ArticleJournal of dental sciences2023
Deep focus approach for accurate bone age estimation from lateral cephalogram.
Article in Journal of dental sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 28 citations in OpenAlex.
- Age Estimation of the Cervical Vertebrae Region Using Deep Learning.Bioengineering (Basel, Switzerland) · 2025Article
- Applications of artificial intelligence in diagnosis and treatment planning of orthodontics: a narrative review.The Saudi dental journal · 2025Review
- Objective Assessment of Bone Age in Orthodontics: A Scoping Review.Journal of pharmacy & bioallied sciences · 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
- Bone age and dental age to assess criminal responsibility: Part I.International journal of legal medicine · 2025Article
- Expert consensus on imaging diagnosis and analysis of early correction of childhood malocclusion.International journal of oral science · 2025Review
- Automated orthodontic diagnosis via self-supervised learning and multi-attribute classification using lateral cephalograms.Biomedical engineering online · 2025Article
- Exploring the Applications of Artificial Intelligence in Dental Image Detection: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Accuracy of Artificial Intelligence for Cervical Vertebral Maturation Assessment-A Systematic Review.Journal of clinical medicine · 2024Review
- A family-centered orthodontic screening approach using a machine learning-based mobile application.Journal of dental sciences · 2024Article
- Determination of cervical vertebral maturation using machine learning in lateral cephalograms.Journal of dental research, dental clinics, dental prospects · 2024Article
- Artificial Intelligence and Its Clinical Applications in Orthodontics: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Article
- Artificial intelligence in forensic medicine and forensic dentistry.The Journal of forensic odonto-stomatology · 2023Review
- A Comprehensive Review of Artificial Intelligence Methods in Bone Age Assessment.Healthcare technology lettersReview
- Cervical Vertebral Maturation Assessment using various Machine Learning techniques on Lateral cephalogram: A systematic literature review.Journal of oral biology and craniofacial researchReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background/purpose: Bone age is a useful indicator of children's growth and development. Recently, the rapid development of deep-learning technique has shown promising results in estimating bone age. This study aimed to devise a deep-learning approach for accurate bone-age estimation by focusing on the cervical vertebrae on lateral cephalograms of growing children using image segmentation. Materials and methods: We included 900 participants, aged 4-18 years, who underwent lateral cephalogram and hand-wrist radiograph on the same day. First, cervical vertebrae segmentation was performed from the lateral cephalogram using DeepLabv3+ architecture. Second, after extracting the region of interest from the segmented image for preprocessing, bone age was estimated through transfer learning using a regression model based on Inception-ResNet-v2 architecture. The dataset was divided into train:test sets in a ratio of 4:1; five-fold cross-validation was performed at each step. Results: The segmentation model possessed average accuracy, intersection over union, and mean boundary F1 scores of 0.956, 0.913, and 0.895, respectively, for the segmentation of cervical vertebrae from lateral cephalogram. The regression model for estimating bone age from segmented cervical vertebrae images yielded average mean absolute error and root mean squared error values of 0.300 and 0.390 years, respectively. The coefficient of determination of the proposed method for the actual and estimated bone age was 0.983. Our method visualized important regions on cervical vertebral images to make a prediction using the gradient-weighted regression activation map technique. Conclusion: Results showed that our proposed method can estimate bone age by lateral cephalogram with sufficiently high accuracy.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.