Diagnostics (Dec 2024)

<i>MECP2</i> Duplication Syndrome: AI-Based Diagnosis, Severity Scale Development and Correlation with Clinical and Molecular Variables

  • Lourdes Vega-Hanna,
  • Dídac Casas-Alba,
  • Sol Balsells,
  • Mercè Bolasell,
  • Patricia Rubio,
  • Ana García-García,
  • Oscar García-García,
  • Mar O’Callaghan,
  • Ainhoa Pascual-Alonso,
  • Judith Armstrong,
  • MDS Group,
  • Antonio F. Martinez-Monseny

DOI
https://doi.org/10.3390/diagnostics15010010
Journal volume & issue
Vol. 15, no. 1
p. 10

Abstract

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Background: MECP2 duplication syndrome (MDS) (MIM#300260) is a rare X-linked neurodevelopmental disorder. This study aims to (1) develop a specific clinical severity scale, (2) explore its correlation with clinical and molecular variables, and (3) automate diagnosis using the Face2gene platform. Methods: A retrospective study was conducted on genetically confirmed MDS patients who were evaluated at a pediatric hospital between 2012 and 2024. Epidemiological, clinical, and molecular data were collected. A standardized clinical questionnaire was collaboratively developed with input from physicians and parents. Patient photographs were used to train Face2Gene. Results: Thirty-five patients (0–24 years, 30 males) were included. Key features in males comprised intellectual disability (100%), hypotonia (93%), autism spectrum disorder (77%) and developmental regression (52%). Recurrent respiratory infections (79%), dysphagia (73%), constipation (73%) and gastroesophageal reflux (57%) were common. Seizures occurred in 53%, with 33% being treatment-refractory. The Face2Gene algorithm was successfully trained to identify MDS. A specific clinical severity scale (MECPDup) was developed and validated, correlating with the MBA (a scale developed for Rett syndrome). The MECPDup score was significantly higher in males (p p = 0.003). It showed significant positive correlations with age (p p = 0.044). Conclusions: This study expands the understanding of MDS through comprehensive clinical and molecular insights. The integration of AI-based facial recognition technology and the development of the MECPDup severity scale hold promise for enhancing diagnostic accuracy, monitoring disease progression, and evaluating treatment responses in individuals affected by MDS.

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