developed an algorithm based on deep convolutional neural networks (CNNs) and collected 4041 microscopic images of 1400 hematoxylin-and-eosin-stained pathology slides stored in the National Center of Neurology and Psychiatry for training CNNs. Our trained algorithm differentiated idiopathic inflammatory myopathies (mostly treatable) from hereditary muscle diseases (mostly non-treatable) with an area under the curve (AUC) of 0.996 and achieved better sensitivity and specificity than the diagnoses done by nine physicians under limited diseases and conditions. Furthermore, it successfully and accurately classified four subtypes of the idiopathic inflammatory myopathies with an average AUC of 0.958 and classified seven subtypes of hereditary muscle disease with an average AUC of 0.936. We also established a method to validate the similarity between the predictions made by the algorithm and the seven physicians using visualization technology and clarified the validity of the predictions. These results support the reliability of the algorithm and suggest that our algorithm has the potential to be used straightforwardly in a clinical setting.

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Fig. 1: Strategy, masked sample images, and deep convolutional neural network architecture.
Fig. 2: Differentiation of IIM and classification of IIM and non-myositis muscle disease.
Fig. 3: Visualization of CNNs predictions.

Data availability

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

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Funding

This study was supported by AMED under Grant Number 20ek0109348s0503 and Intramural Research Grant (2-5) for Neurological and Psychiatric Disorders of NCNP.

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Authors

Contributions

Y.K. designed and conducted the experiments and wrote the manuscript; M.O. reviewed the pathological and genetic data and performed the physicians’ test; H.N. and S.Y. analyzed the data and contributed to writing the manuscript; M.I., M.O., Y.S., J.T., A.I., T.K., YL.C., W.Y., and S.H. performed the physicians’ test; T.I. designed the deep learning algorithm; Y.T. prepared the experimental environment; R.T. and A.T. directed the project; F.M. contributed to obtaining grants and designing the framework of the study; I.N. made the pathological diagnoses for all cases and supervised the study. All authors read and approved the final paper.

Corresponding author

Correspondence to Ichizo Nishino.

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The authors declare no competing interests.

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Kabeya, Y., Okubo, M., Yonezawa, S. et al. Deep convolutional neural network-based algorithm for muscle biopsy diagnosis. Lab Invest (2021). https://doi.org/10.1038/s41374-021-00647-w

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