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Evaluation of height prediction models: from traditional methods to artificial intelligence



Traditional methods for predicting adult height (AHP) rely on manual readings of bone age (BA). However, the incorporation of artificial intelligence has recently improved the accuracy of BA readings and their incorporation into AHP models.


This study aimed to identify the AHP model that fits the current average height for adults in Mexico. Using a cross-sectional design, the study included 1173 participants (5–18 yr). BA readings were done by two experts (manually) and with an automated method (BoneXpert®). AHP was carried out using both traditional and automated methods. The best AHP model was the one that was closest to the population mean.


All models overestimated the population mean (males: 0.7–6.7 cm, females: 0.9–3.7 cm). The AHP models with the smallest difference were BoneXpert for males and Bayley & Pinneau for females. However, the manual readings of BA showed significant interobserver variability (up to 43% of predictions between observers exceeded 5 cm using the Bayley & Pinneau method).


Traditional AHP models relying on manual BA readings have high interobserver variability. Therefore, BoneXpert is the most reliable option, reducing such variability and providing AHP models that remain close to the mean population height.


  • Traditional models for predicting adult height often result in overestimated height predictions.

  • The manual reading of bone age is prone to interobserver variability, which can introduce significant biases in the prediction of adult height.

  • The BoneXpert method minimizes the variability associated with traditional methods and demonstrates consistent results in relation to the average height of the population.

  • This study is the first to assess adult height prediction models specifically in the current generations of Mexican children.

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Fig. 1: Agreement between manual BA readings using the GP and TW2 methods.
Fig. 2: Predictions obtained using different AHP models, by sex.
Fig. 3: AHPs obtained using each of the models, by sex and CA.

Data availability

The data that support the results of this study are not publicly available because they contain information that could compromise the privacy of the pediatric participants who participated in the research. For more information, you can contact the corresponding author: A.L.M-L. with e-mail:


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We thank Dr. Thodberg for the support with licenses for the use of BoneXpert software and the Consejo Nacional de Ciencia y Tecnología (CONACYT) for the scholarship awarded to the master’s student Ana Gabriela Chávez Vázquez.


The study was supported by Mexican Federal Funds (HIM/2017/058 SSA 1344). The funding source was not involved in the research and/or preparation of the paper.

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Authors and Affiliations



C-V. and M-L. conceptualized and designed the study, drafted the initial paper, designed the data collection instruments, collected data, carried out the initial analyses and drafted the initial paper. K-K. conceptualized and designed the study and carried out the statical analyses. G-N., L-G. and S-C. Loyo designed the data collection instruments and collected data. All authors critically reviewed the paper for important intellectual content and approved the final paper as submitted and agree to be accountable for all aspects of the work.

Corresponding author

Correspondence to América L. Miranda-Lora.

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Chávez-Vázquez, A.G., Klünder-Klünder, M., Garibay-Nieto, N.G. et al. Evaluation of height prediction models: from traditional methods to artificial intelligence. Pediatr Res (2023).

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