IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS 1 Haptic bilateral teleoperation system for free-hand dental procedures Lorenzo Pagliara, Student Member, IEEE, Enrico Ferrentino, Member, IEEE, Andrea Chiacchio, Giovanni Russo, Senior Member, IEEE Abstract --Free-hand dental procedures are typically repetitive, time-consuming and require high precision and manual dexterity. Dental robots can play a key role in improving procedural accuracy and safety, enhancing patient comfort, and reducing operator workload. However, robotic solutions for free-hand procedures remain limited or completely lacking, and their acceptance is still low. T o address this gap, we develop a haptic bilateral teleoperation system (HBTS) for free-hand dental procedures. The system includes a dedicated mechanical end-effector, compatible with standard clinical tools, and equipped with an endoscopic camera for improved visibility of the intervention site. By ensuring motion and force correspondence between the operator's actions and the robot's movements, monitored through visual feedback, we enhance the operator's sensory awareness and motor accuracy. Furthermore, recognizing the need to ensure procedural safety, we limit interaction forces by scaling the motion references provided to the admittance controller based solely on measured contact forces. This ensures effective force limitation in all contact states without requiring prior knowledge of the environment. The proposed HBTS is validated in a dental scaling procedure using a dental phantom. The results show that the system improves the naturalness, safety, and accuracy of teleoperation, highlighting its potential to enhance free-hand dental procedures. I NTRODUCTION A. Background R OBOTICS is rewriting the future of healthcare, emerging as a disruptive technology capable of optimizing and revolutionizing the way some medical procedures are conducted.

We apply the domain-specific augmentations of (i) random tooth reduction, (ii) random artifact addition, (iii) random rigid patch transform, as well as (iv) random contrast shifting and Gaussian noise for instance discriminative learning. The DSA is enabled by the anatomical awareness, and its parameters are set by working with dentists to best follow the possible clinical cases. In specific, for random tooth reduction, one tooth area is set to the background intensity according to the tooth mask from semantic segmentation. The background intensity is determined by the average intensity of the non-teeth area. For random artifact addition, two types of artifacts are included: braces and dental filling.

We apply the domain-specific augmentations of (i) random tooth reduction, (ii) random artifact addition, (iii) random rigid patch transform, as well as (iv) random contrast shifting and Gaussian noise for instance discriminative learning. The DSA is enabled by the anatomical awareness, and its parameters are set by working with dentists to best follow the possible clinical cases. In specific, for random tooth reduction, one tooth area is set to the background intensity according to the tooth mask from semantic segmentation. The background intensity is determined by the average intensity of the non-teeth area. For random artifact addition, two types of artifacts are included: braces and dental filling.

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.

Untreated periodontitis causes inflammation within the supporting tissue of the teeth and can ultimately lead to tooth loss. Modeling periodontal outcomes is beneficial as they are difficult and time consuming to measure, but disparities in representation between demographic groups must be considered. There may not be enough participants to build group specific models and it can be ineffective, and even dangerous, to apply a model to participants in an underrepresented group if demographic differences were not considered during training. We propose an extension to RECaST Bayesian transfer learning framework. Our method jointly models multivariate outcomes, exhibiting significant improvement over the previous univariate RECaST method. Further, we introduce an online approach to model sequential data sets. Negative transfer is mitigated to ensure that the information shared from the other demographic groups does not negatively impact the modeling of the underrepresented participants. The Bayesian framework naturally provides uncertainty quantification on predictions. Especially important in medical applications, our method does not share data between domains. We demonstrate the effectiveness of our method in both predictive performance and uncertainty quantification on simulated data and on a database of dental records from the HealthPartners Institute.

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.'

Accurate detection of bone fenestration and dehiscence (FD) is crucial for effective treatment planning in dentistry. While cone-beam computed tomography (CBCT) is the gold standard for evaluating FD, it comes with limitations such as radiation exposure, limited accessibility, and higher cost compared to intraoral images. In intraoral images, dentists face challenges in the differential diagnosis of FD. This paper presents a novel and clinically significant application of FD detection solely from intraoral images. To achieve this, we propose FD-SOS, a novel open-set object detector for FD detection from intraoral images. FD-SOS has two novel components: conditional contrastive denoising (CCDN) and teeth-specific matching assignment (TMA). These modules enable FD-SOS to effectively leverage external dental semantics. Experimental results showed that our method outperformed existing detection methods and surpassed dental professionals by 35% recall under the same level of precision. Code is available at: https: //github.com/xmed-lab/FD-SOS.

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.

Dental disease is one of the most common chronic diseases in the United States, particularly affecting disadvantaged communities. While scientific evidence indicates that healthy oral self-care behaviors (OSCB) (i.e., systematic, twice-a-day tooth brushing) prevent dental disease [Löe, 2000, Attin and Hornecker, 2005], this basic behavior is not consistently practiced [Yaacob et al., 2014]. We have developed Oralytics, an online, reinforcement learning (RL) algorithm that optimizes the delivery of personalized intervention prompts to improve OSCB. These prompts, delivered via push notification from the Oralytics app, are designed to supplement clinician instruction and consist of engaging content tailored to participants, such as brushing feedback and motivational messages. This paper describes the methodology used to design and develop the online RL algorithm. To make quality design decisions, we leveraged prior data, domain expertise, and experiments in a simulation test bed.

Artificial intelligence has dramatically reshaped our interaction with digital technologies, ushering in an era where advancements in AI algorithms and Large Language Models (LLMs) have natural language processing (NLP) systems like ChatGPT. This study delves into the impact of cutting-edge LLMs, notably OpenAI's ChatGPT, on medical diagnostics, with a keen focus on the dental sector. Leveraging publicly accessible datasets, these models augment the diagnostic capabilities of medical professionals, streamline communication between patients and healthcare providers, and enhance the efficiency of clinical procedures. The advent of ChatGPT-4 is poised to make substantial inroads into dental practices, especially in the realm of oral surgery. This paper sheds light on the current landscape and explores potential future research directions in the burgeoning field of LLMs, offering valuable insights for both practitioners and developers. Furthermore, it critically assesses the broad implications and challenges within various sectors, including academia and healthcare, thus mapping out an overview of AI's role in transforming dental diagnostics for enhanced patient care.