ArticleEuropean journal of pediatrics2025
An AI-assisted tool for automated growth monitoring in pediatric achondroplasia.
Article in European journal of pediatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Real-world growth outcomes of vosoritide in children with achondroplasia: a single-center cohort study.Journal of the Endocrine Society · 2026Article
- The Science of Growth Monitoring: Beyond the Basics.Children (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Growth assessment in achondroplasia requires disorder-specific growth charts incorporating sex- and age-specific values. Manual calculations are tedious and subject to error. We present an artificial intelligence (AI)-assisted tool that automates z-score calculations for pediatric patients with achondroplasia. The tool integrates European Lambda-Mu-Sigma (LMS) growth reference data for 9 anthropometric parameters: height, weight, body mass index, head circumference, sitting height, leg length, arm span, relative sitting height, and foot length. It inputs anthropometric measurements and transforms them into sex- and age-specific z-scores and percentiles in real time. Ten pediatric endocrinologists independently calculated anthropometric z-scores for 3 patients with achondroplasia using both the manual growth charts and the automated tool. Time-to-completion and accuracy were recorded and compared. The mean time required by the AI-assisted tool to calculate z-scores for all 9 parameters was significantly shorter than that required by manual calculation (23.4 ± 5.8 vs. 10.1 ± 2.8 min, p < 0.001). The tool demonstrated 100% agreement with manual LMS-based calculations and eliminated human errors to which manual calculations are subject, with significantly higher median absolute z-score deviation compared to the smart tool (0.17 [0.07-0.30] vs. 0 [0-0.01], p < 0.001).
conclusionThis AI-assisted tool provides a user-friendly, accessible, and highly accurate method for automated growth assessment in pediatric achondroplasia. It facilitates efficient clinical and research applications, with potential for future integration into electronic health records and web-based platforms. WHAT IS KNOWN: •Growth monitoring in achondroplasia requires syndrome-specific Lambda-Mu-Sigma based charts. •Manual z-score calculations are time-consuming and subject to error. WHAT IS NEW: •We present an AI-assisted Excel tool that automates z-scores and percentile calculations for 9 anthropometric parameters. •Performance and inter-user reliability testing by 10 pediatric endocrinologists showed significantly improved speed and accuracy over manual methods.
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