Machine Learning Engineer at Promaton Remote › 100% remote position (in European timezone) (Posted Mar 15 2021) About the company Promaton is changing dental healthcare by automating diagnostics and treatment workflows using AI, making healthcare more affordable and accessible for everyone. Did you know dentists miss up to 30% of pathologies on an X-Ray? We are on a mission to eliminate errors in dentistry by improving diagnostic accuracy, and automating mundane work like creating 3D models by hand from an X-Ray. Job description The models you make robust and performant are at the heart of the innovation we're bringing to the dental market, to raise the standard of healthcare Promaton is changing dental healthcare by automating diagnostics and treatment workflows using AI, making healthcare more affordable and accessible for everyone. Did you know dentists miss up to 30% of pathologies on an X-Ray?

Artificial intelligence (AI) encompasses a broad spectrum of emerging technologies that continue to influence daily life. The evolution of AI makes the analysis of big data possible, which provides reliable information and improves the decision-making process. Haitham Askar and associates from the Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Germany conducted this pilot study to apply deep learning to detect white spot lesions in dental photographs. Using 434 photographic images of 51 patients, a dataset of 2781 cropped tooth segments was generated. Pixelwise annotations of sound enamel as well as fluorotic, carious or other types of hypomineralized lesions were generated by experts and assessed by an independent second reviewer.

Dental caries is one of the most chronic diseases involving the majority of the population during their lifetime. Caries lesions are typically diagnosed by radiologists relying only on their visual inspection to detect via dental x-rays. In many cases, dental caries is hard to identify using x-rays and can be misinterpreted as shadows due to different reasons such as low image quality. Hence, developing a decision support system for caries detection has been a topic of interest in recent years. Here, we propose an automatic diagnosis system to detect dental caries in Panoramic images for the first time, to the best of authors' knowledge. The proposed model benefits from various pretrained deep learning models through transfer learning to extract relevant features from x-rays and uses a capsule network to draw prediction results. On a dataset of 470 Panoramic images used for features extraction, including 240 labeled images for classification, our model achieved an accuracy score of 86.05% on the test set. The obtained score demonstrates acceptable detection performance and an increase in caries detection speed, as long as the challenges of using Panoramic x-rays of real patients are taken into account. Among images with caries lesions in the test set, our model acquired recall scores of 69.44% and 90.52% for mild and severe ones, confirming the fact that severe caries spots are more straightforward to detect and efficient mild caries detection needs a more robust and larger dataset. Considering the novelty of current research study as using Panoramic images, this work is a step towards developing a fully automated efficient decision support system to assist domain experts. Dental caries, also known as tooth decay, is one of the most prevalent infectious chronic dental diseases in humans, affecting individuals throughout their lifetime [1]. According to the National Health and Nutrition Examination Survey, dental caries involves approximately 90% of adults in the United States [2], [3]. Dental caries is a dynamic disease procedure resulting from dental biofilm's metabolic activity, which gradually demineralizes enamel and dentine [4]. Tooth decay is a preventable disease, and if detected, can be stopped and potentially reversed in its early stages [5]. A standard tool for radiologists to distinguish dental diseases, such as caries, is x-ray radiography.

AI has been part of our imaginations and simmering in research labs since a handful of computer scientists rallied around the term at a Research Workshop at Dartmouth College in 1956 and birthed the field of AI. Back in 1956, the dream of AI pioneers such as John McCarthy was to construct complex machines that possessed characteristics of human intelligence. However, general AI machines that replicate human senses, human reasoning, and think as we do are still mostly constrained to Hollywood and science fiction novels. AI today is, however, able to perform specific, comparably narrow tasks as well as, or sometimes better than, we humans can. Examples of narrow AI include applications such as classification of pathology from X-ray imagery, identification of people in Facebook photos via facial recognition, or your spam filters in Gmail.

Clove Dental offers a comprehensive set of oral healthcare services, leverages best-in-class equipment, and utilizes the latest pain-management technology to provide affordable healthcare of the highest quality. To establish itself as the industry leader, Clove adheres to the highest standards in clinic safety and hygiene, customer service, and recruiting, with a constant focus on ethics and transparency. Vikas Sood is the Chief Information Officer at Clove Dental. In an interaction with The Tech Pod, Vikas speaks about the future of AI in healthcare. Tell us something about yourself and what does your company do?

Align Technology's Chief Digital Officer Sreelakshmi Kolli is using machine learning and DevOps tactics to power the company's digital transformation.

Modeling eye movement indicative of expertise behavior is decisive in user evaluation. However, it is indisputable that task semantics affect gaze behavior. We present a novel approach to gaze scanpath comparison that incorporates convolutional neural networks (CNN) to process scene information at the fixation level. Image patches linked to respective fixations are used as input for a CNN and the resulting feature vectors provide the temporal and spatial gaze information necessary for scanpath similarity comparison.We evaluated our proposed approach on gaze data from expert and novice dentists interpreting dental radiographs using a local alignment similarity score. Our approach was capable of distinguishing experts from novices with 93% accuracy while incorporating the image semantics. Moreover, our scanpath comparison using image patch features has the potential to incorporate task semantics from a variety of tasks

Know Your Stuff is a new column that unlocks the hidden secrets about the everyday products you own. Dental care has come a long way since we were first using bone and hog hair brushes in sixth-century China, but based on some of the raised eyebrows I've seen at the recent CES electronics show, some might argue that the pendulum has swung too far in the other direction. Oral-B and Colgate, two household names in oral hygiene, each released state-of-the-art toothbrushes that promise to get your teeth cleaner than a standard brush. They join the ranks of dozens of other "smart brushes" that sport a list of features rivaling some laptops, which of course begs the question, "Why?" Aren't we fine with toothbrushes as they already are? Vision of the future:Is your eye the next frontier for small screen tech?

Overjet is an early-stage VC-backed startup building the future of data-driven dentistry. We are using AI to transform the $130B dental care market and improve patient outcomes. We are seeking an entrepreneurially-minded a highly skilled developer who is comfortable with backend software development including deploying machine learning models, loves challenges and is passionate about impacting lives. Please email your resume to careers@overjet.ai. Develop machine learning pipelines Deploy machine learning models for inference Implement and maintain metrics for tracking ML models performance Design and develop microservices and APIs related to data ingestion, machine learning and product quality Ensuring responsiveness of applications.

AI in healthcare is now playing a life-sustaining role helping people to get the accurate treatment with timely diagnosis of various types of diseases. Similarly, machine learning in healthcare is becoming more imperative covering more types of disorders in the body helping people take precautions and well- timed treatments. Machine Learning (ML) in dentistry for dental image analysis is playing an important role to find out the conditions of teeth helping 2. doctors to recommend the right treatment. But there is more improvements required in this sub-field of healthcare sector. Actually, machine learning algorithms is lying under the hood of high-quality medical training data sets, and with further advances in parallel computing and augmentation of training data sets ML will improve the dental image analysis.