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  • Clinical Research Article
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Computer-aided diagnostic screen for Congenital Central Hypoventilation Syndrome with facial phenotype

Pediatric Research (2024)Cite this article

Abstract

Background

Congenital Central Hypoventilation Syndrome (CCHS) has devastating consequences if not diagnosed promptly. Despite identification of the disease-defining gene PHOX2B and a facial phenotype, CCHS remains underdiagnosed. This study aimed to incorporate automated techniques on facial photos to screen for CCHS in a diverse pediatric cohort to improve early case identification and assess a facial phenotype–PHOX2B genotype relationship.

Methods

Facial photos of children and young adults with CCHS were control-matched by age, sex, race/ethnicity. After validating landmarks, principal component analysis (PCA) was applied with logistic regression (LR) for feature attribution and machine learning models for subject classification and assessment by PHOX2B pathovariant.

Results

Gradient-based feature attribution confirmed a subtle facial phenotype and models were successful in classifying CCHS: neural network performed best (median sensitivity 90% (IQR 84%, 95%)) on 179 clinical photos (versus LR and XGBoost, both 85% (IQR 75–76%, 90%)). Outcomes were comparable stratified by PHOX2B genotype and with the addition of publicly available CCHS photos (n = 104) using PCA and LR (sensitivity 83–89% (IQR 67–76%, 92–100%).

Conclusions

Utilizing facial features, findings suggest an automated, accessible classifier may be used to screen for CCHS in children with the phenotype and support providers to seek PHOX2B testing to improve the diagnostics.

Impact

  • Facial landmarking and principal component analysis on a diverse pediatric and young adult cohort with PHOX2B pathovariants delineated a distinct, subtle CCHS facial phenotype.

  • Automated, low-cost machine learning models can detect a CCHS facial phenotype with a high sensitivity in screening to ultimately refer for disease-defining PHOX2B testing, potentially addressing gaps in disease underdiagnosis and allow for critical, timely intervention.

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Fig. 1: Logistic regression modeling with principal component analysis preprocessing after Dlib facial landmarking was applied for each subject with congenital central hypoventilation syndrome (CCHS) compared to controls.
Fig. 2: Distribution of screening results for machine learning classifiers on clinical photos to predict congenital central hypoventilation syndrome (CCHS).

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Data availability

The clinical photos are HIPAA-protected and unavailable. The control photos in this project are publicly available through the UTKFace dataset and Dlib facial landmarking software is additionally open source. The publicly available photos with CCHS, code, and instruction may be available upon request.

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Acknowledgements

We are grateful for the introductory discussions held with Pavel Prusakov, PhD; Bimal Chaudhari, MD, MPH; George El-Ferzli, MD; Sven Bambach, PhD; and Steve Rust, PhD.

Funding

This study is funded in part by the Chicago Community Trust Foundation PHOX2B Patent Fund.

Author information

Authors and Affiliations

  1. Division of Autonomic Medicine, Department of Pediatrics, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL, USA

    Susan M. Slattery, Angeli Mittal, Casey M. Rand, Ilya Khaytin, Tracey M. Stewart & Debra E. Weese-Mayer

  2. Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

    Susan M. Slattery, Joshua J. Baker, Ilya Khaytin & Debra E. Weese-Mayer

  3. Department of Computer Science, Northwestern University McCormick School of Engineering, Evanston, IL, USA

    James Wilkinson, Angeli Mittal, Charlie Zheng, Nicholas Easton, Saumya Singh & David Demeter

  4. Division of Genetics, Department of Pediatrics, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL, USA

    Joshua J. Baker

  5. Stanley Manne Children’s Research Institute, Chicago, IL, USA

    Casey M. Rand & Debra E. Weese-Mayer

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Contributions

All authors have met authorship requirements. Specifically, S.M.S., J.W., A.M., C.Z., N.E., S.S., J.J.B., T.M.S., D.D. and D.E.W.-M. had substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data. S.M.S., J.W., A.M., C.Z., N.E., S.S., J.J.B., C.M.R., I.K., T.M.S., D.D. and D.E.W.-M. drafted the article or revised it critically for intellectual content. S.M.S., J.W., A.M., C.Z., N.E., S.S., J.J.B., C.M.R., I.K., T.M.S., D.D. and D.E.W.-M. provided final approval of this version to be published.

Corresponding author

Correspondence to Susan M. Slattery.

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The authors declare no competing interests.

Consent statement

Obtaining clinical photos of patients with CCHS is included in the standardized inpatient evaluation at the Center for Autonomic Medicine in Pediatrics at Ann & Robert H. Lurie Children’s Hospital of Chicago. For the four included in Fig. 2 of the manuscript, a separate and explicit consent statement was obtained from legal representatives of each child.

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Slattery, S.M., Wilkinson, J., Mittal, A. et al. Computer-aided diagnostic screen for Congenital Central Hypoventilation Syndrome with facial phenotype. Pediatr Res (2024). https://doi.org/10.1038/s41390-023-02990-8

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  • DOI: https://doi.org/10.1038/s41390-023-02990-8

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