Trial reportInternational journal of environmental research and public health2022
Enhanced Tooth Region Detection Using Pretrained Deep Learning Models.
Trial report in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of artificial intelligence in the field of oral and maxillofacial pathology: a systematic review and meta-analysis.BMC oral health · 2024Pooled it
- Application of a novel age estimation model based on permanent maxillary canine morphometric features in a Japanese population.Oral radiology · 2026Article
- A narrative review of artificial intelligence in dental imaging: from dataset design to clinical translation.Oral radiology · 2026Review
- A Pilot Study of the Diagnosis of Oral Cancer Through the Development of an AI Application.Dentistry journal · 2026Article
- Comparison of artificial intelligence methods with manual measurements for mandibular bucco-lingual bone width assessment using cone-beam computed tomography images.Oral radiology · 2026Article
- The Use of Artificial Intelligence in Planning Dental Implant Procedures: A Systematic Review.Dentistry journal · 2026Review
- Application of Artificial Intelligence in Detecting Dental Anomalies: Current Models, Imaging Modalities, and Future Directions.Health science reports · 2026Article
- A Deep Metric Learning and Multimodal Gated Fusion Framework for AI-Driven Risk Assessment of Lingual Plate Perforation and Mandibular Canal Injury in Posterior Mandible Implants.International journal of dentistry · 2026Article
- Diagnostic Support in Dentistry Through Artificial Intelligence: A Systematic Review.Bioengineering (Basel, Switzerland) · 2025Review
- Artificial Intelligence Tools in Dentistry: A Systematic Review on Their Application and Outcomes.Cureus · 2025Review
- Artificial intelligence (AI) in restorative dentistry: current trends and future prospects.BMC oral health · 2025Review
- Deep learning algorithms for detecting fractured instruments in root canals.BMC oral health · 2025Article
- A Comprehensive Analysis of the Radiographic Characteristics and Bilateral Symmetry of the Mental Foramen.Clinical and experimental dental research · 2025Article
- Assessment of using transfer learning with different classifiers in hypodontia diagnosis.BMC oral health · 2025Article
- Automated classification of midpalatal suture maturation using 2D convolutional neural networks on CBCT scans.Frontiers in dental medicine · 2025Article
- Race to the Moon or the Bottom? Applications, Performance, and Ethical Considerations of Artificial Intelligence in Prosthodontics and Implant Dentistry.Dentistry journal · 2024Review
- Using AI in Optimizing Oral and Dental Diagnoses-A Narrative Review.Diagnostics (Basel, Switzerland) · 2024Review
- LMCD-OR: a large-scale, multilevel categorized diagnostic dataset for oral radiography.Journal of translational medicine · 2024Article
- Artificial intelligent-driven decision-making for automating root fracture detection in periapical radiographs.BDJ open · 2024Article
- Deep learning-based approach for 3D bone segmentation and prediction of missing tooth region for dental implant planning.Scientific reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid development of artificial intelligence (AI) has led to the emergence of many new technologies in the healthcare industry. In dentistry, the patient's panoramic radiographic or cone beam computed tomography (CBCT) images are used for implant placement planning to find the correct implant position and eliminate surgical risks. This study aims to develop a deep learning-based model that detects missing teeth's position on a dataset segmented from CBCT images. Five hundred CBCT images were included in this study. After preprocessing, the datasets were randomized and divided into 70% training, 20% validation, and 10% test data. A total of six pretrained convolutional neural network (CNN) models were used in this study, which includes AlexNet, VGG16, VGG19, ResNet50, DenseNet169, and MobileNetV3. In addition, the proposed models were tested with/without applying the segmentation technique. Regarding the normal teeth class, the performance of the proposed pretrained DL models in terms of precision was above 0.90. Moreover, the experimental results showed the superiority of DenseNet169 with a precision of 0.98. In addition, other models such as MobileNetV3, VGG19, ResNet50, VGG16, and AlexNet obtained a precision of 0.95, 0.94, 0.94, 0.93, and 0.92, respectively. The DenseNet169 model performed well at the different stages of CBCT-based detection and classification with a segmentation accuracy of 93.3% and classification of missing tooth regions with an accuracy of 89%. As a result, the use of this model may represent a promising time-saving tool serving dental implantologists with a significant step toward automated dental implant planning.
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.