Machine Learning in Dentistry: A Scoping Review

Machine learning (ML) is being increasingly employed in dental research and application. We aimed to systematically compile studies using ML in dentistry and assess their methodological quality, including the risk of bias and reporting standards. We evaluated studies employing ML in dentistry published from 1 January 2015 to 31 May 2021 on MEDLINE, IEEE Xplore, and arXiv. We assessed publication trends and the distribution of ML tasks (classification, object detection, semantic segmentation, instance segmentation, and generation) in different clinical fields. We appraised the risk of bias and adherence to reporting standards, using the QUADAS-2 and TRIPOD checklists, respectively. Out of 183 identified studies, 168 were included, focusing on various ML tasks and employing a broad range of ML models, input data, data sources, strategies to generate reference tests, and performance metrics. Classification tasks were most common. Forty-two different metrics were used to evaluate model performances, with accuracy, sensitivity, precision, and intersection-over-union being the most common. We observed considerable risk of bias and moderate adherence to reporting standards which hampers replication of results. A minimum (core) set of outcome and outcome metrics is necessary to facilitate comparisons across studies

Automated permanent tooth detection and numbering on panoramic radiograph using a deep learning approach

Objective This study aimed to assess the performance of the deep learning (DL) model for automated tooth numbering in panoramic radiographs. Study Design The dataset of 500 panoramic images was selected according to the inclusion criteria and divided into training and testing data with a ratio of 80%:20%. Annotation on the data set was categorized into 32 classes based on the dental nomenclature of the universal numbering system using the LabelImg software. The training and testing process was carried out using You Only Look Once (YOLO) v4, a deep convolution neural network model for multiobject detection. The performance of YOLO v4 was evaluated using a confusion matrix. Furthermore, the detection time of YOLO v4 was compared with a certified radiologist using the Mann-Whitney test. Results The accuracy, precision, recall, and F1 scores of YOLO v4 for tooth detection and numbering in the panoramic radiograph were 88.5%, 87.70%, 100%, and 93.44%, respectively. The mean numbering time using YOLO v4 was 20.58 ± 0.29 ms, significantly faster than humans (P < .0001). Conclusions The DL approach using the YOLO v4 model can be used to assist dentists in daily practice by performing accurate and fast automated tooth detection and numbering on panoramic radiographs

Advanced Applications of Rapid Prototyping Technology in Modern Engineering

Rapid prototyping (RP) technology has been widely known and appreciated due to its flexible and customized manufacturing capabilities. The widely studied RP techniques include stereolithography apparatus (SLA), selective laser sintering (SLS), three-dimensional printing (3DP), fused deposition modeling (FDM), 3D plotting, solid ground curing (SGC), multiphase jet solidification (MJS), laminated object manufacturing (LOM). Different techniques are associated with different materials and/or processing principles and thus are devoted to specific applications. RP technology has no longer been only for prototype building rather has been extended for real industrial manufacturing solutions. Today, the RP technology has contributed to almost all engineering areas that include mechanical, materials, industrial, aerospace, electrical and most recently biomedical engineering. This book aims to present the advanced development of RP technologies in various engineering areas as the solutions to the real world engineering problems

Enhanced Computerized Surgical Planning System in Craniomaxillofacial Surgery

In the field of craniomaxillofacial (CMF) surgery, surgical planning is an important and necessary procedure due to the complex nature of the craniofacial skeleton. Computed tomography (CT) has brought about a revolution in virtual diagnosis, surgical planning and simulation, and evaluation of treatment outcomes. It provides high-quality 3D image and model of skull for Computer-aided surgical planning system (CSPS). During the planning process, one of the essential steps is to reestablish the dental occlusion. In the first project, a new approach is presented to automatically and efficiently reestablish dental occlusion. It includes two steps. The first step is to initially position the models based on dental curves and a point matching technique. The second step is to reposition the models to the final desired occlusion based on iterative surface-based minimum distance mapping with collision constraints. With linearization of rotation matrix, the alignment is modeled by solving quadratic programming. The simulation was completed on 12 sets of digital dental models. Two sets of dental models were partially edentulous, and another two sets have first premolar extractions for orthodontic treatment. Two validation methods were applied to the articulated models. The results show that using the proposed method, the dental models can be successfully articulated with a small degree of deviations from the occlusion achieved with the gold-standard method. Low contrast resolution in CBCT image has become its major limitation in building skull model. Intensive hand-segmentation is required to reconstruct the skull model. Thin bone images are particularly affected by this limitation. In the second project, a novel segmentation approach is presented based on wavelet active shape model (WASM) for a particular interest in the outer surface of the anterior wall of maxilla. 19 CBCT datasets are used to conduct two experiments. This model-based segmentation approach is validated and compared with three different segmentation approaches. The results show that the performance of this model-based segmentation approach is better than those of the other approaches. It can achieve 0.25 +/- 0.2mm of surface error distance from the ground truth of the bone surface. Field of view (FOV) can be reduced in order to reduce unnecessary radiation dose in CBCT. This ROI imaging is common in most of the dentomaxillofacial imaging and orthodontic practices. However, a truncation effect is created due to the truncation of projection images and becomes one of the limitation in CBCT. In the third project, a method for small region of interest (ROI) imaging and reconstruction of the image of ROI in CBCT and two experiments for measurement of dosage are presented. The first experiment shows at least 60% and 70% of radiation dose can be reduced. It also demonstrates that the image quality was still acceptable with little variation of gray by using the traditional truncation correction approach for ROI imaging. The second experiment demonstrates that the images reconstructed by CBCT reconstruction algorithms without truncation correction can be degraded to unacceptable image quality

Umjetna inteligencija u medicini i dentalnoj medicini

Introduction: Artificial intelligence has been applied in various fields throughout history, but its integration into daily life is more recent. The first applications of AI were primarily in academia and government research institutions, but as technology has advanced, AI has also been applied in industry, commerce, medicine and dentistry. Objective: Considering that the possibilities of applying artificial intelligence are developing rapidly and that this field is one of the areas with the greatest increase in the number of newly published articles, the aim of this paper was to provide an overview of the literature and to give an insight into the possibilities of applying artificial intelligence in medicine and dentistry. In addition, the aim was to discuss its advantages and disadvantages. Conclusion:The possibilities of applying artificial intelligence to medicine and dentistry are just being discovered. Artificial intelligence will greatly contribute to developments in medicine and dentistry, as it is a tool that enables development and progress, especially in terms of personalized healthcare that will lead to much better treatment outcomes.Uvod: Umjetna inteligencija (UI) primjenjivala se u prošlosti u različitim područjima, no njezina integracija u svakodnevni život novija je pojava. Najprije se koristila uglavnom u akademskim i vladinim istraživačkim ustanovama, no kako je tehnologija napredovala, počela se primjenjivati u industriji, trgovini, medicini i stomatologiji. Cilj: Uzimajući u obzir ubrzani razvoj i širenje primjene umjetne inteligencije i zato što je to jedno od područja s najvećim rastom kad je riječ o broju objavljenih članaka, svrha ovoga rada jest dati pregled literature i uvid u mogućnosti primjene umjetne inteligencije u području medicine i stomatologije, osobito s naglaskom na prednosti i nedostatke. Zaključak: Mogućnosti primjene umjetne inteligencije u medicini i stomatologiji tek se otkrivaju. Umjetna inteligencija važan je dio budućeg razvoja medicine i stomatologije jer je to oruđe koje osigurava razvoj i napredak, osobito kad je riječ o individualiziranoj zdravstvenoj skrbi koja obećava značajno poboljšane ishode liječenja

Recent Advances in Forensic Anthropological Methods and Research

Forensic anthropology, while still relatively in its infancy compared to other forensic science disciplines, adopts a wide array of methods from many disciplines for human skeletal identification in medico-legal and humanitarian contexts. The human skeleton is a dynamic tissue that can withstand the ravages of time given the right environment and may be the only remaining evidence left in a forensic case whether a week or decades old. Improved understanding of the intrinsic and extrinsic factors that modulate skeletal tissues allows researchers and practitioners to improve the accuracy and precision of identification methods ranging from establishing a biological profile such as estimating age-at-death, and population affinity, estimating time-since-death, using isotopes for geolocation of unidentified decedents, radiology for personal identification, histology to assess a live birth, to assessing traumatic injuries and so much more

Models and Analysis of Vocal Emissions for Biomedical Applications

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies

A Quantitative Genetic Analysis of Craniofacial Variation in Baboons

This dissertation is an explication of baboon craniofacial variation and its genetic basis. Intraspecific variation is the result of input from and complex interactions among genetic information, functional demands, and developmental processes. The relative effect of each of these on craniofacial variation, as well as the degree of inter-trait covariance, determines whether traits can respond to selection and what that response might look like. Using a sample of pedigreed baboons, I quantify craniofacial variation to address specific questions regarding the distribution and magnitude of phenotypic, genetic, and environmental variation patterns. In addition, I identify regions of the genome containing genetic variants contributing to the production of craniofacial variation. Results demonstrate that the genotype-phenotype map for craniofacial variation in this sample is characterized by patterns of inter-trait correlation that are structured by both functional and developmental relationships. Much of the additive genetic variation is likely pleiotropic and contributes to craniofacial variation regionally, rather than globally. The degree to which regions are affected by this genetic variation lacks patterning, indicating that no one particular region is any more evolvable than others. Finally, after accounting for differences in cranial size among individuals, both the magnitude of genetic correlations and the amount of additive genetic variation decreases, which suggests selection for body size played a major role in craniofacial evolution in baboons