This study demonstrates the functionality and clinical value of magnetic resonance imaging (MRI) to cone-beam computed tomography (CBCT) registration using a new open-source artificial intelligence (AI) model called MR2CBCT. We present five clinical cases in which the AI-based method was used to register CBCT and MRI images. For comparison, manual registration was also performed. Qualitative inspection revealed that manual alignment often showed errors that could compromise diagnostic accuracy. In contrast, the AI-based approach consistently corrected these discrepancies, producing more anatomically coherent fused images to better support clinical decision-making. Our findings highlight MR2CBCT as a reliable and accessible tool for multimodal integration in temporomandibular joint (TMJ) assessment in orthodontics and general dentistry.
Practice-Based Research Networks (PBRN) are optimal settings for research to be conducted in the real world of clinical practice where the majority of care is provided. In this narrative review, we provide an overview of the National Dental Practice-Based Research Network (Network), which is one of the largest and most successful Dental PBRNs, and its impact on orthodontics. The Network is currently in its third seven-year round of funding and has conducted 58 studies to date. There are over 8,000 members enrolled in the Network. The largest prospective cohort study on anterior open bites was conducted through the Network. The Network provides resources and opportunities to engage clinicians in practice-based participatory research where clinical studies with direct impact on clinical outcomes are conducted.

