TGF-ß Signaling in Cranial Neural Crest Affects Late-Stage Mandibular Bone Resorption and Length.

Malocclusions are common craniofacial malformations which cause quality of life and health problems if left untreated. Unfortunately, the current treatment for severe skeletal malocclusion is invasive surgery. Developing improved therapeutic options requires a deeper understanding of the cellular mechanisms responsible for determining jaw bone length. We have recently shown that neural crest mesenchyme (NCM) can alter jaw length by controlling recruitment and function of mesoderm-derived osteoclasts. Transforming growth factor beta (TGF-ß) signaling is critical to craniofacial development by directing bone resorption and formation, and heterozygous mutations in TGF-ß type I receptor (TGFBR1) are associated with micrognathia in humans. To identify what role TGF-ß signaling in NCM plays in controlling osteoclasts during mandibular development, mandibles of mouse embryos deficient in the gene encoding Tgfbr1 specifically in NCM were analyzed. Our lab and others have demonstrated that Tgfbr1fl/fl;Wnt1-Cre mice display significantly shorter mandibles with no condylar, coronoid, or angular processes. We hypothesize that TGF-ß signaling in NCM can also direct later bone remodeling and further regulate late embryonic jaw bone length. Interestingly, analysis of mandibular bone through micro-computed tomography and Masson's trichrome revealed no significant difference in bone quality between the Tgfbr1fl/fl;Wnt1-Cre mice and controls, as measured by bone perimeter/bone area, trabecular rod-like diameter, number and separation, and gene expression of Collagen type 1 alpha 1 (Col1α1) and Matrix metalloproteinase 13 (Mmp13). Though there was not a difference in localization of bone resorption within the mandible indicated by TRAP staining, Tgfbr1fl/fl;Wnt1-Cre mice had approximately three-fold less osteoclast number and perimeter than controls. Gene expression of receptor activator of nuclear factor kappa-ß (Rank) and Mmp9, markers of osteoclasts and their activity, also showed a three-fold decrease in Tgfbr1fl/fl;Wnt1-Cre mandibles. Evaluation of osteoblast-to-osteoclast signaling revealed no significant difference between Tgfbr1fl/fl;Wnt1-Cre mandibles and controls, leaving the specific mechanism unresolved. Finally, pharmacological inhibition of Tgfbr1 signaling during the initiation of bone mineralization and resorption significantly shortened jaw length in embryos. We conclude that TGF-ß signaling in NCM decreases mesoderm-derived osteoclast number, that TGF-ß signaling in NCM impacts jaw length late in development, and that this osteoblast-to-osteoclast communication may be occurring through an undescribed mechanism.

A novel collaborative learning model for mixed dentition and fillings segmentation in panoramic radiographs.

INTRODUCTION: It is critical for dentists to identify and differentiate primary and permanent teeth, fillings, dental restorations and areas with pathological findings when reviewing dental radiographs to ensure that an accurate diagnosis is made and the optimal treatment can be planned. Unfortunately, dental radiographs are sometimes read incorrectly due to human error or low-quality images. While secondary or group review can help catch errors, many dentists work in practice alone and/or do not have time to review all of their patients' radiographs with another dentist. Artificial intelligence may facilitate the accurate interpretation of radiographs. To help support the review of panoramic radiographs, we developed a novel collaborative learning model that simultaneously identifies and differentiates primary and permanent teeth and detects fillings. METHODS: We used publicly accessible dental panoramic radiographic images and images obtained from the University of Missouri-Kansas City, School of Dentistry to develop and optimize two high-performance classifiers: (1) a system for tooth segmentation that can differentiate primary and permanent teeth and (2) a system to detect dental fillings. RESULTS: By utilizing these high-performance classifiers, we created models that can identify primary and permanent teeth (mean average precision [mAP] 95.32 % and performance [F-1] 92.50 %), as well as their associated dental fillings (mAP 91.53 % and F-1 91.00 %). We also designed a novel method for collaborative learning that utilizes these two classifiers to enhance recognition performance (mAP 94.09 % and F-1 93.41 %). CONCLUSIONS: Our model improves upon the existing machine learning models to simultaneously identify and differentiate primary and permanent teeth, and to identify any associated fillings. CLINICAL SIGNIFICANCE: Human error can lead to incorrect readings of panoramic radiographs. By developing artificial intelligence and machine learning methods to analyze panoramic radiographs, dentists can use this information to support their radiograph interpretations, help communicate the information to patients, and assist dental students learning to read radiographs.

Incremental Learning for Panoramic Radiograph Segmentation.

This study aimed to determine a fundamental method for the automated detection and treatment of dental and orthodontic problems. Manual intervention is inefficient and prone to human error in detecting anomalies. Deep learning was used to identify a solution to this problem. We proposed leveraging incremental learning approaches using Mask RCNN as backbone networks on small datasets to construct a more accurate model from automatically labeled data. The knowledge acquired at one stage of education is carried over to the subsequent stage. By incorporating newly annotated data, transfer learning improved the model's performance. Despite the data scarcity issues inherent in radiograph image collection, the findings for filling and tooth segmentation tasks were encouraging and adequate. We compared our results to prior research to optimize the performance of our proposed method.

Collaborative deep learning model for tooth segmentation and identification using panoramic radiographs.

Panoramic radiographs are an integral part of effective dental treatment planning, supporting dentists in identifying impacted teeth, infections, malignancies, and other dental issues. However, screening for anomalies solely based on a dentist's assessment may result in diagnostic inconsistency, posing difficulties in developing a successful treatment plan. Recent advancements in deep learning-based segmentation and object detection algorithms have enabled the provision of predictable and practical identification to assist in the evaluation of a patient's mineralized oral health, enabling dentists to construct a more successful treatment plan. However, there has been a lack of efforts to develop collaborative models that enhance learning performance by leveraging individual models. The article describes a novel technique for enabling collaborative learning by incorporating tooth segmentation and identification models created independently from panoramic radiographs. This collaborative technique permits the aggregation of tooth segmentation and identification to produce enhanced results by recognizing and numbering existing teeth (up to 32 teeth). The experimental findings indicate that the proposed collaborative model is significantly more effective than individual learning models (e.g., 98.77% vs. 96% and 98.44% vs.91% for tooth segmentation and recognition, respectively). Additionally, our models outperform the state-of-the-art segmentation and identification research. We demonstrated the effectiveness of collaborative learning in detecting and segmenting teeth in a variety of complex situations, including healthy dentition, missing teeth, orthodontic treatment in progress, and dentition with dental implants.