ArticleJournal of personalized medicine2021
Influence of the Depth of the Convolutional Neural Networks on an Artificial Intelligence Model for Diagnosis of Orthognathic Surgery.
Article in Journal of personalized medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
37 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Parameter-level comparison of artificial intelligence and manual cephalometric measurements: a systematic review and meta-analysis.Frontiers in dental medicine · 2026Pooled it
- Application and performance of artificial intelligence-based models in the detection, segmentation and classification of periapical lesions: a systematic review.Frontiers in dental medicine · 2025Pooled it
- Artificial-Intelligence-Based Cephalometric Landmark Detection in Lateral Cephalograms.Journal of clinical medicine · 2026Article
- Deep Learning-Based Prediction of Orthognathic Surgery Need in Skeletal Class II Patients Using Facial Profile Images: An Artificial Intelligence Study.Journal of imaging informatics in medicine · 2026Article
- Can classification strategies improve automated cervical vertebral maturation staging? A comparative study.Scientific reports · 2026Article
- An interpretable machine learning model based on habitat radiomics combined with deep learning for predicting the WHO/ISUP grade of patients with clear cell renal cell carcinoma.BMC medical imaging · 2026Article
- Clinical decisions in Orthodontics using x-ray-based images and artificial intelligence approaches: a scoping review.Dental press journal of orthodontics · 2026Article
- Quantitative Evaluation of Skeletal, Dental, and Soft Tissue Changes After Orthognathic Surgery: A Cephalometric and Statistical Analysis.Journal of clinical medicine · 2025Article
- Review
- Deep Spectrogram Learning for Gunshot Classification: A Comparative Study of CNN Architectures and Time-Frequency Representations.Journal of imaging · 2025Article
- Integrating Artificial Intelligence into Orthodontic Education: A Systematic Review and Meta-Analysis of Clinical Teaching Application.Journal of clinical medicine · 2025Review
- Innovative Aesthetic and Functional Orthodontic Planning with Hard and Soft Tissue Analyses.Journal of clinical medicine · 2025Article
- Foundation models and intelligent decision-making: Progress, challenges, and perspectives.Innovation (Cambridge (Mass.)) · 2025Review
- Artificial Intelligence Tools in Dentistry: A Systematic Review on Their Application and Outcomes.Cureus · 2025Review
- Integrating Machine Learning and Deep Learning for Predicting Non-Surgical Root Canal Treatment Outcomes Using Two-Dimensional Periapical Radiographs.Diagnostics (Basel, Switzerland) · 2025Article
- Federated Learning-Based CNN Models for Orthodontic Skeletal Classification and Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
- Associations of Digital Measurements: Analysis of Orthopantomography Versus Lateral Cephalograms for Evaluation of Facial Asymmetry.Journal of clinical medicine · 2025Article
- Application of Artificial Intelligence in Orthognathic Surgery: A Scoping Review.BioMed research international · 2025Article
- Artificial Intelligence in Surgery: A Systematic Review of Use and Validation.Journal of clinical medicine · 2024Review
- Reliability and accuracy of Artificial intelligence-based software for cephalometric diagnosis. A diagnostic study.BMC oral health · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of this study was to investigate the relationship between image patterns in cephalometric radiographs and the diagnosis of orthognathic surgery and propose a method to improve the accuracy of predictive models according to the depth of the neural networks. The study included 640 and 320 patients requiring non-surgical and surgical orthodontic treatments, respectively. The data of 150 patients were exclusively classified as a test set. The data of the remaining 810 patients were split into five groups and a five-fold cross-validation was performed. The convolutional neural network models used were ResNet-18, 34, 50, and 101. The number in the model name represents the difference in the depth of the blocks that constitute the model. The accuracy, sensitivity, and specificity of each model were estimated and compared. The average success rate in the test set for the ResNet-18, 34, 50, and 101 was 93.80%, 93.60%, 91.13%, and 91.33%, respectively. In screening, ResNet-18 had the best performance with an area under the curve of 0.979, followed by ResNets-34, 50, and 101 at 0.974, 0.945, and 0.944, respectively. This study suggests the required characteristics of the structure of an artificial intelligence model for decision-making based on medical images.
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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.