Applications of Machine Learning in Periodontology and Implantology: A Comprehensive Review.

Machine learning (ML) has led to significant advances in dentistry, easing the workload of professionals and improving the performance of various medical processes. The fields of periodontology and implantology can profit from these advances for tasks such as determining periodontally compromised teeth, assisting doctors in the implant planning process, determining types of implants, or predicting the occurrence of peri-implantitis. The current paper provides an overview of recent ML techniques applied in periodontology and implantology, aiming to identify popular models for different medical tasks, to assess the impact of the training data on the success of the automatic algorithms and to highlight advantages and disadvantages of various approaches. 48 original research papers, published between 2016 and 2023, were selected and divided into four classes: periodontology, implant planning, implant brands and types, and success of dental implants. These papers were analyzed in terms of aim, technical details, characteristics of training and testing data, results, and medical observations. The purpose of this paper is not to provide an exhaustive survey, but to show representative methods from recent literature that highlight the advantages and disadvantages of various approaches, as well as the potential of applying machine learning in dentistry.

A review of sonification solutions in assistive systems for visually impaired people.

PURPOSE: Visually impaired people (VIP) find it challenging to understand and gain awareness of their surroundings. Most activities require the use of the auditory or tactile senses. As such, assistive systems which are capable of aiding visually impaired people to understand, navigate and form a mental representation of their environment are extensively being studied and developed. The aim of this paper is to provide insight regarding the characteristics, as well as the advantages and drawbacks of different types of sonification strategies in assistive systems, to assess their suitability for certain use-cases. MATERIALS AND METHODS: To this end, we reviewed a sizeable number of assistive solutions for VIP which provide a form of auditory feedback to the user, encountered in different scientific databases (Scopus, IEEE Xplore, ACM and Google Scholar) through direct searches and cross-referencing. RESULTS: We classified these solutions based on the aural information they provide to the VIP - alerts, guidance and information about their environment, be it spatial or semantic. Our intention is not to provide an exhaustive review, but to select representative implementations from recent literature that highlight the particularities of each sonification approach. CONCLUSIONS: Thus, anyone who is intent on developing an assistive solution will be able to choose the desired sonification class, being aware of the advantages/disadvantages and at the same time having a fairly wide selection of articles from the representative class.

The motivation behind this paper is to provide an overview of sonification strategies in the context of assistive systems for the visually impaired people.Whilst surveys and reviews which provide in-depth insights into assistive technologies and sonification exist, papers which provide a combined view of these topics are rather lacking.The analysis of the selected papers provides insight regarding the characteristics of different types of sonification strategies in assistive systems for visually impaired people and their suitability for certain use-cases.

CultReal-A Rapid Development Platform for AR Cultural Spaces, with Fused Localization.

Virtual and augmented reality technologies have known an impressive market evolution due to their potential to provide immersive experiences. However, they still have significant difficulties to enable fully fledged, consumer-ready applications that can handle complex tasks such as multi-user collaboration or time-persistent experiences. In this context, CultReal is a rapid creation and deployment platform for augmented reality (AR), aiming to revitalize cultural spaces. The platform's content management system stores a representation of the environment, together with a database of multimedia objects that can be associated with a location. The localization component fuses data from beacons and from video cameras, providing an accurate estimation of the position and orientation of the visitor's smartphone. A mobile application running the localization component displays the augmented content, which is seamlessly integrated with the real world. The paper focuses on the series of steps required to compute the position and orientation of the user's mobile device, providing a comprehensive evaluation with both virtual and real data. Pilot implementations of the system are also described in the paper, revealing the potential of the platform to enable rapid deployment in new cultural spaces. Offering these functionalities, CultReal will allow for the fast development of AR solutions in any location.

Semi-automatic vessel detection for challenging cases of peripheral arterial disease.

OBJECTIVES: Manual or semi-automated segmentation of the lower extremity arterial tree in patients with Peripheral arterial disease (PAD) remains a notoriously difficult and time-consuming task. The complex manifestations of the disease, including discontinuities of the vascular flow channels, the presence of calcified atherosclerotic plaque in close vicinity to adjacent bone, and the presence of metal or other imaging artifacts currently preclude fully automated vessel identification. New machine learning techniques may alleviate this challenge, but require large and reasonably well segmented training data. METHODS: We propose a novel semi-automatic vessel tracking approach for peripheral arteries to facilitate and accelerate the creation of annotated training data by expert cardiovascular radiologists or technologists, while limiting the number of necessary manual interactions, and reducing processing time. After automatically classifying blood vessels, bones, and other tissue, the relevant vessels are tracked and organized in a tree-like structure for further visualization. RESULTS: We conducted a pilot (N = 9) and a clinical study (N = 24) in which we assess the accuracy and required time for our approach to achieve sufficient quality for clinical application, with our current clinically established workflow as the standard of reference. Our approach enabled expert physicians to readily identify all clinically relevant lower extremity arteries, even in problematic cases, with an average sensitivity of 92.9%, and an average specificity and overall accuracy of 99.9%. CONCLUSIONS: Compared to the clinical workflow in our collaborating hospitals (28:40 ± 7:45 [mm:ss]), our approach (17:24 ± 6:44 [mm:ss]) is on average 11:16 [mm:ss] (39%) faster.

A Comprehensive Survey of Indoor Localization Methods Based on Computer Vision.

Computer vision based indoor localization methods use either an infrastructure of static cameras to track mobile entities (e.g., people, robots) or cameras attached to the mobile entities. Methods in the first category employ object tracking, while the others map images from mobile cameras with images acquired during a configuration stage or extracted from 3D reconstructed models of the space. This paper offers an overview of the computer vision based indoor localization domain, presenting application areas, commercial tools, existing benchmarks, and other reviews. It provides a survey of indoor localization research solutions, proposing a new classification based on the configuration stage (use of known environment data), sensing devices, type of detected elements, and localization method. It groups 70 of the most recent and relevant image based indoor localization methods according to the proposed classification and discusses their advantages and drawbacks. It highlights localization methods that also offer orientation information, as this is required by an increasing number of applications of indoor localization (e.g., augmented reality).