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).
Conclusion: This 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.
Keywords: Z-scores; Achondroplasia; Anthropometric measurements; Artificial intelligence (AI); Electronic growth charts; Growth assessment.