4,040 research outputs found
The Requirements for Ontologies in Medical Data Integration: A Case Study
Evidence-based medicine is critically dependent on three sources of
information: a medical knowledge base, the patients medical record and
knowledge of available resources, including where appropriate, clinical
protocols. Patient data is often scattered in a variety of databases and may,
in a distributed model, be held across several disparate repositories.
Consequently addressing the needs of an evidence-based medicine community
presents issues of biomedical data integration, clinical interpretation and
knowledge management. This paper outlines how the Health-e-Child project has
approached the challenge of requirements specification for (bio-) medical data
integration, from the level of cellular data, through disease to that of
patient and population. The approach is illuminated through the requirements
elicitation and analysis of Juvenile Idiopathic Arthritis (JIA), one of three
diseases being studied in the EC-funded Health-e-Child project.Comment: 6 pages, 1 figure. Presented at the 11th International Database
Engineering & Applications Symposium (Ideas2007). Banff, Canada September
200
Pattern classification approaches for breast cancer identification via MRI: state‐of‐the‐art and vision for the future
Mining algorithms for Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCEMRI)
of breast tissue are discussed. The algorithms are based on recent advances in multidimensional
signal processing and aim to advance current state‐of‐the‐art computer‐aided detection
and analysis of breast tumours when these are observed at various states of development. The topics
discussed include image feature extraction, information fusion using radiomics, multi‐parametric
computer‐aided classification and diagnosis using information fusion of tensorial datasets as well
as Clifford algebra based classification approaches and convolutional neural network deep learning
methodologies. The discussion also extends to semi‐supervised deep learning and self‐supervised
strategies as well as generative adversarial networks and algorithms using generated
confrontational learning approaches. In order to address the problem of weakly labelled tumour
images, generative adversarial deep learning strategies are considered for the classification of
different tumour types. The proposed data fusion approaches provide a novel Artificial Intelligence
(AI) based framework for more robust image registration that can potentially advance the early
identification of heterogeneous tumour types, even when the associated imaged organs are
registered as separate entities embedded in more complex geometric spaces. Finally, the general
structure of a high‐dimensional medical imaging analysis platform that is based on multi‐task
detection and learning is proposed as a way forward. The proposed algorithm makes use of novel
loss functions that form the building blocks for a generated confrontation learning methodology
that can be used for tensorial DCE‐MRI. Since some of the approaches discussed are also based on
time‐lapse imaging, conclusions on the rate of proliferation of the disease can be made possible. The
proposed framework can potentially reduce the costs associated with the interpretation of medical
images by providing automated, faster and more consistent diagnosis
Quantitative analysis with machine learning models for multi-parametric brain imaging data
Gliomas are considered to be the most common primary adult malignant brain tumor. With the dramatic increases in computational power and improvements in image analysis algorithms, computer-aided medical image analysis has been introduced into clinical applications. Precision tumor grading and genotyping play an indispensable role in clinical diagnosis, treatment and prognosis. Gliomas diagnostic procedures include histopathological imaging tests, molecular imaging scans and tumor grading. Pathologic review of tumor morphology in histologic sections is the traditional method for cancer classification and grading, yet human study has limitations that can result in low reproducibility and inter-observer agreement. Compared with histopathological images, Magnetic resonance (MR) imaging present the different structure and functional features, which might serve as noninvasive surrogates for tumor genotypes. Therefore, computer-aided image analysis has been adopted in clinical application, which might partially overcome these shortcomings due to its capacity to quantitatively and reproducibly measure multilevel features on multi-parametric medical information. Imaging features obtained from a single modal image do not fully represent the disease, so quantitative imaging features, including morphological, structural, cellular and molecular level features, derived from multi-modality medical images should be integrated into computer-aided medical image analysis. The image quality differentiation between multi-modality images is a challenge in the field of computer-aided medical image analysis. In this thesis, we aim to integrate the quantitative imaging data obtained from multiple modalities into mathematical models of tumor prediction response to achieve additional insights into practical predictive value. Our major contributions in this thesis are: 1. Firstly, to resolve the imaging quality difference and observer-dependent in histological image diagnosis, we proposed an automated machine-learning brain tumor-grading platform to investigate contributions of multi-parameters from multimodal data including imaging parameters or features from Whole Slide Images (WSI) and the proliferation marker KI-67. For each WSI, we extract both visual parameters such as morphology parameters and sub-visual parameters including first-order and second-order features. A quantitative interpretable machine learning approach (Local Interpretable Model-Agnostic Explanations) was followed to measure the contribution of features for single case. Most grading systems based on machine learning models are considered “black boxes,” whereas with this system the clinically trusted reasoning could be revealed. The quantitative analysis and explanation may assist clinicians to better understand the disease and accordingly to choose optimal treatments for improving clinical outcomes. 2. Based on the automated brain tumor-grading platform we propose, multimodal Magnetic Resonance Images (MRIs) have been introduced in our research. A new imaging–tissue correlation based approach called RA-PA-Thomics was proposed to predict the IDH genotype. Inspired by the concept of image fusion, we integrate multimodal MRIs and the scans of histopathological images for indirect, fast, and cost saving IDH genotyping. The proposed model has been verified by multiple evaluation criteria for the integrated data set and compared to the results in the prior art. The experimental data set includes public data sets and image information from two hospitals. Experimental results indicate that the model provided improves the accuracy of glioma grading and genotyping
Medical imaging analysis with artificial neural networks
Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging
Prospects for Theranostics in Neurosurgical Imaging: Empowering Confocal Laser Endomicroscopy Diagnostics via Deep Learning
Confocal laser endomicroscopy (CLE) is an advanced optical fluorescence
imaging technology that has the potential to increase intraoperative precision,
extend resection, and tailor surgery for malignant invasive brain tumors
because of its subcellular dimension resolution. Despite its promising
diagnostic potential, interpreting the gray tone fluorescence images can be
difficult for untrained users. In this review, we provide a detailed
description of bioinformatical analysis methodology of CLE images that begins
to assist the neurosurgeon and pathologist to rapidly connect on-the-fly
intraoperative imaging, pathology, and surgical observation into a
conclusionary system within the concept of theranostics. We present an overview
and discuss deep learning models for automatic detection of the diagnostic CLE
images and discuss various training regimes and ensemble modeling effect on the
power of deep learning predictive models. Two major approaches reviewed in this
paper include the models that can automatically classify CLE images into
diagnostic/nondiagnostic, glioma/nonglioma, tumor/injury/normal categories and
models that can localize histological features on the CLE images using weakly
supervised methods. We also briefly review advances in the deep learning
approaches used for CLE image analysis in other organs. Significant advances in
speed and precision of automated diagnostic frame selection would augment the
diagnostic potential of CLE, improve operative workflow and integration into
brain tumor surgery. Such technology and bioinformatics analytics lend
themselves to improved precision, personalization, and theranostics in brain
tumor treatment.Comment: See the final version published in Frontiers in Oncology here:
https://www.frontiersin.org/articles/10.3389/fonc.2018.00240/ful
A computer-aided diagnosis system for glioma grading using three dimensional texture analysis and machine learning in MRI brain tumour
Glioma grading is vital for therapeutic planning where the higher level of glioma is associated with high mortality. It is a challenging task as different glioma grades have mixed morphological characteristics of brain tumour. A computer-aided diagnosis (CAD) system based on three-dimensional textural grey level co-occurrence matrix (GLCM) and machine learning is proposed for glioma grading. The purpose of this paper is to assess the usefulness of the 3D textural analysis in establishing a malignancy prediction model for glioma grades. Furthermore, this paper aims to find the best classification model based on textural analysis for glioma grading. The classification system was evaluated using leave-one-out cross-validation technique. The experimental design includes feature extraction, feature selection, and finally the classification that includes single and ensemble classification models in a comparative study. Experimental results illustrate that single and ensemble classification models, can achieve efficient prediction performance based on 3D textural analysis and the classification accuracy result has significantly improved after using feature selection methods. In this paper, we compare the proficiency of applying different angles of 3D textural analysis and different classification models to determine the malignant level of glioma. The obtained sensitivity, accuracy and specificity are 100%, 96.6%, 90% respectively. The prediction system presents an effective approach to assess the malignancy level of glioma with a non-invasive, reproducible and accurate CAD system for glioma grading
Искусственный интеллект при колоректальном раке: обзор
The study objective: the study objective is to examine the use of artificial intelligence (AI) in the diagnosis, treatment, and prognosis of Colorectal Cancer (CRC) and discuss the future potential of AI in CRC. Material and Methods. The Web of Science, Scopus, PubMed, Medline, and eLIBRARY databases were used to search for the publications. A study on the application of Artificial Intelligence (AI) to the diagnosis, treatment, and prognosis of Colorectal Cancer (CRC) was discovered in more than 100 sources. In the review, data from 83 articles were incorporated. Results. The review article explores the use of artificial intelligence (AI) in medicine, specifically focusing on its applications in colorectal cancer (CRC). It discusses the stages of AI development for CRC, including molecular understanding, image-based diagnosis, drug design, and individualized treatment. The benefits of AI in medical image analysis are highlighted, improving diagnosis accuracy and inspection quality. Challenges in AI development are addressed, such as data standardization and the interpretability of machine learning algorithms. The potential of AI in treatment decision support, precision medicine, and prognosis prediction is discussed, emphasizing the role of AI in selecting optimal treatments and improving surgical precision. Ethical and regulatory considerations in integrating AI are mentioned, including patient trust, data security, and liability in AI-assisted surgeries. The review emphasizes the importance of an AI standard system, dataset standardization, and integrating clinical knowledge into AI algorithms. Overall, the article provides an overview of the current research on AI in CRC diagnosis, treatment, and prognosis, discussing its benefits, challenges, and future prospects in improving medical outcomes.Цель исследования - оценка возможностей использования искусственного интеллекта (ИИ) в диагностике, лечении и прогнозировании колоректального рака (КРР), а также обсуждение потенциала ИИ в лечении КРР. Материал и методы. Проведен поиск научных публикаций в поисковых системах Web of Science, Scopus, PubMed, Medline и eLIBRARY. Было просмотрено более 100 источников по применению ИИ для диагностики, лечения и прогнозирования КРР. В обзор включены данные из 83 статей. Результаты. Проведен анализ литературы, посвященной применению искусственного интеллекта в медицине, особое внимание уделено его использованию при колоректальном раке. Обсуждаются этапы развития ИИ при КРР, включая молекулярную верификацию, лучевую диагностику, разработку лекарств и индивидуальное лечение. Подчеркнуты преимущества ИИ в анализе медицинских изображений, таких как КТ, МРТ и ПЭТ, что повышает точность диагностики. Рассматриваются такие проблемы развития ИИ, как стандартизация данных и интерпретируемость алгоритмов машинного обучения. Подчеркивается роль ИИ в выборе оптимальной тактики лечения и повышении эффективности хирургического вмешательства. Учитываются этические и нормативные аспекты ИИ, включая доверие пациентов, безопасность данных и ответственность в проведении операций с использованием ИИ. Обсуждаются преимущества ИИ в диагностике, лечении и прогнозировании колоректального рака, проблемы и перспективы улучшения результатов лечения
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