AI in Dental Radiology-Improving the Efficiency of Reporting With ChatGPT: Comparative Study.

Background: Structured and standardized documentation is critical for accurately recording diagnostic findings, treatment plans, and patient progress in health care. Manual documentation can be labor-intensive and error-prone, especially under time constraints, prompting interest in the potential of artificial intelligence (AI) to automate and optimize these processes, particularly in medical documentation.

Objective: This study aimed to assess the effectiveness of ChatGPT (OpenAI) in generating radiology reports from dental panoramic radiographs, comparing the performance of AI-generated reports with those manually created by dental students.

Automated tooth segmentation in magnetic resonance scans using deep learning - A pilot study.

Objectives: The main objective was to develop and evaluate an artificial intelligence model for tooth segmentation in magnetic resonance (MR) scans.

Methods: MR scans of 20 patients performed with a commercial 64-channel head coil with a T1-weighted 3D-SPACE (Sampling Perfection with Application Optimized Contrasts using different flip angle Evolution) sequence were included. Sixteen datasets were used for model training and 4 for accuracy evaluation.

AI-Powered Identification of Osteoporosis in Dental Panoramic Radiographs: Addressing Methodological Flaws in Current Research.

Osteoporosis, a systemic skeletal disorder, is expected to affect 60% of women over 50. While dual-energy X-ray absorptiometry (DXA) scans are the current gold standard for diagnosis, they are typically used only after fractures occur, highlighting the need for early detection tools. Initial studies have shown panoramic radiographs (PRs) to be a potential medium, but these have methodological flaws.

Automated condylar seating assessment using a deep learning-based three-step approach.

Objectives: In orthognatic surgery, one of the primary determinants for reliable three-dimensional virtual surgery planning (3D VSP) and an accurate transfer of 3D VSP to the patient in the operation room is the condylar seating. Incorrectly seated condyles would primarily affect the accuracy of maxillary-first bimaxillary osteotomies as the maxillary repositioning is dependent on the positioning of the mandible in the cone-beam computed tomography (CBCT) scan. This study aimed to develop and validate a novel tool by utilizing a deep learning algorithm that automatically evaluates the condylar seating based on CBCT images as a proof of concept.

Automated monitoring of tooth wear progression using AI on intraoral scans.

Objectives: This study aimed to develop and evaluate a fully automated method for visualizing and measuring tooth wear progression using pairs of intraoral scans (IOSs) in comparison with a manual protocol.

Methods: Eight patients with severe tooth wear progression were retrospectively included, with IOSs taken at baseline and 1-year, 3-year, and 5-year follow-ups. For alignment, the automated method segmented the arch into separate teeth in the IOSs.

Effects of volume management on free flap perfusion and metabolism in a large animal model study.

Free flap failure represents a substantial clinical burden. The role of intraoperative volume management remains controversial, with valid studies lacking. Here, using a large animal model, we investigated the influence of volume management on free flap perfusion and metabolism.

Advancements in diagnosing oral potentially malignant disorders: leveraging Vision transformers for multi-class detection.

Objectives: Diagnosing oral potentially malignant disorders (OPMD) is critical to prevent oral cancer. This study aims to automatically detect and classify the most common pre-malignant oral lesions, such as leukoplakia and oral lichen planus (OLP), and distinguish them from oral squamous cell carcinomas (OSCC) and healthy oral mucosa on clinical photographs using vision transformers.

Methods: 4,161 photographs of healthy mucosa, leukoplakia, OLP, and OSCC were included.

Does facial asymmetry vary between subjects of different age groups? A 3D stereophotogrammetry analysis.

This study was aimed to assess whether facial asymmetry increases with age and to examine potential gender differences using 3D stereophotogrammetry. A prospective cross-sectional study was performed. 3D photographs were acquired from 600 control subjects, 300 male, 300 female, and were stratified into 15 different age groups ranging from 0 to 70+.

Combining public datasets for automated tooth assessment in panoramic radiographs.

Objective: Panoramic radiographs (PRs) provide a comprehensive view of the oral and maxillofacial region and are used routinely to assess dental and osseous pathologies. Artificial intelligence (AI) can be used to improve the diagnostic accuracy of PRs compared to bitewings and periapical radiographs. This study aimed to evaluate the advantages and challenges of using publicly available datasets in dental AI research, focusing on solving the novel task of predicting tooth segmentations, FDI numbers, and tooth diagnoses, simultaneously.

Challenges in conducting clinical research in primary care dentistry.

The integration of dentistry into primary health care is crucial for promoting patient well-being. However, clinical studies in dentistry face challenges, including issues with study design, transparency, and relevance to primary care. Clinical trials in dentistry often focus on specific issues with strict eligibility criteria, limiting the generalizability of findings.

Fully automated landmarking and facial segmentation on 3D photographs.

Three-dimensional facial stereophotogrammetry provides a detailed representation of craniofacial soft tissue without the use of ionizing radiation. While manual annotation of landmarks serves as the current gold standard for cephalometric analysis, it is a time-consuming process and is prone to human error. The aim in this study was to develop and evaluate an automated cephalometric annotation method using a deep learning-based approach.

The Perfect Timing-Immediate versus Delayed Microvascular Reconstruction of the Mandible.

In this retrospective study, the clinical and economic implications of microvascular reconstruction of the mandible were assessed, comparing immediate versus delayed surgical approaches. Utilizing data from two German university departments for oral and maxillofacial surgery, the study included patients who underwent mandibular reconstruction following continuity resection. The data assessed included demographic information, reconstruction details, medical history, dental rehabilitation status, and flap survival rates.

Detection of caries around restorations on bitewings using deep learning.

Objective: Secondary caries lesions adjacent to restorations, a leading cause of restoration failure, require accurate diagnostic methods to ensure an optimal treatment outcome. Traditional diagnostic strategies rely on visual inspection complemented by radiographs. Recent advancements in artificial intelligence (AI), particularly deep learning, provide potential improvements in caries detection.

Exploring Deep Cervical Compartments in Head and Neck Surgical Oncology through Augmented Reality Vision: A Proof of Concept.

Virtual surgical planning allows surgeons to meticulously define surgical procedures by creating a digital replica of patients' anatomy. This enables precise preoperative assessment, facilitating the selection of optimal surgical approaches and the customization of treatment plans. In neck surgery, virtual planning has been significantly underreported compared to craniofacial surgery, due to a multitude of factors, including the predominance of soft tissues, the unavailability of intraoperative navigation and the complexity of segmenting such areas.

Intra-oral scan segmentation using deep learning.

Objective: Intra-oral scans and gypsum cast scans (OS) are widely used in orthodontics, prosthetics, implantology, and orthognathic surgery to plan patient-specific treatments, which require teeth segmentations with high accuracy and resolution. Manual teeth segmentation, the gold standard up until now, is time-consuming, tedious, and observer-dependent. This study aims to develop an automated teeth segmentation and labeling system using deep learning.

Skeletal structure of asymmetric mandibular prognathism and retrognathism.

Background: This study aimed to compare the skeletal structures between mandibular prognathism and retrognathism among patients with facial asymmetry.

Results: Patients who had mandibular asymmetry with retrognathism (Group A) in The Netherlands were compared with those with deviated mandibular prognathism (Group B) in Korea. All the data were obtained from 3D-reformatted cone-beam computed tomography images from each institute.

Optimizing efficiency in the creation of patient-specific plates through field-driven generative design in maxillofacial surgery.

Field driven design is a novel approach that allows to define through equations geometrical entities known as implicit bodies. This technology does not rely upon conventional geometry subunits, such as polygons or edges, rather it represents spatial shapes through mathematical functions within a geometrical field. The advantages in terms of computational speed and automation are conspicuous, and well acknowledged in engineering, especially for lattice structures.

Patients' perspectives on the use of artificial intelligence in dentistry: a regional survey.

The use of artificial intelligence (AI) in dentistry is rapidly evolving and could play a major role in a variety of dental fields. This study assessed patients' perceptions and expectations regarding AI use in dentistry. An 18-item questionnaire survey focused on demographics, expectancy, accountability, trust, interaction, advantages and disadvantages was responded to by 330 patients; 265 completed questionnaires were included in this study.

Positional assessment of lower third molar and mandibular canal using explainable artificial intelligence.

Objective: The aim of this study is to automatically assess the positional relationship between lower third molars (M3i) and the mandibular canal (MC) based on the panoramic radiograph(s) (PR(s)).

Material And Methods: A total of 1444 M3s were manually annotated and labeled on 863 PRs as a reference. A deep-learning approach, based on MobileNet-V2 combination with a skeletonization algorithm and a signed distance method, was trained and validated on 733 PRs with 1227 M3s to classify the positional relationship between M3i and MC into three categories.

Super-Resolution of Dental Panoramic Radiographs Using Deep Learning: A Pilot Study.

Using super-resolution (SR) algorithms, an image with a low resolution can be converted into a high-quality image. Our objective was to compare deep learning-based SR models to a conventional approach for improving the resolution of dental panoramic radiographs. A total of 888 dental panoramic radiographs were obtained.

Deep learning for automated segmentation of the temporomandibular joint.

Objective: Quantitative analysis of the volume and shape of the temporomandibular joint (TMJ) using cone-beam computed tomography (CBCT) requires accurate segmentation of the mandibular condyles and the glenoid fossae. This study aimed to develop and validate an automated segmentation tool based on a deep learning algorithm for accurate 3D reconstruction of the TMJ.

Materials And Methods: A three-step deep-learning approach based on a 3D U-net was developed to segment the condyles and glenoid fossae on CBCT datasets.