ArticleBMC oral health2023
The psc-CVM assessment system: A three-stage type system for CVM assessment based on deep learning.
Article in BMC oral health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 3 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Radiographic choice for skeletal maturation assessment: a systematic review of Cervical Vertebrae Method and Middle Phalanx Method.Oral radiology · 2026Pooled it
- Artificial intelligence for predicting the pubertal growth spurt using cephalometric and hand-wrist radiographs: a systematic review and meta-analysis.BMC oral health · 2026Pooled it
- Performance of artificial intelligence on cervical vertebral maturation assessment: a systematic review and meta-analysis.BMC oral health · 2025Pooled it
- Diagnostic Accuracy of a Machine Learning Model for Cervical Vertebra-Based Skeletal Maturity Assessment in Pediatric and Adolescent Patients Using Cephalometric Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- Single-model deep learning approach for simultaneous cervical vertebral maturation staging and skeletal jaw relationship on lateral cephalograms using YOLOv8 and CNN.BMC oral health · 2026Article
- Comparative performance analysis of AI-based large language models in assessing cervical vertebral maturation stages on lateral cephalometric radiographs.BMC oral health · 2026Article
- The Role of Artificial Intelligence in Orthodontics for Determining Skeletal Age Based on Cervical Vertebra Maturation Degree: A Comprehensive Review.Health science reports · 2025Article
- Applications of artificial intelligence in diagnosis and treatment planning of orthodontics: a narrative review.The Saudi dental journal · 2025Review
- Improving cervical maturation degree classification accuracy using a multi-stage deep learning approach.Imaging science in dentistry · 2025Article
- Growth Prediction in Orthodontics: ASystematic Review of Past Methods up to Artificial Intelligence.Children (Basel, Switzerland) · 2025Review
- Deep learning based quantitative cervical vertebral maturation analysis.Head & face medicine · 2025Article
- Deep learning approaches for quantitative and qualitative assessment of cervical vertebral maturation staging systems.PloS one · 2025Article
- Accuracy of Artificial Intelligence for Cervical Vertebral Maturation Assessment-A Systematic Review.Journal of clinical medicine · 2024Review
- Mapping an intelligent algorithm for predicting female adolescents' cervical vertebrae maturation stage with high recall and accuracy.Progress in orthodontics · 2024Article
- The Utility of Cervical Vertebral Maturation Method for Staging Skeletal Growth and Curve Progression in Patients with Adolescent Idiopathic Scoliosis.JB & JS open accessArticle
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15 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundMany scholars have proven cervical vertebral maturation (CVM) method can predict the growth and development and assist in choosing the best time for treatment. However, assessing CVM is a complex process. The experience and seniority of the clinicians have an enormous impact on judgment. This study aims to establish a fully automated, high-accuracy CVM assessment system called the psc-CVM assessment system, based on deep learning, to provide valuable reference information for the growth period determination.
methodsThis study used 10,200 lateral cephalograms as the data set (7111 in train set, 1544 in validation set and 1545 in test set) to train the system. The psc-CVM assessment system is designed as three parts with different roles, each operating in a specific order. 1) Position Network for locating the position of cervical vertebrae; 2) Shape Recognition Network for recognizing and extracting the shapes of cervical vertebrae; and 3) CVM Assessment Network for assessing CVM according to the shapes of cervical vertebrae. Statistical analysis was conducted to detect the performance of the system and the agreement of CVM assessment between the system and the expert panel. Heat maps were analyzed to understand better what the system had learned. The area of the third (C3), fourth (C4) cervical vertebrae and the lower edge of second (C2) cervical vertebrae were activated when the system was assessing the images.
resultsThe system has achieved good performance for CVM assessment with an average AUC (the area under the curve) of 0.94 and total accuracy of 70.42%, as evaluated on the test set. The Cohen's Kappa between the system and the expert panel is 0.645. The weighted Kappa between the system and the expert panel is 0.844. The overall ICC between the psc-CVM assessment system and the expert panel was 0.946. The F1 score rank for the psc-CVM assessment system was: CVS (cervical vertebral maturation stage) 6 > CVS1 > CVS4 > CVS5 > CVS3 > CVS2.
conclusionsThe results showed that the psc-CVM assessment system achieved high accuracy in CVM assessment. The system in this study was significantly consistent with expert panels in CVM assessment, indicating that the system can be used as an efficient, accurate, and stable diagnostic aid to provide a clinical aid for determining growth and developmental stages by CVM.
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