Multi-class segmentation of temporomandibular joint using ensemble deep learning.

Temporomandibular joint disorders are prevalent causes of orofacial discomfort. Diagnosis predominantly relies on assessing the configuration and positions of temporomandibular joint components in magnetic resonance images. The complex anatomy of the temporomandibular joint, coupled with the variability in magnetic resonance image quality, often hinders an accurate diagnosis. To surmount this challenge, we developed deep learning models tailored to the automatic segmentation of temporomandibular joint components, including the temporal bone, disc, and condyle. These models underwent rigorous training and validation utilizing a dataset of 3693 magnetic resonance images from 542 patients. Upon evaluation, our ensemble model, which combines five individual models, yielded average Dice similarity coefficients of 0.867, 0.733, 0.904, and 0.952 for the temporal bone, disc, condyle, and background class during internal testing. In the external validation, the average Dice similarity coefficients values for the temporal bone, disc, condyle, and background were 0.720, 0.604, 0.800, and 0.869, respectively. When applied in a clinical setting, these artificial intelligence-augmented tools enhanced the diagnostic accuracy of physicians, especially when discerning between temporomandibular joint anterior disc displacement and osteoarthritis. In essence, automated temporomandibular joint segmentation by our deep learning approach, stands as a promising aid in refining temporomandibular joint disorders diagnosis and treatment strategies.

AI-based dental caries and tooth number detection in intraoral photos: Model development and performance evaluation.

OBJECTIVES: In this study, we aimed to integrate tooth number recognition and caries detection in full intraoral photographic images using a cascade region-based deep convolutional neural network (R-CNN) model to facilitate the practical application of artificial intelligence (AI)-driven automatic caries detection in clinical practice. METHODS: Our dataset comprised 24,578 images, encompassing 4787 upper occlusal, 4347 lower occlusal, 5230 right lateral, 5010 left lateral, and 5204 frontal views. In each intraoral image, tooth numbers and, when present, dental caries, including their location and stage, were annotated using bounding boxes. A cascade R-CNN model was used for dental caries detection and tooth number recognition within intraoral images. RESULTS: For tooth number recognition, the model achieved an average mean average precision (mAP) score of 0.880. In the task of dental caries detection, the model's average mAP score was 0.769, with individual scores spanning from 0.695 to 0.893. CONCLUSIONS: The primary objective of integrating tooth number recognition and caries detection within full intraoral photographic images has been achieved by our deep learning model. The model's training on comprehensive intraoral datasets has demonstrated its potential for seamless clinical application. CLINICAL SIGNIFICANCE: This research holds clinical significance by achieving AI-driven automatic integration of tooth number recognition and caries detection in full intraoral images where multiple teeth are visible. It has the potential to promote the practical application of AI in real-life and clinical settings.

Explainable deep learning-based clinical decision support engine for MRI-based automated diagnosis of temporomandibular joint anterior disk displacement.

BACKGROUND AND OBJECTIVE: MRI is considered the gold standard for diagnosing anterior disc displacement (ADD), the most common temporomandibular joint (TMJ) disorder. However, even highly trained clinicians find it difficult to integrate the dynamic nature of MRI with the complicated anatomical features of the TMJ. As the first validated study for MRI-based automatic TMJ ADD diagnosis, we propose a clinical decision support engine that diagnoses TMJ ADD using MR images and provides heat maps as the visualized rationale of diagnostic predictions using explainable artificial intelligence. METHODS: The engine builds on two deep learning models. The first deep learning model detects a region of interest (ROI) containing three TMJ components (i.e., temporal bone, disc, and condyle) in the entire sagittal MR image. The second deep learning model classifies TMJ ADD into three classes (i.e., normal, ADD without reduction, and ADD with reduction) within the detected ROI. In this retrospective study, the models were developed and tested on the dataset acquired between April 2005 to April 2020. The additional independent dataset acquired at a different hospital between January 2016 to February 2019 was used for the external test of the classification model. Detection performance was assessed by mean average precision (mAP). Classification performance was assessed by the area under the receiver operating characteristic (AUROC), sensitivity, specificity, and Youden's index. 95% confidence intervals were calculated via non-parametric bootstrap to assess the statistical significance of model performances. RESULTS: The ROI detection model achieved mAP of 0.819 at 0.75 intersection over union (IoU) thresholds in the internal test. In internal and external tests, the ADD classification model achieved AUROC values of 0.985 and 0.960, sensitivities of 0.950 and 0.926, and specificities of 0.919 and 0.892, respectively. CONCLUSIONS: The proposed explainable deep learning-based engine provides clinicians with the predictive result and its visualized rationale. The clinicians can make the final diagnosis by integrating primary diagnostic prediction obtained from the proposed engine with the patient's clinical examination findings.