In planning a mandibular posterior dental implant, identifying the exact location of the alveolar bone (AB) and mandibular canal (MC) is essential to determine the height and width of the available bone. Cone beam computed tomography (CBCT) is a 3D imaging modality widely used for dental implant planning, which requires a lower radiation dose compared to medical CT and can provide cross-sectional image quality to visualize AB and MC. The radiologist carried out the AB and MC detection processes manually on each section of the CBCT image until the appropriate area was determined for bone measurement. This process is time consuming, and the measurement accuracy depends on the ability and experience of the radiologist. This study proposes an automatic and simultaneous detection system for AB and MC based on 2D grayscale CBCT images, that can simplify and expedite dental implant planning. We introduce Dental-YOLO, an efficient version of YOLOv4 specifically developed to detect AB and MC, with two-scale feature maps at low and high scales. The height and width of the available bone in the implant area were estimated by using the detected bounding box attributes. The AB and MC detection performances using Dental-YOLO reached a mean average precision of 99.46%. The two-way analysis of variance (ANOVA) test showed no difference in the bone height and width measurements produced by the proposed approach and manual measurement by radiologists. Our results suggest that the Dental-YOLO detection system could be helpful for dental implant surgery and presurgical treatment planning.

Context : Several recent studies in dentistry and maxillofacial imaging have concentrated on the mandibular canal (MC) segmentation in digital dental radiographs. In this research domain, deep learning approaches have demonstrated promising outcomes. Objective : This systematic literature review (SLR) aims to comprehensively analyze and synthesize the recent advancements in applying deep learning techniques for MC segmentation in digital dental radiographs. Method : This review encompasses studies published between 2018 and 2023, sourced from reputable databases, including PubMed, ScienceDirect, IEEE Xplore, and Google Scholar. Results : This study identified 30 primary research papers focusing on MC segmentation in digital dental radiographs. The review categorizes papers into two groups based on the digital dental radiograph type. The first group uses a 2D digital dental radiograph from a panoramic radiograph and 2D Cone Beam Computed Tomography (CBCT) scans. The second group uses 3D datasets from volumetric data from CBCT scans. Conclusion : The synthesized knowledge from this review is intended to guide researchers, dentists, and oral surgeons in leveraging deep learning advancements in MC segmentation for oral and maxillofacial surgery. Prior studies have faced challenges, including limited public datasets, variations in MC anatomy, time consumption and observer variability in MC annotation, complexities in deep learning models, and lack of practical implementation. To overcome these challenges, it is suggested that more public datasets be collaboratively created and shared within the research community, focusing on handling anatomy variability, improving digital dental radiograph quality, streamlining annotation processes through automated tools, and simplifying deep learning models for practical implementation.

Aim: We aimed to evaluate the diagnostic performance of the You Only Look Once version 8 (YOLOv8) deep learning model in the automated detection of periodontal bone loss (PBL) on panoramic radiographs and assess its potential as a clinical diagnostic aid. Materials and Methods: A total of 500 annotated panoramic radiographs with PBL were retrospectively collected and randomized into training (n = 400), validation (n = 50), and testing (n = 50) datasets. Image annotation was performed using Roboflow and validated by a radiologist and periodontist. YOLOv8 models of five variants (n, s, m, l, and x) were trained using Google Colab. Model performance was evaluated via confusion matrix-derived metrics: accuracy, precision, recall, specificity, F1-score, and average detection time. False cases were also analyzed. Results: After training and optimization, YOLOv8-s showed the highest mAP result (83.7 ± 1.74%) compared to other variants. During testing, YOLOv8-s demonstrated the highest balance between speed and performance, achieving an accuracy of 90.27% (CI 95% [88.49, 92.05]), precision of 91.27% (CI 95% [89.25, 93.29]), recall of 94.64% (CI 95% [92.99,96.29]), specificity of 80.94% (CI 95% [76.76,85.12]), and an F1-score of 92.98% (CI 95% [91.22, 94.78]) at a 10% confidence threshold. The average detection time per image was 22.6 ms. The most frequent false positives were healthy teeth, while missed detections were mainly mild cases of PBL. Conclusion: The YOLOv8 model effectively detects PBL on panoramic radiographs with high accuracy and speed. It holds significant potential as an assistive tool in clinical diagnosis of periodontitis. Future research should focus on severity classification and multi-center validation to enhance clinical integration.

Putra RH, et al. Imaging Sci Dent. 2023 Sep;53:e29. https://doi.org/10.5624/isd.20230058