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Revolutionizing congenital central hypoventilation syndrome screening: the potential of machine learning approaches

Pediatric Research (2024)Cite this article

Congenital central hypoventilation syndrome (CCHS) is a rare autosomal dominant disorder characterized by central hypoventilation and apnea due to attenuated or absent peripheral and central chemoreceptor responsiveness.1 CCHS can present at any age, as early as within hours of birth, and sometimes may be misdiagnosed as sudden infant death syndrome (SIDS) due to unexplained prolonged apnea leading to death.2 Early diagnosis and timely intervention are crucial for long-term prognosis, while there are still a considerable amount of cases remaining undiagnosed worldwide due to the lack of awareness of the disease and the need of genetic testing.

The PHOX2B gene stands out as the primary pathogenic gene for CCHS. Genetic testing assumes a crucial role in both diagnosing and managing CCHS, aiding in the prediction of clinical severity, disease staging, and potential complications. Nevertheless, diagnosing CCHS in developing countries, particularly in economically disadvantaged rural areas, poses significant challenges. Similar to various other genetic syndromes, a majority of children affected by CCHS often exhibit abnormal facial features. The advent of machine learning, leveraging distinctive facial characteristics, holds the promise of providing convenient and efficient tools for screening rare genetic syndromes.3,4 It is noteworthy that, unlike some other genetic syndromes such as Noonan syndrome and Williams syndrome, the facial features associated with CCHS are often less conspicuous. Machine learning has the capability to discern much subtler differences than the human eye alone, presenting an opportunity for disease diagnosis. However, the body of relevant research on this subject remains limited.

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References

  1. Slattery, S. M. et al. Transitional care and clinical management of adolescents, young adults, and suspected new adult patients with congenital central hypoventilation syndrome. Clin. Auton. Res. 33, 231–249 (2023).

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  2. Trang, H. et al. Guidelines for diagnosis and management of congenital central hypoventilation syndrome. Orphanet J. Rare Dis. 15, 1–21 (2020).

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Authors and Affiliations

  1. Department of Pediatric Cardiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China

    Qinchang Chen & Shushui Wang

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  1. Qinchang Chen
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  2. Shushui Wang
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QC drafted the initial manuscript and SW critically revised the manuscript. Both authors have approved the final version of the manuscript.

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Correspondence to Shushui Wang.

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The authors declare no competing interests.

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Chen, Q., Wang, S. Revolutionizing congenital central hypoventilation syndrome screening: the potential of machine learning approaches. Pediatr Res (2024). https://doi.org/10.1038/s41390-024-03090-x

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  • DOI: https://doi.org/10.1038/s41390-024-03090-x

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