13,500 research outputs found
Odontology & artificial intelligence
Neste trabalho avaliam-se os trĂŞs fatores que fizeram da inteligĂŞncia artificial uma tecnologia essencial hoje em dia, nomeadamente para a odontologia: o desempenho do computador, Big Data e avanços algorĂtmicos. Esta revisĂŁo da literatura avaliou todos os artigos publicados na PubMed atĂ© Abril de 2019 sobre inteligĂŞncia artificial e odontologia. Ajudado com inteligĂŞncia artificial, este artigo analisou 1511 artigos. Uma árvore de decisĂŁo (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 ColĂłnia. Concluiu, assim, de forma crĂtica que há uma lacuna entre tecnologia e aplicação clĂnica desta, sendo que a inteligĂŞncia artificial fornecida pela indĂşstria de hoje pode ser considerada um atraso para o clĂnico de amanhĂŁ, indicando-se um possĂvel rumo para a aplicação clĂnica da inteligĂŞncia 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
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
A Survey on Artificial Intelligence Techniques for Biomedical Image Analysis in Skeleton-Based Forensic Human Identification
This paper represents the first survey on the application of AI techniques for the analysis
of biomedical images with forensic human identification purposes. Human identification is of
great relevance in today’s society and, in particular, in medico-legal contexts. As consequence,
all technological advances that are introduced in this field can contribute to the increasing necessity
for accurate and robust tools that allow for establishing and verifying human identity. We first
describe the importance and applicability of forensic anthropology in many identification scenarios.
Later, we present the main trends related to the application of computer vision, machine learning
and soft computing techniques to the estimation of the biological profile, the identification through
comparative radiography and craniofacial superimposition, traumatism and pathology analysis,
as well as facial reconstruction. The potentialities and limitations of the employed approaches are
described, and we conclude with a discussion about methodological issues and future research.Spanish Ministry of Science, Innovation and UniversitiesEuropean Union (EU)
PGC2018-101216-B-I00Regional Government of Andalusia under grant EXAISFI
P18-FR-4262Instituto de Salud Carlos IIIEuropean Union (EU)
DTS18/00136European Commission H2020-MSCA-IF-2016 through the Skeleton-ID Marie Curie Individual Fellowship
746592Spanish Ministry of Science, Innovation and Universities-CDTI, Neotec program 2019
EXP-00122609/SNEO-20191236European Union (EU)Xunta de Galicia
ED431G 2019/01European Union (EU)
RTI2018-095894-B-I0
Automatic Segmentation of the Mandible for Three-Dimensional Virtual Surgical Planning
Three-dimensional (3D) medical imaging techniques have a fundamental role in the field of oral and maxillofacial surgery (OMFS). 3D images are used to guide diagnosis, assess the severity of disease, for pre-operative planning, per-operative guidance and virtual surgical planning (VSP). In the field of oral cancer, where surgical resection requiring the partial removal of the mandible is a common treatment, resection surgery is often based on 3D VSP to accurately design a resection plan around tumor margins. In orthognathic surgery and dental implant surgery, 3D VSP is also extensively used to precisely guide mandibular surgery. Image segmentation from the radiography images of the head and neck, which is a process to create a 3D volume of the target tissue, is a useful tool to visualize the mandible and quantify geometric parameters. Studies have shown that 3D VSP requires accurate segmentation of the mandible, which is currently performed by medical technicians. Mandible segmentation was usually done manually, which is a time-consuming and poorly reproducible process. This thesis presents four algorithms for mandible segmentation from CT and CBCT and contributes to some novel ideas for the development of automatic mandible segmentation for 3D VSP. We implement the segmentation approaches on head and neck CT/CBCT datasets and then evaluate the performance. Experimental results show that our proposed approaches for mandible segmentation in CT/CBCT datasets exhibit high accuracy
Advanced Computational Methods for Oncological Image Analysis
[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.
Magnetic resonance imaging of obesity and metabolic disorders: Summary from the 2019 ISMRM Workshop
More than 100 attendees from Australia, Austria, Belgium, Canada, China, Germany, Hong Kong, Indonesia, Japan, Malaysia, the Netherlands, the Philippines, Republic of Korea, Singapore, Sweden, Switzerland, the United Kingdom, and the United States convened in Singapore for the 2019 ISMRM-sponsored workshop on MRI of Obesity and Metabolic Disorders. The scientific program brought together a multidisciplinary group of researchers, trainees, and clinicians and included sessions in diabetes and insulin resistance; an update on recent advances in water–fat MRI acquisition and reconstruction methods; with applications in skeletal muscle, bone marrow, and adipose tissue quantification; a summary of recent findings in brown adipose tissue; new developments in imaging fat in the fetus, placenta, and neonates; the utility of liver elastography in obesity studies; and the emerging role of radiomics in population-based “big data” studies. The workshop featured keynote presentations on nutrition, epidemiology, genetics, and exercise physiology. Forty-four proffered scientific abstracts were also presented, covering the topics of brown adipose tissue, quantitative liver analysis from multiparametric data, disease prevalence and population health, technical and methodological developments in data acquisition and reconstruction, newfound applications of machine learning and neural networks, standardization of proton density fat fraction measurements, and X-nuclei applications. The purpose of this article is to summarize the scientific highlights from the workshop and identify future directions of work
Post hoc Explanations may be Ineffective for Detecting Unknown Spurious Correlation
We investigate whether three types of post hoc model explanations--feature
attribution, concept activation, and training point ranking--are effective for
detecting a model's reliance on spurious signals in the training data.
Specifically, we consider the scenario where the spurious signal to be detected
is unknown, at test-time, to the user of the explanation method. We design an
empirical methodology that uses semi-synthetic datasets along with
pre-specified spurious artifacts to obtain models that verifiably rely on these
spurious training signals. We then provide a suite of metrics that assess an
explanation method's reliability for spurious signal detection under various
conditions. We find that the post hoc explanation methods tested are
ineffective when the spurious artifact is unknown at test-time especially for
non-visible artifacts like a background blur. Further, we find that feature
attribution methods are susceptible to erroneously indicating dependence on
spurious signals even when the model being explained does not rely on spurious
artifacts. This finding casts doubt on the utility of these approaches, in the
hands of a practitioner, for detecting a model's reliance on spurious signals
Computational Anatomy for Multi-Organ Analysis in Medical Imaging: A Review
The medical image analysis field has traditionally been focused on the
development of organ-, and disease-specific methods. Recently, the interest in
the development of more 20 comprehensive computational anatomical models has
grown, leading to the creation of multi-organ models. Multi-organ approaches,
unlike traditional organ-specific strategies, incorporate inter-organ relations
into the model, thus leading to a more accurate representation of the complex
human anatomy. Inter-organ relations are not only spatial, but also functional
and physiological. Over the years, the strategies 25 proposed to efficiently
model multi-organ structures have evolved from the simple global modeling, to
more sophisticated approaches such as sequential, hierarchical, or machine
learning-based models. In this paper, we present a review of the state of the
art on multi-organ analysis and associated computation anatomy methodology. The
manuscript follows a methodology-based classification of the different
techniques 30 available for the analysis of multi-organs and multi-anatomical
structures, from techniques using point distribution models to the most recent
deep learning-based approaches. With more than 300 papers included in this
review, we reflect on the trends and challenges of the field of computational
anatomy, the particularities of each anatomical region, and the potential of
multi-organ analysis to increase the impact of 35 medical imaging applications
on the future of healthcare.Comment: Paper under revie
Wearable Fall Detector Using Recurrent Neural Networks
Falls have become a relevant public health issue due to their high prevalence and negative
effects in elderly people. Wearable fall detector devices allow the implementation of continuous
and ubiquitous monitoring systems. The effectiveness for analyzing temporal signals with low
energy consumption is one of the most relevant characteristics of these devices. Recurrent neural
networks (RNNs) have demonstrated a great accuracy in some problems that require analyzing
sequential inputs. However, getting appropriate response times in low power microcontrollers
remains a difficult task due to their limited hardware resources. This work shows a feasibility study
about using RNN-based deep learning models to detect both falls and falls’ risks in real time using
accelerometer signals. The effectiveness of four different architectures was analyzed using the SisFall
dataset at different frequencies. The resulting models were integrated into two different embedded
systems to analyze the execution times and changes in the model effectiveness. Finally, a study of
power consumption was carried out. A sensitivity of 88.2% and a specificity of 96.4% was obtained.
The simplest models reached inference times lower than 34 ms, which implies the capability to
detect fall events in real-time with high energy efficiency. This suggests that RNN models provide
an effective method that can be implemented in low power microcontrollers for the creation of
autonomous wearable fall detection systems in real-time
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