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An AI-assisted tool for automated growth monitoring in pediatric achondroplasia
Eyal Cohen-Sela1,2, Yael Lebenthal3,4, Avivit Brener1,2
1The Institute of Pediatric Endocrinology, Diabetes and Metabolism, Dana-Dwek Children's Hospital, Tel Aviv Sourasky Medical Center, 64239-06, Tel Aviv, Israel.
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).
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.
