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Pediatric age estimation from thoracic and abdominal CT scout views using deep learning.

Aydin Demircioğlu1, Kai Nassenstein2, Lale Umutlu2

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This study shows that deep neural networks can accurately predict chronological age in pediatric patients using CT scout views. This method offers a precise tool for assessing physical development in children and adolescents.

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Pediatrics

Background:

  • Age assessment is crucial for evaluating pediatric physical development and maturity.
  • Current methods rely on clinical assessments by pediatric endocrinologists.

Purpose of the Study:

  • To investigate the feasibility of using deep neural networks (DNNs) for age assessment.
  • To determine if CT scout views can be utilized for accurate chronological age prediction in pediatric patients.

Main Methods:

  • A DNN was trained on 1949 retrospective pediatric CT scout views (2013-2018).
  • The model's performance was evaluated on an independent test set of 502 CT scout views (2019-2020).

Main Results:

  • The DNN model achieved a mean absolute error of 1.18 ± 1.14 years on the test data.
  • Statistical analysis confirmed the difference between predicted and actual age was significantly less than 2.0 years (p < 0.001).
  • A high correlation coefficient (R = 0.97) was observed between predicted and chronological ages.

Conclusions:

  • Deep neural networks can accurately assess the chronological age of pediatric patients from CT scout views.
  • This AI-driven approach shows high precision for age assessment in children and adolescents.