Preinterventional Third-Molar Assessment Using Robust Machine Learning

Validity and Utility of a Risk Prediction Model for Wound Infection After Lower Third Molar Surgery

OBJECTIVES

To externally validate a clinical prediction model for surgical site infection (SSI) after lower third molar (L3M) surgery and evaluate its clinical usefulness.

METHODS

We conducted a retrospective cohort study of patients who underwent L3M surgery at Hokkaido University Hospital. The study was designed to evaluate the historical and methodological transportability. Clinical usefulness was evaluated using decision curve analysis on the data of the non-antibiotic-treated patients.

RESULTS

We obtained 2543 validation cohorts from April 2020 to March 2023, and 640 non-antibiotic cohorts from July 2010 to September 2023. The incidences of SSI after L3M surgery were 5.3% (135/2543) and 7.7% (49/640) in the validation and non-antibiotic cohorts, respectively. The discrimination ability of the prediction model was acceptable for the external validation cohort (c-statistic: 0.67; 95% CI: 0.62-0.71) and adequate for the non-antibiotic cohort (c-statistic: 0.72; 95% CI: 0.63-0.79). In both cohorts, the model showed excellent calibration between the observed and predicted probabilities. Decision curve analysis showed increased net benefit across a range of meaningful risk thresholds.

CONCLUSION

A simple risk prediction model for SSI after L3M surgery demonstrated clinical transportability and usefulness. This model may help surgeons/clinicians determine the appropriateness of prophylactic antibiotics administration for patients in L3M surgery.

LMCD-OR: a large-scale, multilevel categorized diagnostic dataset for oral radiography

In recent years, digital dentistry has increasingly utilized advanced image analysis techniques, such as image classification and disease diagnosis, to improve clinical outcomes. Despite these advances, the lack of comprehensive benchmark datasets is a significant barrier. To address this gap, our research team develop LMCD-OR, a substantial collection of oral radiograph images designed to support extensive artificial intelligence (AI)-driven diagnostics. LMCD-OR comprises 3,818 digital imaging and communications in medicine (DICOM) oral X-ray images from local medical institutions that are meticulously annotated to provide broad category information for both primary dental outpatient services and detailed secondary disease diagnoses. This dataset is engineered to train and validate multiclassification models to improve the precision and scope of oral disease diagnostics. To ensure robust dataset validation, we employ four cutting-edge visual neural network classification models as benchmarks. These models are tested against rigorous performance metrics, demonstrating the ability of the dataset to support advanced image classification and disease diagnosis tasks. LMCD-OR is publicly available at http://dentaldataset.zeroacademy.net .

Prediction of extraction difficulty for impacted maxillary third molars with deep learning approach

OBJECTIVE

The aim of this study is to determine if a deep learning (DL) model can predict the surgical difficulty for impacted maxillary third molar tooth using panoramic images before surgery.

MATERIALS AND METHODS

The dataset consists of 708 panoramic radiographs of the patients who applied to the Oral and Maxillofacial Surgery Clinic for various reasons. Each maxillary third molar difficulty was scored based on dept (V), angulation (H), relation with maxillary sinus (S), and relation with ramus (R) on panoramic images. The YoloV5x architecture was used to perform automatic segmentation and classification. To prevent re-testing of images, participate in the training, the data set was subdivided as: 80 % training, 10 % validation, and 10 % test group.

RESULTS

Impacted Upper Third Molar Segmentation model showed best success on sensitivity, precision and F1 score with 0,9705, 0,9428 and 0,9565, respectively. S-model had a lesser sensitivity, precision and F1 score than the other models with 0,8974, 0,6194, 0,7329, respectively.

CONCLUSION

The results showed that the proposed DL model could be effective for predicting the surgical difficulty of an impacted maxillary third molar tooth using panoramic radiographs and this approach might help as a decision support mechanism for the clinicians in peri‑surgical period.

Imaging in Third Molar Surgery: A Clinical Update

Third molar surgery is one of the most common surgical procedures performed in oral and maxillofacial surgery. Considering the patient's young age and the often-elective nature of the procedure, a comprehensive preoperative evaluation of the surgical site, relying heavily on preoperative imaging, is key to providing accurate diagnostic work-up, evidence-based clinical decision making, and, when appropriate, indication-specific surgical planning. Given the rapid developments of dental imaging in the field, the aim of this article is to provide a comprehensive, up-to-date clinical overview of various imaging techniques related to perioperative imaging in third molar surgery, ranging from panoramic radiography to emerging technologies, such as photon-counting computed tomography and magnetic resonance imaging. Each modality's advantages, limitations, and recent improvements are evaluated, highlighting their role in treatment planning, complication prevention, and postoperative follow-ups. The integration of recent technological advances, including artificial intelligence and machine learning in biomedical imaging, coupled with a thorough preoperative clinical evaluation, marks another step towards personalized dentistry in high-risk third molar surgery. This approach enables minimally invasive surgical approaches while reducing inefficiencies and risks by incorporating additional imaging modality- and patient-specific parameters, potentially facilitating and improving patient management.