Precision orthodontics entails the use of personalized clinical, biological, social and environmental knowledge of each patient for deep individualized clinical phenotyping and diagnosis combined with the delivery of care using advanced customized devices, technologies and biologics. From its historical origins as a mechanotherapy and materials driven profession, the most recent advances in orthodontics in the past three decades have been propelled by technological innovations including volumetric and surface 3D imaging and printing, advances in software that facilitate the derivation of diagnostic details, enhanced personalization of treatment plans and fabrication of custom appliances. Still, the use of these diagnostic and therapeutic technologies is largely phenotype driven, focusing mainly on facial/skeletal morphology and tooth positions. Future advances in orthodontics will involve comprehensive understanding of an individual's biology through omics, a field of biology that involves large‐scale rapid analyses of DNA, mRNA, proteins and other biological regulators from a cell, tissue or organism. Such understanding will define individual biological attributes that will impact diagnosis, treatment decisions, risk assessment and prognostics of therapy. Equally important are the advances in artificial intelligence (AI) and machine learning, and its applications in orthodontics. AI is already being used to perform validation of approaches for diagnostic purposes such as landmark identification, cephalometric tracings, diagnosis of pathologies and facial phenotyping from radiographs and/or photographs. Other areas for future discoveries and utilization of AI will include clinical decision support, precision orthodontics, payer decisions and risk prediction. The synergies between deep 3D phenotyping and advances in materials, omics and AI will propel the technological and omics era towards achieving the goal of delivering optimized and predictable precision orthodontics.

Objectives: Colony‐stimulating factor‐1 receptor (CSF1R) is vital for the recruitment of monocytes, and their proliferation and differentiation into functional osteoclasts. Mouse studies, where CSF1R and its cognate ligand are absent, have significant craniofacial phenotypes, but these have not been studied in detail. Materials and Methods: Pregnant CD1 mice were fed diets laced with CSF1R inhibitor—PLX5622 starting at embryonic day 3.5 (E3.5) up to birth. Pups were collected at E18.5 to study CSF1R expression using immunofluorescence. Additional pups were studied at postnatal day 21 (P21) and P28 using microcomputed tomography (μCT) and Geometric Morphometrics, to evaluate craniofacial form. Results: CSF1R‐positive cells were present throughout the developing craniofacial region, including the jaw bones, surrounding teeth, tongue, nasal cavities, brain, cranial vault and base regions. Animals exposed to the CSF1R inhibitor in utero had severe depletion of CSF1R‐positive cells at E18.5 and had significant differences in craniofacial form (size and shape) at postnatal timepoints. Centroid sizes for the mandibular and cranio‐maxillary regions were significantly smaller in CSF1R‐inhibited animals. Proportionally, these animals had a domed skull, with taller and wider cranial vaults and shortening of their midfacial regions. Mandibles were smaller vertically and anterio‐posteriorly, with proportionally wider inter‐condylar distances. Conclusions: Embryonic inhibition of CSF1R impacts postnatal craniofacial morphogenesis, with significant influences on the mandibular and cranioskeletal size and shape. These data indicate that CSF1R plays a role in early cranio‐skeletal patterning, likely through osteoclast depletion.

Purpose: This study aims to evaluate the accuracy of fit of full‐arch implant titanium frameworks fabricated from a fully digital workflow using a novel micro‐CT measurement technique. Materials and Methods: A 3D‐printed model with four implant analogs was fabricated. A baseline micro‐CT was obtained after placing temporary cylinders on the model. Next, the printed model was scanned with an intraoral scanner (TRIOS 5), and the STL files were used to fabricate 10 titanium frameworks. Each framework was placed back on the model, and another micro‐CT was taken under two conditions: single screw test (SST‐CT) and final fit test (FFT‐CT), and the measurements were compared to the baseline. Framework passivity was evaluated using a single‐screw test (SST) and a screw‐resistance test (SRT). The accuracy of the intraoral scans was assessed by superimposing the 10 scans with a laboratory scan STL to determine if the misfit was due to scanning or milling and designing errors. Results: None of the frameworks was deemed acceptable using SST‐CT, and only three had an acceptable fit using FFT‐CT. SST and SRT non‐passivity rates were 60% and 80%, respectively. Superimposition analysis revealed that only two intraoral scans used for framework fabrication fell within the acceptable deviation range of 150 microns, suggesting a high tendency for scanning errors and a possible milling or designing error in two samples. Conclusion: The results show a significant level of misfit. This suggests that the full‐digital workflow for full‐mouth rehabilitation can present some limitations. Due to the rapid advancement in intraoral scanning, further studies are required to validate these findings.