Enhancing dental caries classification in CBCT images by using image processing and self-supervised learning.

Diagnosing dental caries poses a significant challenge in dentistry, necessitating precise and early detection for effective management. This study utilizes Self-Supervised Learning (SSL) tasks to improve the classification of dental caries in Cone Beam Computed Tomography (CBCT) images, employing the International Caries Detection and Assessment System (ICDAS). Faced with the challenge of scarce annotated medical images, our research employs SSL to utilize unlabeled data, thereby improving model performance.

A Systematic Review on Caries Detection, Classification, and Segmentation from X-Ray Images: Methods, Datasets, Evaluation, and Open Opportunities.

Dental caries occurs from the interaction between oral bacteria and sugars, generating acids that damage teeth over time. The importance of X-ray images for detecting oral problems is undeniable in dentistry. With technological advances, it is feasible to identify these lesions using techniques such as deep learning, machine learning, and image processing.

Augmented Reality in Orthopedic Surgery Is Emerging from Proof of Concept Towards Clinical Studies: a Literature Review Explaining the Technology and Current State of the Art.

Purpose Of Review: Augmented reality (AR) is becoming increasingly popular in modern-day medicine. Computer-driven tools are progressively integrated into clinical and surgical procedures. The purpose of this review was to provide a comprehensive overview of the current technology and its challenges based on recent literature mainly focusing on clinical, cadaver, and innovative sawbone studies in the field of orthopedic surgery.

Down syndrome detection based on facial features using a geometric descriptor.

Down syndrome is one of the most common genetic disorders caused by chromosome abnormalities in humans. Among other physical characteristics, certain facial features are typically associated in people with Down syndrome. We investigate the problem of Down syndrome detection from a collection of face images.

Measuring Knowledge Acquisition in 3D Virtual Learning Environments.

Virtual environments can contribute to the effective learning of various subjects for people of all ages. Consequently, they assist in reducing the cost of maintaining physical structures of teaching, such as laboratories and classrooms. However, the measurement of how learners acquire knowledge in such environments is still incipient in the literature.

Building a open source framework for virtual medical training.

This paper presents a framework to build medical training applications by using virtual reality and a tool that helps the class instantiation of this framework. The main purpose is to make easier the building of virtual reality applications in the medical training area, considering systems to simulate biopsy exams and make available deformation, collision detection, and stereoscopy functionalities. The instantiation of the classes allows quick implementation of the tools for such a purpose, thus reducing errors and offering low cost due to the use of open source tools.