Determination of the pubertal growth spurt by artificial intelligence analysis of cervical vertebrae maturation in lateral cephalometric radiographs

Abstract

ObjectiveThis study aimed to assess the performance of a convolutional neural network (CNN) model in detecting the pubertal growth spurt by analyzing cervical vertebrae maturation (CVM) in lateral cephalometric radiographs (LCRs). Study designIn total, LCRs of patients from 6 to 17 years old were selected. Three radiologists independently and blindly classified the maturation stages on the LCRs and defined the difficulty of each classification. Subsequently, the stage and level of difficulty were determined by consensus. LCRs were distributed between training, validation, and test sets across four CNN-based models. The models’ responses were compared with the radiologists’ reference standard, and the architecture with the highest success rate was selected for evaluation. Models were developed using full and cropped LCRs with original and simplified maturation classifications. ResultsIn the simplified classification, the Inception-v3 CNN yielded accuracy of 74% and 75%, with recall and precision values of 61% and 62%, for full and cropped LCRs, respectively. It achieved 61% and 62% success rates with full and cropped LCRs, respectively, reaching 72.7% for easy-to-classify cropped cases. ConclusionOverall, the CNN model demonstrated potential for determining whether the patient's maturation status regarding the pubertal growth spurt through images of the cervical vertebrae. It may be useful as an initial assessment tool or as an aid for optimizing the assessment and treatment decisions of the clinician.