Facial and mandibular landmark tracking with habitual head posture estimation using linear and fiducial markers.

This study compared the accuracy of facial landmark measurements using deep learning-based fiducial marker (FM) and arbitrary width reference (AWR) approaches. It quantitatively analysed mandibular hard and soft tissue lateral excursions and head tilting from consumer camera footage of 37 participants. A custom deep learning system recognised facial landmarks for measuring head tilt and mandibular lateral excursions. Circular fiducial markers (FM) and inter-zygion measurements (AWR) were validated against physical measurements using electrognathography and electronic rulers. Results showed notable differences in lower and mid-face estimations for both FM and AWR compared to physical measurements. The study also demonstrated the comparability of both approaches in assessing lateral movement, though fiducial markers exhibited variability in mid-face and lower face parameter assessments. Regardless of the technique applied, hard tissue movement was typically seen to be 30% less than soft tissue among the participants. Additionally, a significant number of participants consistently displayed a 5 to 10° head tilt.

Clinical Annotation and Segmentation Tool (CAST) Implementation for Dental Diagnostics.

Purpose This study aims to document the early stages of development of an unsupervised, deep learning-based clinical annotation and segmentation tool (CAST) capable of isolating clinically significant teeth in both intraoral photographs and their corresponding oral radiographs. Methods The dataset consisted of 172 intraoral photographs and 424 dental radiographs, manually annotated by two operators, augmented to yield 6258 images for training, 183 for validation, and 98 for testing. The training involved the use of an object detection model ('YOLOv8') combined with a feature extraction system ('Segment Anything Model'). This combination enabled the auto-annotation and segmentation of tooth-related features and lesions in both types of images without operator intervention. Outputs were further processed using a data relabelling tool ('X-AnyLabeling') enabling the option to manually reannotate erroneous data outputs through reinforcement learning. Results The trained object detection model achieved a mean average precision (mAP) of 77.4%, with precision and recall rates of 75.0% and 72.1%, respectively. The model was able to segment features from oral images annotated by polygonal boundaries better than radiological images annotated using bounding boxes. Conclusion The development of the auto-annotation and segmentation tool showed initial promise in automating the image labelling and segmentation process for intraoral images and radiographs. Further work is required to address the limitations.

Experimental validation of computer-vision methods for the successful detection of endodontic treatment obturation and progression from noisy radiographs.

PURPOSE: (1) To evaluate the effects of denoising and data balancing on deep learning to detect endodontic treatment outcomes from radiographs. (2) To develop and train a deep-learning model and classifier to predict obturation quality from radiomics. METHODS: The study conformed to the STARD 2015 and MI-CLAIMS 2021 guidelines. 250 deidentified dental radiographs were collected and augmented to produce 2226 images. The dataset was classified according to endodontic treatment outcomes following a set of customized criteria. The dataset was denoised and balanced, and processed with YOLOv5s, YOLOv5x, and YOLOv7 models of real-time deep-learning computer vision. Diagnostic test parameters such as sensitivity (Sn), specificity (Sp), accuracy (Ac), precision, recall, mean average precision (mAP), and confidence were evaluated. RESULTS: Overall accuracy for all the deep-learning models was above 85%. Imbalanced datasets with noise removal led to YOLOv5x's prediction accuracy to drop to 72%, while balancing and noise removal led to all three models performing at over 95% accuracy. mAP saw an improvement from 52 to 92% following balancing and denoising. CONCLUSION: The current study of computer vision applied to radiomic datasets successfully classified endodontic treatment obturation and mishaps according to a custom progressive classification system and serves as a foundation to larger research on the subject matter.

Clinical Response of 277 Patients with Spinal Cord Injury to Stem Cell Therapy in Iraq

BACKGROUND AND OBJECTIVES: Spinal cord injury is a common neurological problem secondary to car accidents, war injuries and other causes, it may lead to varying degrees of neurological disablement, and apart from physiotherapy there is no available treatment to regain neurological function loss. Our aim is to find a new method using autologous hematopoietic stem cells to gain some of the neurologic functions lost after spinal cord injury. METHODS AND RESULTS: 277 patients suffering from spinal cord injury were submitted to an intrathecally treatment with peripheral stem cells. The cells were harvested from the peripheral blood after a treatment with G-CSF and then concentrated to 4~6 ml. 43% of the patients improved; ASIA score shifted from A to B in 88 and from A to C in 32. The best results were achieved in patients treated within one year from the injury. CONCLUSIONS: Since mesenchymal cells increase in the peripheral blood after G-CSF stimulation, a peripheral blood harvest seems easier and cheaper than mesenchymal cell cultivation prior to injection. It seems reasonable treatment for spinal cord injury.