The aim was to systematically synthesize the current research and influence of artificial intelligence (AI) models on temporomandibular joint (TMJ) osteoarthritis (OA) diagnosis using cone-beam computed tomography (CBCT) or panoramic radiography. Seven databases (PubMed, Embase, Scopus, Web of Science, LILACS, ProQuest, and SpringerLink) were searched for TMJ OA and AI articles. We used QUADAS-2 to assess the risk of bias, while with MI-CLAIM we checked the minimum information about clinical artificial intelligence modeling. Two hundred and three records were identified, out of which seven were included, amounting to 10,077 TMJ images. Three studies focused on the diagnosis of TMJ OA using panoramic radiography with various transfer learning models (ResNet model) on which the meta-analysis was performed. The pooled sensitivity was 0.76 (95% CI 0.35–0.95) and the specificity was 0.79 (95% CI 0.75–0.83). The other studies investigated the 3D shape of the condyle and disease classification observed on CBCT images, as well as the numerous radiomics features that can be combined with clinical and proteomic data to investigate the most effective models and promising features for the diagnosis of TMJ OA. The accuracy of the methods was nearly equivalent; it was higher when the indeterminate diagnosis was excluded or when fine-tuning was used.

Background/Objectives: The integration of AI into dentistry holds promise for improving diagnostic workflows, particularly in the detection of dental pathologies and pre-radiotherapy screening for head and neck cancer patients. This study aimed to develop and validate an AI model for detecting various dental conditions, with a focus on identifying teeth at risk prior to radiotherapy. Methods: A YOLOv8 model was trained on a dataset of 1628 annotated panoramic radiographs and externally validated on 180 radiographs from multiple centers. The model was designed to detect a variety of dental conditions, including periapical lesions, impacted teeth, root fragments, prosthetic restorations, and orthodontic devices. Results: The model showed strong performance in detecting implants, endodontic treatments, and surgical devices, with precision and recall values exceeding 0.8 for several conditions. However, performance declined during external validation, highlighting the need for improvements in generalizability. Conclusions: YOLOv8 demonstrated robust detection capabilities for several dental conditions, especially in training data. However, further refinement is needed to enhance generalizability in external datasets and improve performance for conditions like periapical lesions and bone loss.

Dental cavities are common oral diseases that can lead to pain, discomfort, and eventually, tooth loss. Early detection and treatment of cavities can prevent these negative consequences. We propose CariSeg, an intelligent system composed of four neural networks that result in the detection of cavities in dental X-rays with 99.42% accuracy. The first model of CariSeg, trained using the U-Net architecture, segments the area of interest, the teeth, and crops the radiograph around it. The next component segments the carious lesions and it is an ensemble composed of three architectures: U-Net, Feature Pyramid Network, and DeeplabV3. For tooth identification two merged datasets were used: The Tufts Dental Database consisting of 1000 panoramic radiography images and another dataset of 116 anonymized panoramic X-rays, taken at Noor Medical Imaging Center, Qom. For carious lesion segmentation, a dataset consisting of 150 panoramic X-ray images was acquired from the Department of Oral and Maxillofacial Surgery and Radiology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca. The experiments demonstrate that our approach results in 99.42% accuracy and a mean 68.2% Dice coefficient. AI helps in detecting carious lesions by analyzing dental X-rays and identifying cavities that might be missed by human observers, leading to earlier detection and treatment of cavities and resulting in better oral health outcomes.

Background and Objectives: This research was aimed at constructing a complete automated temporomandibular joint disc position identification system that could assist with magnetic resonance imaging disc displacement diagnosis on oblique sagittal and oblique coronal images. Materials and Methods: The study included fifty subjects with magnetic resonance imaging scans of the temporomandibular joint. Oblique sagittal and coronal sections of the magnetic resonance imaging scans were analyzed. Investigations were performed on the right and left coronal images with a closed mouth, as well as right and left sagittal images with closed and open mouths. Three hundred sagittal and coronal images were employed to train the artificial intelligence algorithm. Results: The accuracy ratio of the completely computerized articular disc identification method was 81%. Conclusions: An automated and accurate evaluation of temporomandibular joint disc position was developed by using both oblique sagittal and oblique coronal magnetic resonance imaging images.

Background/Objectives: Thermal injury during implant osteotomy is a recognized complication of guided surgery. Closed guide architectures and metallic sleeves restrict coolant access to the drill–bone interface, reducing the effectiveness of conventional external irrigation. This study aimed to describe the design and fabrication of a 3D-printed surgical guide system with integrated internal irrigation channels and to evaluate its technical and clinical feasibility in guided implant surgery. Methods: The system was developed by integrating CBCT and IOS data into BlueSky Bio planning software. Irrigation channels were generated by CAD subtraction. Guides were fabricated by additive manufacturing—digital light processing (DLP) in biocompatible Asiga DentaGuide resin and, in one case, selective laser melting (SLM) in Mediloy metal—with metal auxiliary components incorporated where required. Channel patency was confirmed by flow equivalence testing and visual inspection. The design was evaluated for compatibility with the BioHorizons Pro Surgical Guide Kit and the Ticare Fidelis Kit. Clinical feasibility was assessed in two cases: maxillary All-on-6 rehabilitation and immediate implant placement in the esthetic zone. Results: Both guides were successfully fabricated with confirmed patent irrigation channels. Guide seating was verified through occlusal fenestrations and secured with fixation pins. Irrigation flow remained unobstructed throughout all osteotomy steps, and no guide-related or irrigation-related complications were observed. The system was integrated into standard drilling protocols without modification for either implant platform. Conclusions: This proof-of-concept study demonstrates the technical feasibility of an internally irrigated surgical guide across two implant systems and distinct clinical scenarios. The proposed design was successfully integrated into standard guided surgery workflows and maintained functional irrigation. Further in vitro thermal studies and prospective clinical investigations are required to evaluate its effect on temperature control.