Dental education involves teaching and assessing the acquisition of verifiable domains that require superior psychomotor, communication, and cognitive skills. Evolving technologies and methods of a...

Objectives: This research aims to assess the test–retest reliability, the face, content and known groups validity, and responsiveness to change, of OHIP‐TMDs, a 22‐item TMDs‐specific version of the Oral Health Impact Profile (OHIP).Methods: Test–retest reliability – A group of patients with TMDs (n = 20) was administered OHIP‐TMDs twice before initial consultation with a 2‐week interval. Face and content validity – Content validity index assessments were undertaken with professionals and patients. Known groups validity – Participants (n = 76) with confirmed Axis 1 RDC/TMD diagnoses completed OHIP‐TMDs prior to TMDs treatment. Their responses were compared, using inferential statistics, with those of age‐ and gender‐matched controls. Responsiveness to change – Using the same 76 participants, a comparison was made of OHIP‐TMDs with OHIP‐49 (order of administration randomized) both at baseline and 3 months after starting treatment.Results: OHIP‐TMDs showed good test–retest reliability ICC [2,1] 0.805 (95% CI: 0.565, 0.918); good face and content validity; significant differences (P < 0.001) between controls and participants demonstrating known groups validity. Its responsiveness to change was similar to OHIP‐49.Conclusions: OHIP‐TMDs is an appropriate biopsychosocial, patient‐centred, outcome measure for assessing QOL in patients with TMDs. It is less than half the length of OHIP‐49 and contains proportionately more items relevant to TMDs.

Objective Dental implants are considered the optimum solution to replace missing teeth and restore the mouth's function and aesthetics. Surgical planning of the implant position is critical to avoid damage to vital anatomical structures; however, the manual measurement of the edentulous (toothless) bone on cone beam computed tomography (CBCT) images is time-consuming and is subject to human error. An automated process has the potential to reduce human errors and save time and costs. This study developed an artificial intelligence (AI) solution to identify and delineate edentulous alveolar bone on CBCT images before implant placement. Materials and Methods After obtaining the ethical approval, CBCT images were extracted from the database of the University Dental Hospital Sharjah based on predefined selection criteria. Manual segmentation of the edentulous span was done by three operators using ITK-SNAP software. A supervised machine learning approach was undertaken to develop a segmentation model on a “U-Net” convolutional neural network (CNN) in the Medical Open Network for Artificial Intelligence (MONAI) framework. Out of the 43 labeled cases, 33 were utilized to train the model, and 10 were used for testing the model's performance. Statistical Analysis The degree of 3D spatial overlap between the segmentation made by human investigators and the model's segmentation was measured by the dice similarity coefficient (DSC). Results The sample consisted mainly of lower molars and premolars. DSC yielded an average value of 0.89 for training and 0.78 for testing. Unilateral edentulous areas, comprising 75% of the sample, resulted in a better DSC (0.91) than bilateral cases (0.73). Conclusion Segmentation of the edentulous spans on CBCT images was successfully conducted by machine learning with good accuracy compared to manual segmentation. Unlike traditional AI object detection models that identify objects present in the image, this model identifies missing objects. Finally, challenges in data collection and labeling are discussed, together with an outlook at the prospective stages of a larger project for a complete AI solution for automated implant planning.

Blockchain technology in the healthcare industry has potential to enable enhanced privacy, increased security, and an interoperable data record. Blockchain technology is being implemented in dental care systems to store and share medical information, improve insurance claims, and provide innovative dental data ledgers. Because the healthcare sector is a large and ever-growing industry, the use of blockchain technology would have many benefits. To improve dental care delivery, researchers advocate using blockchain technology and smart contracts due to their numerous advantages. In this research, we concentrate on blockchain-based dental care systems. In particular, we examine the current research literature, pinpoint issues with existing dental care systems, and consider how blockchain technology may be used to address these issues. Finally, the limitations of the proposed blockchain-based dental care systems are discussed which may be regarded as open issues.