Authors: Dr.Nielsen Santos Pereira

Year: 2025

Description: This study analyzed 3D cone-beam CT images from 60 patients with unilateral cleft lip and palate (UCP) to measure asymmetry of the maxilla. Using a deep learning-based segmentation protocol with manual refinement, the researchers compared the cleft and non-cleft sides. The results showed that the cleft side had significant reductions in maxillary and alveolar volume, length, and height, while the anterior width was increased. Statistical analysis confirmed that defect dimensions were closely related to the variability of the cleft-side maxilla. In conclusion, the study identified hypoplasia in the pyriform aperture and alveolar crest areas, highlighting that defect structures contribute to maxillary asymmetry in UCP patients. DOI: https://doi.org/10.1111/ocr.12482

Authors: Dr.Nielsen Santos Pereira

Year: 2025

Description: This video explores one of the earliest deep learning studies focused on fully automated 3D tooth segmentation and labeling. The authors developed a two-level hierarchical CNN capable of separating teeth and gingiva from dental meshes with remarkable precision — 99.06% accuracy for upper models and 98.79% for lower ones. By combining boundary-aware mesh simplification, graph-based label optimization, and fuzzy refinement, the study presents a clinically useful pipeline for orthodontic CAD systems. It highlights how artificial intelligence can bring automation, precision, and efficiency to digital dentistry. DOI: 10.1109/TVCG.2018.2839685

Authors: Dr.Nielsen Santos Pereira

Year: 2025

Description: This study investigates how panoramic radiographs can be used to predict dysphagia through quantitative radiographic features. Analyzing 77 patients who underwent both panoramic X-rays and videofluorographic swallowing studies, the researchers found that the vertical hyoid bone position was the key indicator associated with dysphagia risk. The study identified a clear cutoff level (AUC = 0.72) — when the hyoid bone lies below the mandibular border line, the likelihood of dysphagia increases significantly. These findings establish a foundation for future AI models designed to automatically assess swallowing risk and enhance patient safety in dental treatment, especially for elderly or frail populations. DOI: https://doi.org/10.3390/ijerph19084529

Authors: Dr.Nielsen Santos Pereira

Year: 2025

Description: This study explores how AI-based feature extraction from dental panoramic radiographs can assist in the early detection of osteoporosis — a condition characterized by decreased bone density and fracture risk. Using 575 radiographs (267 osteoporotic and 308 normal), the researchers compared 13 types of image features, such as Gabor filters, Haar wavelets, and steerable filters, combined with SOM/LVQ and SVM models. The best-performing approach (SOM/LVQ with Gabor features) achieved 92.6% accuracy, 97.1% sensitivity, and 86.4% specificity, showing that texture and edge orientation in mandibular bone regions are reliable indicators of low bone density. The authors conclude that dental panoramic radiographs, routinely acquired in clinics, could become a low-cost AI-assisted screening tool for identifying early signs of osteoporosis — especially useful in populations where DEXA scans are not readily available. DOI: https://doi.org/10.1016/j.cmpb.2019.105301

Authors: Dr.Nielsen Santos Pereira

Year: 2025

Description: This IEEE 2018 study introduces a CNN-based auto-positioning system that detects and corrects patient dental-arch misalignment during rotational panoramic imaging. Using simulated datasets of 5,166 panoramic reconstructions, the algorithm estimates forward–backward deviations within ±20 mm and reconstructs sharper DPRs with minimized anterior blur. Four CNN models (13–15 layers) achieved mean error < 0.5 mm, showing that AI can automatically reposition the dental arch for clearer diagnostic images and reduce the need for retakes. DOI: https://doi.org/10.1109/embc.2018.8512732

Authors: Dr.Nielsen Santos Pereira

Year: 2025

Description: This study, published in BMC Oral Health (2022), presents a comprehensive artificial intelligence framework for automatic tooth detection, numbering, and diagnostic charting using panoramic, periapical, and bitewing radiographs. The proposed deep learning system uses segmentation models (U-Net and U-Net + ResNet-34) to extract tooth, bone, and CEJ masks, assign FDI tooth numbers via multi-scale matching, and arrange full-mouth series (FMS) templates. It achieved a precision and recall of 0.96 (panoramic match) and 0.87 (repository match), outperforming other state-of-the-art models. Importantly, the framework integrates additional diagnostic modules — periodontal bone loss and caries detection — enabling the generation of clinical reports with numbered teeth, which facilitates communication, documentation, and treatment planning. DOI: https://doi.org/10.1186/s12903-022-02514-6

Authors: Dr.Nielsen Santos Pereira

Year: 2025

Description: This 2022 study from Tsinghua University and Peking University School and Hospital of Stomatology proposed a relation-based framework for automated teeth recognition in periapical radiographs. Using 1,250 periapical X-rays with 4,336 labeled teeth, the researchers developed a multi-task CNN integrating a Label Reconstruction technique, a Proposal Correlation Module, and a Teeth Sequence Refinement Module to improve both classification and localization.

Authors: Dr.Nielsen Santos Pereira

Year: 2025

Description: This 2023 retrospective study from Marmara University used clinical intraoral photographs (137 images: 65 healthy, 72 OLP) to train a Google Inception V3 deep-learning model. Using the CranioCatch platform, histopathologically confirmed data yielded 100 % accuracy in classifying normal vs OLP mucosa, demonstrating that AI can analyze standard photographs without radiation to assist clinicians in diagnosing mucosal diseases.

Authors: Dr.Nielsen Santos Pereira

Year: 2025

Description: This study proposes a CNN-based system using transfer learning to detect apical lesions on periapical radiographs. Using adaptive thresholding, advanced image enhancement, and multiple CNN architectures, the model achieved up to 96.21% accuracy with AlexNet. The approach improves clinical decision support and reduces diagnostic workload.

Authors: Dr.Nielsen Santos Pereira

Year: 2025

Description: This study evaluates three deep learning semantic segmentation algorithms, 3DiscNet, U‑Net, and SegNet‑Basic, for automatic segmentation of the TMJ articular disc on MRI. Using 217 MR images, 3DiscNet and SegNet‑Basic achieved superior Dice, sensitivity and PPV, demonstrating the potential of deep learning to support TMD diagnosis.

Authors: Dr.Nielsen Santos Pereira

Year: 2025

Description: This 2022 study from Kastamonu University, Karabuk University, and Ordu University (Turkey) proposed an enhanced Mask R-CNN–based system for automatic tooth segmentation and numbering on bitewing radiographs, using the FDI notation. A total of 1,200 bitewing X-rays were annotated by oral radiologists and divided into 1,000 for training and 200 for testing. The network used ResNet-101 + FPN as backbone and achieved outstanding performance after 400 epochs: - Segmentation: 100 % precision, 97.49 % mAP, and 97.36 % F1-score. - Tooth numbering: 94.35 % precision, 91.51 % mAP, and 93.33 % F1-score. The framework also compared 12 deep learning architectures (ResNet, DenseNet, MobileNet, GoogleNet, HarDNet, etc.), confirming Mask R-CNN as the top performer for both segmentation and numbering. This research demonstrates how AI can support radiographic analysis, enabling accurate identification and classification of individual teeth using standardized FDI numbering.

Authors: Dr.Nielsen Santos Pereira

Year: 2025

Description: Manual landmarking in 3D cephalometry is accurate, but it takes a lot of time , which limits its use in daily practice and research. This study, published in Scientific Reports (2021), proposed an automatic system for 3D cephalometric landmark detection using multi-stage deep reinforcement learning (DRL). The model simulates how experts sequentially identify landmarks and applies that logic to CT images. Tested on a dataset of 28 patients, the system reached a mean error of 1.96 ± 0.78 mm and high detection rates within 2.5 to 4 mm of accuracy. Unlike other methods, it does not require prior segmentation or 3D mesh reconstruction, which makes the process faster and more direct. DOI: https://doi.org/10.1016/j.dental.2025.01.005