Neanderthal'dentists' treated cavities 59,000 years ago A molar points to some sophisticated dental work performed by our extinct cousins. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. The molar tooth found in Chagyrskaya Cave and its macro-features. Breakthroughs, discoveries, and DIY tips sent six days a week. Neanderthals () were once considered to have been extremely primitive and unsophisticated compared to us humans ().

From veneers to abrasive toothpastes, a perfect smile can hide cavities and cause other problems. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Your teeth probably don't look like a movie star's, and that might be a good thing. Breakthroughs, discoveries, and DIY tips sent six days a week. However, in recent years, critics have pointed out that one thing can immediately dispel historical accuracy: actors' blindingly white, perfect teeth.

Dental forensic identification targets to identify persons with dental traces. The task is vital for the investigation of criminal scenes and mass disasters because of the resistance of dental structures and the wide-existence of dental imaging.

Small jaws mean big problems for modern humans. Modern diets gave us smaller jaws--and a lifetime of orthodontic problems. Breakthroughs, discoveries, and DIY tips sent six days a week. Every year, millions of children and teens undergo a common ritual of growing up: getting braces. And it's not just young folks who turn to metal brackets to handle some common dental issues--the Cleveland Clinic estimates that some 20% of new orthodontic patients are over the age of 18 .

It may even repair damaged enamel and improve your oral microbiome. Breakthroughs, discoveries, and DIY tips sent six days a week. Whitening your teeth often comes at a financial and physical cost. Many of today's most popular products including gels, strips, and rinses rely on peroxide-based bleaching solutions. While effective, the chemical processes generate reactive oxygen species (ROS) compounds that not only destroy staining molecules--they can eventually erode tooth enamel .

Cone-Beam Computed Tomography (CBCT) is a widely used 3D imaging technique in dentistry, providing volumetric information about the anatomical structures of jaws and teeth. Accurate segmentation of these anatomies is critical for clinical applications such as diagnosis and surgical planning, but remains time-consuming and challenging. In this paper, we present U-Mamba2, a new neural network architecture designed for multi-anatomy CBCT segmentation in the context of the ToothFairy3 challenge. U-Mamba2 integrates the Mamba2 state space models into the U-Net architecture, enforcing stronger structural constraints for higher efficiency without compromising performance. In addition, we integrate interactive click prompts with cross-attention blocks, pre-train U-Mamba2 using self-supervised learning, and incorporate dental domain knowledge into the model design to address key challenges of dental anatomy segmentation in CBCT. Extensive experiments, including independent tests, demonstrate that U-Mamba2 is both effective and efficient, securing first place in both tasks of the Toothfairy3 challenge. In Task 1, U-Mamba2 achieved a mean Dice of 0.84, HD95 of 38.17 with the held-out test data, with an average inference time of 40.58s. In Task 2, U-Mamba2 achieved the mean Dice of 0.87 and HD95 of 2.15 with the held-out test data. The code is publicly available at https://github.com/zhiqin1998/UMamba2.

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 .

Orthopantomogram (OPGs) and Cone-Beam Computed Tomography (CBCT) are vital for dentistry, but creating large datasets for automated tooth segmentation is hindered by the labor-intensive process of manual instance-level annotation. This research aimed to benchmark and advance semi-supervised learning (SSL) as a solution for this data scarcity problem. We organized the 2nd Semi-supervised Teeth Segmentation (STS 2024) Challenge at MICCAI 2024. We provided a large-scale dataset comprising over 90,000 2D images and 3D axial slices, which includes 2,380 OPG images and 330 CBCT scans, all featuring detailed instance-level FDI annotations on part of the data. The challenge attracted 114 (OPG) and 106 (CBCT) registered teams. To ensure algorithmic excellence and full transparency, we rigorously evaluated the valid, open-source submissions from the top 10 (OPG) and top 5 (CBCT) teams, respectively. All successful submissions were deep learning-based SSL methods. The winning semi-supervised models demonstrated impressive performance gains over a fully-supervised nnU-Net baseline trained only on the labeled data. For the 2D OPG track, the top method improved the Instance Affinity (IA) score by over 44 percentage points. For the 3D CBCT track, the winning approach boosted the Instance Dice score by 61 percentage points. This challenge confirms the substantial benefit of SSL for complex, instance-level medical image segmentation tasks where labeled data is scarce. The most effective approaches consistently leveraged hybrid semi-supervised frameworks that combined knowledge from foundational models like SAM with multi-stage, coarse-to-fine refinement pipelines. Both the challenge dataset and the participants' submitted code have been made publicly available on GitHub (https://github.com/ricoleehduu/STS-Challenge-2024), ensuring transparency and reproducibility.

Scientists at King's College London developed a toothpaste ingredient using keratin from human hair that can repair and strengthen damaged tooth enamel.

The advancement of artificial intelligence in oral healthcare relies on the availability of large-scale multimodal datasets that capture the complexity of clinical practice. In this paper, we present a comprehensive multimodal dataset, comprising 8775 dental checkups from 4800 patients collected over eight years (2018-2025), with patients ranging from 10 to 90 years of age. The dataset includes 50000 intraoral images, 8056 radiographs, and detailed textual records, including diagnoses, treatment plans, and follow-up notes. The data were collected under standard ethical guidelines and annotated for benchmarking. To demonstrate its utility, we fine-tuned state-of-the-art large vision-language models, Qwen-VL 3B and 7B, and evaluated them on two tasks: classification of six oro-dental anomalies and generation of complete diagnostic reports from multimodal inputs. We compared the fine-tuned models with their base counterparts and GPT-4o. The fine-tuned models achieved substantial gains over these baselines, validating the dataset and underscoring its effectiveness in advancing AI-driven oro-dental healthcare solutions. The dataset is publicly available, providing an essential resource for future research in AI dentistry.