Cephalometric landmark detection: Artificial intelligence vs human examination

Magister Scientiae Dentium - MSc(Dent)Cephalometric landmark detection is important for accurate diagnosis and treatment planning. The most common cause of random errors, in both computer-aided cephalometry and manual cephalometric analysis, is inconsistency in landmark detection. These methods are time-consuming. As a result, attempts have been made to automate cephalometric analysis, to improve the accuracy and precision of landmark detection whilst also minimizing errors caused by clinician subjectivity.This mini-thesis aimed to determine the precision of two cephalometric landmark identification methods, namely an artificial intelligence programme (BoneFinder®) and a computer-assisted examination software (Dolphin ImagingTM)

Odontology & artificial intelligence

Neste trabalho avaliam-se os três fatores que fizeram da inteligência artificial uma tecnologia essencial hoje em dia, nomeadamente para a odontologia: o desempenho do computador, Big Data e avanços algorítmicos. Esta revisão da literatura avaliou todos os artigos publicados na PubMed até Abril de 2019 sobre inteligência artificial e odontologia. Ajudado com inteligência artificial, este artigo analisou 1511 artigos. Uma árvore de decisão (If/Then) foi executada para selecionar os artigos mais relevantes (217), e um algoritmo de cluster k-means para resumir e identificar oportunidades de inovação. O autor discute os artigos mais interessantes revistos e compara o que foi feito em inovação durante o International Dentistry Show, 2019 em Colónia. Concluiu, assim, de forma crítica que há uma lacuna entre tecnologia e aplicação clínica desta, sendo que a inteligência artificial fornecida pela indústria de hoje pode ser considerada um atraso para o clínico de amanhã, indicando-se um possível rumo para a aplicação clínica da inteligência artificial.There are three factors that have made artificial intelligence (AI) an essential technology today: the computer performance, Big Data and algorithmic advances. This study reviews the literature on AI and Odontology based on articles retrieved from PubMed. With the help of AI, this article analyses a large number of articles (a total of 1511). A decision tree (If/Then) was run to select the 217 most relevant articles-. Ak-means cluster algorithm was then used to summarize and identify innovation opportunities. The author discusses the most interesting articles on AI research and compares them to the innovation presented during the International Dentistry Show 2019 in Cologne. Three technologies available now are evaluated and three suggested options are been developed. The author concludes that AI provided by the industry today is a hold-up for the praticioner of tomorrow. The author gives his opinion on how to use AI for the profit of patients

Clinically applicable artificial intelligence system for dental diagnosis with CBCT

Abstract In this study, a novel AI system based on deep learning methods was evaluated to determine its real-time performance of CBCT imaging diagnosis of anatomical landmarks, pathologies, clinical effectiveness, and safety when used by dentists in a clinical setting. The system consists of 5 modules: ROI-localization-module (segmentation of teeth and jaws), tooth-localization and numeration-module, periodontitis-module, caries-localization-module, and periapical-lesion-localization-module. These modules use CNN based on state-of-the-art architectures. In total, 1346 CBCT scans were used to train the modules. After annotation and model development, the AI system was tested for diagnostic capabilities of the Diagnocat AI system. 24 dentists participated in the clinical evaluation of the system. 30 CBCT scans were examined by two groups of dentists, where one group was aided by Diagnocat and the other was unaided. The results for the overall sensitivity and specificity for aided and unaided groups were calculated as an aggregate of all conditions. The sensitivity values for aided and unaided groups were 0.8537 and 0.7672 while specificity was 0.9672 and 0.9616 respectively. There was a statistically significant difference between the groups (p = 0.032). This study showed that the proposed AI system significantly improved the diagnostic capabilities of dentists

Artificial Intelligence in Oral Health

This Special Issue is intended to lay the foundation of AI applications focusing on oral health, including general dentistry, periodontology, implantology, oral surgery, oral radiology, orthodontics, and prosthodontics, among others

Medical Robotics

The first generation of surgical robots are already being installed in a number of operating rooms around the world. Robotics is being introduced to medicine because it allows for unprecedented control and precision of surgical instruments in minimally invasive procedures. So far, robots have been used to position an endoscope, perform gallbladder surgery and correct gastroesophogeal reflux and heartburn. The ultimate goal of the robotic surgery field is to design a robot that can be used to perform closed-chest, beating-heart surgery. The use of robotics in surgery will expand over the next decades without any doubt. Minimally Invasive Surgery (MIS) is a revolutionary approach in surgery. In MIS, the operation is performed with instruments and viewing equipment inserted into the body through small incisions created by the surgeon, in contrast to open surgery with large incisions. This minimizes surgical trauma and damage to healthy tissue, resulting in shorter patient recovery time. The aim of this book is to provide an overview of the state-of-art, to present new ideas, original results and practical experiences in this expanding area. Nevertheless, many chapters in the book concern advanced research on this growing area. The book provides critical analysis of clinical trials, assessment of the benefits and risks of the application of these technologies. This book is certainly a small sample of the research activity on Medical Robotics going on around the globe as you read it, but it surely covers a good deal of what has been done in the field recently, and as such it works as a valuable source for researchers interested in the involved subjects, whether they are currently “medical roboticists” or not

Label Efficient Deep Learning in Medical Imaging

Recent state-of-the-art deep learning frameworks require large, fully annotated training datasets that are, depending on the objective, time-consuming to generate. While in most fields, these labelling tasks can be parallelized massively or even outsourced, this is not the case for medical images. Usually, only a highly trained expert is able to generate these datasets. However, since additional manual annotation, especially for the purpose of segmentation or tracking, is typically not part of a radiologist's workflow, large and fully annotated datasets are a rare and scarce good. In this context, a variety of frameworks are proposed in this work to solve the problems that arise due to the lack of annotated training data across different medical imaging tasks and modalities. The first contribution as part of this thesis was to investigate weakly supervised learning on PET/CT data for the task of lesion segmentation. Using only class labels (tumor vs. no tumor), a classifier was first trained and subsequently used to generate Class Activation Maps highlighting regions with lesions. Based on these region proposals, final tumor segmentation could be performed with high accuracy in clinically relevant metrics. This drastically simplifies the process of training data generation, as only class labels have to be assigned to each slice of a scan instead of a full pixel-wise segmentation. To further reduce the time required to prepare training data, two self-supervised methods were investigated for the task of anatomical tissue segmentation and landmark detection. To this end, as a second contribution, a state-of-the-art tracking framework based on contrastive random walks was transferred, adapted and extended to the medical imaging domain. As contrastive learning often lacks real-time capability, a self-supervised template matching network was developed to address the task of real-time anatomical tissue tracking, yielding the third contribution of this work. Both of these methods have in common that only during inference the object or region of interest is defined, reducing the number of required labels to as few as one and allowing adaptation to different tasks without having to re-train or access the original training data. Despite the limited amount of labelled data, good results could be achieved for both tracking of organs across subjects as well as tissue tracking within time-series. State-of-the-art self-supervised learning in medical imaging is usually performed on 2D slices due to the lack of training data and limited computational resources. To exploit the three-dimensional structure of this type of data, self-supervised contrastive learning was performed on entire volumes using over 40,000 whole-body MRI scans forming the fourth contribution. Due to this pre-training, a large number of downstream tasks could be successfully addressed using only limited labelled data. Furthermore, the learned representations allows to visualize the entire dataset in a two-dimensional view. To encourage research in the field of automated lesion segmentation in PET/CT image data, the autoPET challenge was organized, which represents the fifth contribution