Dental developmental abnormalities and dental development-related diagnosis and treatment represents a critical and challenging area of clinical practice. This process spans multiple stages, from diagnosis to the creation of treatment plans, requiring substantial theoretical knowledge and rich clinical experience. In recent years, the development of artificial intelligence (AI), particularly deep learning technologies exemplified by convolutional neural networks, has been facilitated by the abundance of dental clinical image resources. Advancements in AI have provided substantial support for the diagnosis and treatment of oral diseases, significantly enhancing clinical efficiency. Deep learning has numerous applications in developmental abnormalities and dental development-related diagnosis and treatment. First, deep learning can assist in the identification of developmental abnormalities in radiographs and intraoral images, helping dentists make accurate diagnoses. Second, this technology can be used to assess dental development and predict tooth eruption, providing valuable reference for the formulation of personalized treatment plans. Furthermore, deep learning can identify root and root canal morphology, as well as locate challenging root canals, thereby enhancing the dentists' understanding of root canal anatomy and improving the success rate of endodontic treatments. Despite its significant potential in these areas, research in this field remains in the early stage. There are several limitations in the literature, including the inability to implement systematic disease diagnosis and treatment and a lack of multi-center studies. Future research should aim to design multi-center studies and develop deep learning models that integrate disease diagnosis, developmental assessment, and other factors, conducting a comprehensive analysis of multiple variables to further enhance the practical value of these models.

PTH-related peptide (PTHrP) improves the bone marrow micro-environment to activate the bone-remodelling, but the coordinated regulation of PTHrP and transforming growth factor-β (TGFβ) signalling in TMJ-OA remains incompletely understood. We used disordered occlusion to establish model animals that recapitulate the ordinary clinical aetiology of TMJ-OA. Immunohistochemical and histological analyses revealed condylar fibrocartilage degeneration in model animals following disordered occlusion. TMJ-OA model animals administered intermittent PTHrP (iPTH) exhibited significantly decreased condylar cartilage degeneration. Micro-CT, histomorphometry, and Western Blot analyses disclosed that iPTH promoted subchondral bone formation in the TMJ-OA model animals. In addition, iPTH increased the number of osterix (OSX)-positive cells and osteocalcin (OCN)-positive cells in the subchondral bone marrow cavity. However, the number of osteoclasts was also increased by iPTH, indicating that subchondral bone volume increase was mainly due to the iPTH-mediated increase in the bone-formation ability of condylar subchondral bone. In vitro, PTHrP treatment increased condylar subchondral bone marrow-derived mesenchymal stem cell (SMSC) osteoblastic differentiation potential and upregulated the gene and protein expression of key regulators of osteogenesis. Furthermore, we found that PTHrP-PTH1R signalling inhibits TGFβ signalling during osteoblastic differentiation. Collectively, these data suggested that iPTH improves OA lesions by enhancing osteoblastic differentiation in subchondral bone and suppressing aberrant active TGFβ signalling. These findings indicated that PTHrP, which targets the TGFβ signalling pathway, may be an effective biological reagent to prevent and treat TMJ-OA in the clinic.