Fast and automated biomarker detection in breath samples with machine learning
Loughborough University（拉夫堡大学）；University of Edinburgh（爱丁堡大学）；NHS Lothian（NHS洛锡安）；Western General Hospital（西部综合医院）
Angelika Skarysz, Dahlia Salman, Michael Eddleston, Martin Sykora, Eugenie Hunsicker, William H Nailon, Kareen Darnley, Duncan B McLaren, C L Paul Thomas, Andrea Soltoggio
Volatile organic compounds (VOCs) in human breath can reveal a large spectrum of health conditions and can be used for fast, accurate and non-invasive diagnostics. Gas chromatography-mass spectrometry (GC-MS) is used to measure VOCs, but its application is limited by expert-driven data analysis that is time-consuming, subjective and may introduce errors. We propose a system to perform GC-MS data analysis that exploits deep learning pattern recognition ability to learn and automatically detect VOCs directly from raw data, thus bypassing expert-led processing. The new proposed approach showed to outperform the expert-led analysis by detecting a significantly higher number of VOCs in just a fraction of time while maintaining high specificity. These results suggest that the proposed method can help the large-scale deployment of breath-based diagnosis by reducing time and cost, and increasing accuracy and consistency.
Surgical resections are the most prevalent curative treatment for primary liver cancer. Tumors located in critical positions are known to complexify liver resections (LR). While experienced surgeons in specialized medical centers may have the necessary expertise to accurately anticipate LR complexity, and prepare accordingly, an objective method able to reproduce this behavior would have the potential to improve the standard routine of care, and avoid intra- and postoperative complications. In this article, we propose CoRe, an automated medical image processing pipeline for the prediction of p…
Budapest University of Technology and Economics（布达佩斯技术与经济大学）
A major challenge in medical image analysis is the automated detection of biomarkers from neuroimaging data. Traditional approaches, often based on image registration, are limited in capturing the high variability of cortical organisation across individuals. Deep learning methods have been shown to be successful in overcoming this difficulty, and some of them have even outperformed medical professionals on certain datasets. In this paper, we apply a deep neural network to analyse the cortical surface data of neonates, derived from the publicly available Developing Human Connectome Project (dHC…
Real-time deep learning phase imaging flow cytometer reveals blood cell aggregate biomarkers for haematology diagnostics
Technical University of Munich（慕尼黑工业大学）；TUMCREATE；Bioinformatics Institute, Agency for Science, Technology and Research（科技研究局生物信息研究所）；TUM University Hospital（TUM大学医院）；National University of Singapore（新加坡国立大学）；National University Hospital（新加坡国立大学医院）；German Center for Infection Research (DZIF)（德国感染研究中心）；Infectious Diseases Labs (AIDL), Agency for Science, Technology and Research（科技研究局传染病实验室）；Nanyang Technological University（南洋理工大学）；Centre for Frontier AI Research, Agency for Science, Technology and Research（科技研究局前沿人工智能研究中心）
Circulating Tumor Cells (CTCs) bear great promise as biomarkers in tumor prognosis. However, the process of identification and later enumeration of CTCs require manual labor, which is error-prone and time-consuming. The recent developments in object detection via Deep Learning using Mask-RCNNs and wider availability of pre-trained models have enabled sensitive tasks with limited data of such to be tackled with unprecedented accuracy. In this report, we present a novel 3-stage detection model for automated identification of Circulating Tumor Cells in multi-channel darkfield microscopic images c…
Heidelberg University Hospital（海德堡大学医院）；German Cancer Research Center (DKFZ)（德国癌症研究中心）；HIDSS4Health - Helmholtz Information and Data Science School for Health（亥姆霍兹健康信息与数据科学学校）；Heidelberg University（海德堡大学）；University Hospital Essen（埃森大学医院）；University Duisburg-Essen（杜伊斯堡-埃森大学）
Sepsis is a leading cause of mortality and critical illness worldwide. While robust biomarkers for early diagnosis are still missing, recent work indicates that hyperspectral imaging (HSI) has the potential to overcome this bottleneck by monitoring microcirculatory alterations. Automated machine learning-based diagnosis of sepsis based on HSI data, however, has not been explored to date. Given this gap in the literature, we leveraged an existing data set to (1) investigate whether HSI-based automated diagnosis of sepsis is possible and (2) put forth a list of possible confounders relevant for …
Predicting Lethal Outcome (Cause) And Understanding Key Biomarkers Linked With Acute Myocardial Infarction Using Deep Artificial Neural Network And Ensemble Of Machine Learning Methodologies
National Heart and Lung Institute, Imperial College London（帝国理工学院国家心肺研究所）；Imperial College London（帝国理工学院）；Royal Brompton Hospital（皇家布朗普顿医院）
Temporal patterns of cardiac motion provide important information for cardiac disease diagnosis. This pattern could be obtained by three-directional CINE multi-slice left ventricular myocardial velocity mapping (3Dir MVM), which is a cardiac MR technique providing magnitude and phase information of the myocardial motion simultaneously. However, long acquisition time limits the usage of this technique by causing breathing artifacts, while shortening the time causes low temporal resolution and may provide an inaccurate assessment of cardiac motion. In this study, we proposed a frame synthesis al…
Automated Erythrocyte Detection and Tracking for Retinal Blood Flow Quantification in Erythrocyte-Mediated Angiography
Department of Bioengineering, University of Maryland, College Park, MD 20742, USA（生物工程系，马里兰大学，学院公园，MD 20742，美国）；Department of Ophthalmology and Visual Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA（眼科学与视觉科学系，马里兰大学医学院，巴尔的摩，MD 21201，美国）；Department of Electrical and Computer Engineering, University of Maryland, College Park, MD 20742, USA（电气与计算机工程系，马里兰大学，学院公园，MD 20742，美国）
A Multicentric Dataset for Training and Benchmarking Breast Cancer Segmentation in H&E Slides
Department of Pathology, Radboud University Medical Center（病理学系，拉德堡德大学医学中心）；Fondazione Bruno Kessler（布鲁诺·凯塞勒基金会）；Department of Cellular, Computational and Integrative Biology, University of Trento（细胞、计算与整合生物学系，特伦托大学）；Biopticka Laboratory Ltd.（Biopticka实验室有限公司）；Department of Pathology, Faculty of Medicine in Pilsen, Charles University（病理学系，皮尔森医学院，查尔斯大学）；University of Washington Medical Center（华盛顿大学医学中心）；Department of Surgical Pathology, Santa Chiara Hospital, APSS（外科病理学系，圣查理医院，APSS）；CISMed - Centre for Medical Sciences, University of Trento（CISMed医学研究中心，特伦托大学）；Pathology Unit, University and Hospital Trust of Verona（病理学部，韦罗纳大学与医院信托）；IRCCS Sacro Cuore Don Calabria Hospital（IRCCS圣心多玛利亚医院）
Automated interictal epileptic spike detection from simple and noisy annotations in MEG data
Université Claude Bernard Lyon 1（里昂第一大学）；CNRS（法国国家科学研究中心）；INSERM（法国国家健康与医学研究院）；Centre de Recherche en Neurosciences de Lyon CRNL（里昂神经科学研究中心）；EDUWELL；Hospices Civils de Lyon（里昂公民医院）
Cine cardiac magnetic resonance (CMR) imaging is considered the gold standard for cardiac function evaluation. However, cine CMR acquisition is inherently slow and in recent decades considerable effort has been put into accelerating scan times without compromising image quality or the accuracy of derived results. In this paper, we present a fully-automated, quality-controlled integrated framework for reconstruction, segmentation and downstream analysis of undersampled cine CMR data. The framework enables active acquisition of radial k-space data, in which acquisition can be stopped as soon as …
Female-RHINO: A Real-Time Scanner-Integrated Framework for Automated Quantitative Uterine MRI Analysis and Structured Reporting
Radiologisches Institut, Universitätsklinikum Erlangen（埃朗根大学医院放射学研究所）；School of Biomedical Engineering and Imaging, KCL（KCL生物医学工程与成像学院）；Institute for Information Processing, Leibniz University Hannover（汉诺威莱布尼茨大学信息处理研究所）；CAIMed, L3S research institute（CAIMed，L3S研究机构）
Detection of high-frequency oscillations using time-frequency analysis
Department of Computational Perception, Johannes Kepler University（约翰内斯·开普勒大学计算感知系）；Department of Computer Engineering, Payame Noor University（帕亚米努尔大学计算机工程系）；Azerbaijan University（阿塞拜疆大学）；Institute of Mathematics and Mechanics（数学与力学研究所）；Institute for Physical Problems, Baku State University（巴库国立大学物理问题研究所）
