Determination of the pubertal growth spurt by artificial intelligence analysis of cervical vertebrae maturation in lateral cephalometric radiographs.
Oral Surg Oral Med Oral Pathol Oral Radiol
; 138(2): 306-315, 2024 08.
Article
in En
| MEDLINE
| ID: mdl-38553310
ABSTRACT
OBJECTIVE:
This 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). STUDYDESIGN:
In total, 600 LCRs of patients from 6 to 17 years old were selected. Three radiologists independently and blindly classified the maturation stages of 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 datasets across 4 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.RESULTS:
In the simplified classification, the Inception-v3 CNN yielded an 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% total success rates with full and cropped LCRs, respectively, reaching 72.7% for easy-to-classify cropped cases.CONCLUSION:
Overall, the CNN model demonstrated potential for determining the 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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Cervical Vertebrae
/
Cephalometry
/
Puberty
Limits:
Adolescent
/
Child
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Female
/
Humans
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Male
Language:
En
Journal:
Oral Surg Oral Med Oral Pathol Oral Radiol
/
Oral surgery, oral medicine, oral pathology and oral radiology
/
Oral surgery, oral medicine, oral pathology and oral radiology (Online)
Year:
2024
Document type:
Article
Country of publication: