The high-pitched whine of dentistry tools creates a lot of anxiety, especially for kids. The fear of going to the dentist is called odontophobia. Breakthroughs, discoveries, and DIY tips sent every weekday. If the thought of going to the dentist makes your teeth chatter with fear, you're not alone. At least 15 to 20 percent of adults are believed to have odontophobia--aka dental anxiety--which prevents them from maintaining regular cleanings and dental check-ups .

A study of over 5,000 teens found a strong connection between childhood stress and dental anxiety. Childhood stresses may increase your chances of developing dental fear. Breakthroughs, discoveries, and DIY tips sent every weekday. Few people enjoy someone else poking around their mouth. A recent study from the New York University College of Dentistry indicates that as many as three out of four adults dread going to the dentist .

There has been increasing interest in the generation of high-quality, realistic synthetic medical images in recent years. Such synthetic datasets can mitigate the scarcity of public datasets for artificial intelligence research, and can also be used for educational purposes. In this paper, we propose a combination of diffusion-based generation (PanoDiff) and Super-Resolution (SR) for generating synthetic dental panoramic radiographs (PRs). The former generates a low-resolution (LR) seed of a PR (256 X 128) which is then processed by the SR model to yield a high-resolution (HR) PR of size 1024 X 512. For SR, we propose a state-of-the-art transformer that learns local-global relationships, resulting in sharper edges and textures. Experimental results demonstrate a Frechet inception distance score of 40.69 between 7243 real and synthetic images (in HR). Inception scores were 2.55, 2.30, 2.90 and 2.98 for real HR, synthetic HR, real LR and synthetic LR images, respectively. Among a diverse group of six clinical experts, all evaluating a mixture of 100 synthetic and 100 real PRs in a time-limited observation, the average accuracy in distinguishing real from synthetic images was 68.5% (with 50% corresponding to random guessing).

Robotic dentistry is becoming a reality. Your dentist may remind you to brush, floss and mouthwash – but what is the "right" order to do it? While all steps of oral hygiene can benefit dental health, Dr. Mike Wei, DDS, of New York City, shared with Fox News Digital that he'd recommend the below order. Starting with floss helps to remove food debris and plaque between the teeth and along the gumline, which a toothbrush "may not reach effectively," according to Wei. Dr. Ellie Phillips (not pictured) recommends using xylitol gum and mints to promote healthy salivary flow.

Hallucination is a common problem for Large Vision-Language Models (LVLMs) with long generations which is difficult to eradicate. The generation with hallucinations is partially inconsistent with the image content. To mitigate hallucination, current studies either focus on the process of model inference or the results of model generation, but the solutions they design sometimes do not deal appropriately with various types of queries and the hallucinations of the generations about these queries. To accurately deal with various hallucinations, we present a unified framework, Dentist, for hallucination mitigation. The core step is to first classify the queries, then perform different processes of hallucination mitigation based on the classification result, just like a dentist first observes the teeth and then makes a plan. In a simple deployment, Dentist can classify queries as perception or reasoning and easily mitigate potential hallucinations in answers which has been demonstrated in our experiments.

For many people, sitting back in the dentist's chair can already be a terrifying experience. But now a trip to the dentist could get a whole lot scarier as an AI-powered robot completes its first unsupervised procedure on a live human. The robot, developed by US-based company Perspective, successfully carried out a crown replacement in just 15 minutes - eight times faster than a human specialist. To carry out the procedure, the patient's mouth was first mapped with a 3D scanner before an AI planned and carried out the operation autonomously. Dr Chris Ciriello, CEO and founder of Perceptive, says: 'This medical breakthrough enhances precision and efficiency of dental procedures, and democratizes access to better dental care, for improved patient experience and clinical outcomes.'

Occlusal contacts are the locations at which the occluding surfaces of the maxilla and the mandible posterior teeth meet. Occlusal contact detection is a vital tool for restoring the loss of masticatory function and is a mandatory assessment in the field of dentistry, with particular importance in prosthodontics and restorative dentistry. The most common method for occlusal contact detection is articulating paper. However, this method can indicate significant medically false positive and medically false negative contact areas, leaving the identification of true occlusal indications to clinicians. To address this, we propose a multiclass Vision Transformer and Fully Convolutional Network ensemble semantic segmentation model with a combination hierarchical loss function, which we name as Hierarchical Fully Convolutional Branch Transformer (H-FCBFormer). We also propose a method of generating medically true positive semantic segmentation masks derived from expert annotated articulating paper masks and gold standard masks. The proposed model outperforms other machine learning methods evaluated at detecting medically true positive contacts and performs better than dentists in terms of accurately identifying object-wise occlusal contact areas while taking significantly less time to identify them.

This article describes the clinical validation study setup, statistical analysis and results for a deep learning algorithm which detects dental anomalies in intraoral radiographic images, more specifically caries, apical lesions, root canal treatment defects, marginal defects at crown restorations, periodontal bone loss and calculus. The study compares the detection performance of dentists using the deep learning algorithm to the prior performance of these dentists evaluating the images without algorithmic assistance. Calculating the marginal profit and loss of performance from the annotated paired image data allows for a quantification of the hypothesized change in sensitivity and specificity. The statistical significance of these results is extensively proven using both McNemar's test and the binomial hypothesis test. The average sensitivity increases from $60.7\%$ to $85.9\%$, while the average specificity slightly decreases from $94.5\%$ to $92.7\%$. We prove that the increase of the area under the localization ROC curve (AUC) is significant (from $0.60$ to $0.86$ on average), while the average AUC is bounded by the $95\%$ confidence intervals ${[}0.54, 0.65{]}$ and ${[}0.82, 0.90{]}$. When using the deep learning algorithm for diagnostic guidance, the dentist can be $95\%$ confident that the average true population sensitivity is bounded by the range $79.6\%$ to $91.9\%$. The proposed paired data setup and statistical analysis can be used as a blueprint to thoroughly test the effect of a modality change, like a deep learning based detection and/or segmentation, on radiographic images.

Robotic technologies are becoming increasingly popular in dentistry due to the high level of precision required in delicate dental procedures. Most dental robots available today are designed for implant surgery, helping dentists to accurately place implants in the desired position and depth. In this paper, we introduce the DentiBot, the first robot specifically designed for dental endodontic treatment. The DentiBot is equipped with a force and torque sensor, as well as a string-based Patient Tracking Module, allowing for real-time monitoring of endodontic file contact and patient movement. We propose a 6-DoF hybrid position/force controller that enables autonomous adjustment of the surgical path and compensation for patient movement, while also providing protection against endodontic file fracture. In addition, a file flexibility model is incorporated to compensate for file bending. Pre-clinical evaluations performed on acrylic root canal models and resin teeth confirm the feasibility of the DentiBot in assisting endodontic treatment.

Doctors believe Artificial Intelligence is now saving lives, after a major advancement in breast cancer screenings. A.I. is detecting early signs of the disease, in some cases years before doctors would find the cancer on a traditional scan. Gum disease (periodontitis) affects more than 47% of Americans -- or nearly 65 million people -- including former Major League Baseball player Alex Rodriguez, who recently announced he has the condition. As artificial intelligence continues to expand into dental and medical uses, dentists are relying on the technology to quickly and accurately detect and prevent periodontitis, decay, bone loss and other gum health issues. Now, VideaHealth, a medical technology company in Boston, has created a dental AI platform that is available to 90% of dental practices in the U.S., the company said.