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Artificial intelligence for nailfold capillaroscopy analyses – a proof of concept application in juvenile dermatomyositis

Pediatric Research volume 95, pages 981–987 (2024)Cite this article

Abstract

Background

Biomarkers for idiopathic inflammatory myopathies are difficult to identify and may involve expensive laboratory tests. We assess the potential for artificial intelligence (AI) to differentiate children with juvenile dermatomyositis (JDM) from healthy controls using nailfold capillaroscopy (NFC) images. We also assessed the potential of NFC images to reflect the range of disease activity with JDM.

Methods

A total of 1,120 NFC images from 111 children with active JDM, diagnosed between 1990 and 2020, and 321 NFC images from 31 healthy controls were retrieved from the CureJM JDM Registry. We built a lightweight and explainable deep neural network model called NFC-Net. Images were downscaled by interpolation techniques to reduce the computational cost.

Results

NFC-Net achieved high performance in differentiating patients with JDM from controls, with an area under the ROC curve (AUROC) of 0.93 (0.84, 0.99) and accuracy of 0.91 (0.82, 0.92). With sensitivity (0.85) and specificity (0.90) resulted in model precision of 0.95. The AUROC and accuracy for predicting clinical disease activity from inactivity were 0.75 (0.61, 0.81) and 0.74 (0.65, 0.79).

Conclusion

The good performance of the NFC-Net demonstrates that NFC images are sufficient for detecting often unrecognized JDM disease activity, providing a reliable indicator of disease status.

Impact

  • Proposed NFC-Net can accurately predict children with JDM from healthy controls using nailfold capillaroscopy (NFC) images. Additionally, it predicts the scores to JDM disease activity versus no activity. Equipped with gradients, NFC-Net is explainable and gives visual information beside the reported accuracies. NFC-Net is computationally efficient since it is applied to substantially downscaled NFC images. Furthermore, the model can be wrapped within an edge-based device like a mobile application that is accessible to both clinicians and patients.

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Fig. 1: Examples of control and NFC images.
Fig. 2: Proposed lightweight NFC-Net architecture for JDM prediction task.
Fig. 3: Confusion matrix and area under the receiver characteristic curve (AUROC).
Fig. 4: Attribution map by integrated gradients.
Fig. 5: Confusion matrix for binary classification of finger disease prediction.
Fig. 6: ROC curve for the proposed explainable convolutional neural network.

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Data availability

The datasets used during the current study are not publicly available due to the privacy of participants but may be available upon request to the CureJM foundation.

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Acknowledgements

Rachel Davis assisted with technical revisions to the manuscript.

Funding

Supported in part by the Children’s Hospital of Orange County (CHOC) Chief Scientific Officer (CSO) Award, the Vivian Allison and Daniel J. Pachman Fund, the DenUyl Family Fund, the Cure JM Foundation, and other much-appreciated donors. We also acknowledge the contribution of Wellesley College Honors Program. The REDCap database is supported by NUCATS and funded in part by a Clinical and Translational Science award (CTSA) grant from the National Institutes of Health (NIH), [UL1TR001422].

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Authors and Affiliations

  1. Research Institute, Children’s Hospital of Orange County (CHOC), Orange, CA, USA

    Peyman Hosseinzadeh Kassani, Louis Ehwerhemuepha, Chloe Martin-King & Ryan Kassab

  2. Department of Biological Sciences, Wellesley College, Wellesley, MA, USA

    Ellie Gibbs

  3. Division of Pediatric Rheumatology, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL, USA

    Gabrielle Morgan & Lauren M. Pachman

  4. Northwestern Feinberg School of Medicine, Chicago, IL, USA

    Lauren M. Pachman

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  1. Peyman Hosseinzadeh Kassani
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Contributions

P.H.K. and L.E. made substantial contributions to conception and design of the study and drafted the article. L.E. designed the problem and motivation of the research. P.H.K. designed the AI-based model. L.E., P.H.K., C.M.K., and R.K. cooperated in coding and implementation. E.G., G.M., and L.M. P. helped with data acquisition and clinical assessment. All authors revised the manuscript critically for important intellectual content. All authors give final approval of the version to be published.

Corresponding author

Correspondence to Louis Ehwerhemuepha.

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The authors declare no competing interests.

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Informed consent was obtained from patients whose data were captured as part of the CureJM JDM Registry.

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Kassani, P.H., Ehwerhemuepha, L., Martin-King, C. et al. Artificial intelligence for nailfold capillaroscopy analyses – a proof of concept application in juvenile dermatomyositis. Pediatr Res 95, 981–987 (2024). https://doi.org/10.1038/s41390-023-02894-7

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