ArticleScientific reports2023
Pediatric age estimation from thoracic and abdominal CT scout views using deep learning.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Bone age estimation from chest radiographs using deep neural networks: a proof-of-concept study.Pediatric radiology · 2026Article
- Hierarchical organ aging signatures from routine abdominal CT add incremental disease risk stratification beyond blood biomarkers.medRxiv : the preprint server for health sciences · 2026Article
- Predicting pediatric age from chest X-rays using deep learning: a novel approach.Insights into imaging · 2025Article
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Authors and funding
3 authors.
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Abstract
Age assessment is regularly used in clinical routine by pediatric endocrinologists to determine the physical development or maturity of children and adolescents. Our study investigates whether age assessment can be performed using CT scout views from thoracic and abdominal CT scans using a deep neural network. Hence, we retrospectively collected 1949 CT scout views from pediatric patients (acquired between January 2013 and December 2018) to train a deep neural network to predict the chronological age from CT scout views. The network was then evaluated on an independent test set of 502 CT scout views (acquired between January 2019 and July 2020). The trained model showed a mean absolute error of 1.18 ± 1.14 years on the test data set. A one-sided t-test to determine whether the difference between the predicted and actual chronological age was less than 2.0 years was statistically highly significant (p < 0.001). In addition, the correlation coefficient was very high (R = 0.97). In conclusion, the chronological age of pediatric patients can be assessed with high accuracy from CT scout views using a deep neural network.
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