BIO-CXRNET: A Robust Multimodal Stacking Machine Learning Technique for Mortality Risk Prediction of COVID-19 Patients using Chest X-Ray Images and Clinical Data
Qatar University（卡塔尔大学）；North South University（北南大学）；University of South Carolina（南卡罗来纳大学）；Hamad General Hospital（哈马德综合医院）；AFG College with the University of Aberdeen（阿伯丁大学AFG学院）
Tawsifur Rahman, Muhammad E. H. Chowdhury, Amith Khandakar, Zaid Bin Mahbub, Md Sakib Abrar Hossain, Abraham Alhatou, Eynas Abdalla, Sreekumar Muthiyal, Khandak… 展开作者
Tawsifur Rahman, Muhammad E. H. Chowdhury, Amith Khandakar, Zaid Bin Mahbub, Md Sakib Abrar Hossain, Abraham Alhatou, Eynas Abdalla, Sreekumar Muthiyal, Khandaker Farzana Islam, Saad Bin Abul Kashem, Muhammad Salman Khan, Susu M. Zughaier, Maqsud Hossain
Fast and accurate detection of the disease can significantly help in reducing the strain on the healthcare facility of any country to reduce the mortality during any pandemic. The goal of this work is to create a multimodal system using a novel machine learning framework that uses both Chest X-ray (CXR) images and clinical data to predict severity in COVID-19 patients. In addition, the study presents a nomogram-based scoring technique for predicting the likelihood of death in high-risk patients. This study uses 25 biomarkers and CXR images in predicting the risk in 930 COVID-19 patients admitted during the first wave of COVID-19 (March-June 2020) in Italy. The proposed multimodal stacking technique produced the precision, sensitivity, and F1-score, of 89.03%, 90.44%, and 89.03%, respectively to identify low or high-risk patients. This multimodal approach improved the accuracy by 6% in comparison to the CXR image or clinical data alone. Finally, nomogram scoring system using multivariate logistic regression -- was used to stratify the mortality risk among the high-risk patients identified in the first stage. Lactate Dehydrogenase (LDH), O2 percentage, White Blood Cells (WBC) Count, Age, and C-reactive protein (CRP) were identified as useful predictor using random forest feature selection model. Five predictors parameters and a CXR image based nomogram score was developed for quantifying the probability of death and categorizing them into two risk groups: survived (<50%), and death (>=50%), respectively. The multi-modal technique was able to predict the death probability of high-risk patients with an F1 score of 92.88 %. The area under the curves for the development and validation cohorts are 0.981 and 0.939, respectively.
Australian Centre for Visual Technologies, The University of Adelaide（阿德莱德大学澳大利亚视觉技术中心）；School of Public Health, The University of Adelaide（阿德莱德大学公共卫生学院）；School of ITEE, The University of Queensland（昆士兰大学信息技术与电气工程学院）；Institute for Systems and Robotics, Instituto Superior Técnico（葡萄牙里斯本高等技术学院系统与机器人研究所）
We propose new methods for the prediction of 5-year mortality in elderly individuals using chest computed tomography (CT). The methods consist of a classifier that performs this prediction using a set of features extracted from the CT image and segmentation maps of multiple anatomic structures. We explore two approaches: 1) a unified framework based on deep learning, where features and classifier are automatically learned in a single optimisation process; and 2) a multi-stage framework based on the design and selection/extraction of hand-crafted radiomics features, followed by the classifier l…
Manchester Metropolitan University（曼彻斯特城市大学）；Brunel University（布鲁内尔大学）；Whiston Hospital（惠斯顿医院）；St Helens and Knowsley Teaching Hospitals NHS Trust（圣海伦斯和诺斯利教学医院国民保健服务信托）；University of Sheffield（谢菲尔德大学）
Accurate and rapid detection of COVID-19 pneumonia is crucial for optimal patient treatment. Chest X-Ray (CXR) is the first line imaging test for COVID-19 pneumonia diagnosis as it is fast, cheap and easily accessible. Inspired by the success of deep learning (DL) in computer vision, many DL-models have been proposed to detect COVID-19 pneumonia using CXR images. Unfortunately, these deep classifiers lack the transparency in interpreting findings, which may limit their applications in clinical practice. The existing commonly used visual explanation methods are either too noisy or imprecise, wi…
Stony Brook University（石溪大学）；Newark Beth Israel Medical Center（纽瓦克贝丝以色列医疗中心）；University Hospitals Cleveland Medical Center（大学医院克利夫兰医疗中心）
We predict mechanical ventilation requirement and mortality using computational modeling of chest radiographs (CXRs) for coronavirus disease 2019 (COVID-19) patients. This two-center, retrospective study analyzed 530 deidentified CXRs from 515 COVID-19 patients treated at Stony Brook University Hospital and Newark Beth Israel Medical Center between March and August 2020. DL and machine learning classifiers to predict mechanical ventilation requirement and mortality were trained and evaluated using patient CXRs. A novel radiomic embedding framework was also explored for outcome prediction. All …
G42 Healthcare（G42医疗）；NYU Abu Dhabi（纽约大学阿布扎比分校）；NYU Grossman School of Medicine（纽约大学格罗斯曼医学院）
Multi-modal fusion approaches aim to integrate information from different data sources. Unlike natural datasets, such as in audio-visual applications, where samples consist of "paired" modalities, data in healthcare is often collected asynchronously. Hence, requiring the presence of all modalities for a given sample is not realistic for clinical tasks and significantly limits the size of the dataset during training. In this paper, we propose MedFuse, a conceptually simple yet promising LSTM-based fusion module that can accommodate uni-modal as well as multi-modal input. We evaluate the fusion …
A Joint Representation Using Continuous and Discrete Features for Cardiovascular Diseases Risk Prediction on Chest CT Scans
DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；Tsinghua University（清华大学）；Hupan Laboratory（湖畔实验室）；Beijing Key Laboratory of Precision Medicine for Chronic Heart Failure（慢性心力衰竭精准医学北京市重点实验室）；National Engineering Research Center for Medical Big Data Application Technology（国家医疗大数据应用技术工程研究中心）
University of Warwick（华威大学）；Guy’s and St Thomas’ NHS Foundation Trust（盖伊和圣托马斯国民保健服务基金会信托）；University Hospitals Coventry and Warwickshire NHS Trust（考文垂与沃里克郡大学医院国民保健服务信托）；Guy’s and St Thomas’ NHS Trust（盖伊和圣托马斯国民保健服务信托）；King’s College London（伦敦国王学院）；The Alan Turing Institute（艾伦·图灵研究所）
Understanding the internal physiological changes accompanying the aging process is an important aspect of medical image interpretation, with the expected changes acting as a baseline when reporting abnormal findings. Deep learning has recently been demonstrated to allow the accurate estimation of patient age from chest X-rays, and shows potential as a health indicator and mortality predictor. In this paper we present a novel comparative study of the relative performance of radiologists versus state-of-the-art deep learning models on two tasks: (a) patient age estimation from a single chest X-r…
Vellore Institute of Technology, Chennai（韦洛尔理工学院（金奈校区））
Tuberculosis is an infectious disease that is leading to the death of millions of people across the world. The mortality rate of this disease is high in patients suffering from immuno-compromised disorders. The early diagnosis of this disease can save lives and can avoid further complications. But the diagnosis of TB is a very complex task. The standard diagnostic tests still rely on traditional procedures developed in the last century. These procedures are slow and expensive. So this paper presents an automatic approach for the diagnosis of TB from posteroanterior chest x-rays. This is a two-…
Stanford University（斯坦福大学）；Massachusetts Institute of Technology（麻省理工学院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Fundacion Progreso y Salud（进步与健康基金会）；National Tsing Hua University（台湾清华大学）；Albert Einstein Medical Center（阿尔伯特·爱因斯坦医疗中心）；Memorial Sloan Kettering Cancer Center（纪念斯隆·凯特琳癌症中心）；National University Hospital（新加坡国立大学医院）；Hoboken University Medical Center–CarePoint Health（霍博肯大学医疗中心-CarePoint Health）；Hospital Universitario Monteprincipe（蒙特普林西比大学医院）；Grupo HM Hospitales（HM医院集团）；Seoul National University College of Medicine（首尔大学医学院）；Centro Nacional de Investigaciones Cardiovasculares（国家心血管研究中心）；Instituto de Salud Carlos III（卡洛斯三世健康研究所）；Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）
The unprecedented global crisis brought about by the COVID-19 pandemic has sparked numerous efforts to create predictive models for the detection and prognostication of SARS-CoV-2 infections with the goal of helping health systems allocate resources. Machine learning models, in particular, hold promise for their ability to leverage patient clinical information and medical images for prediction. However, most of the published COVID-19 prediction models thus far have little clinical utility due to methodological flaws and lack of appropriate validation. In this paper, we describe our methodology…
Chest X-ray Foundation Model with Global and Local Representations Integration
Department of Biomedical Engineering and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute（生物医学工程系和生物技术及跨学科研究所以及罗切斯特理工学院）；Department of Radiology, Massachusetts General Hospital, Harvard Medical School（放射学系、麻省总医院、哈佛医学院）
Northwestern University（西北大学）；Northwestern Memorial Hospital（西北纪念医院）
Propose: To present DeepCOVID-Fuse, a deep learning fusion model to predict risk levels in patients with confirmed coronavirus disease 2019 (COVID-19) and to evaluate the performance of pre-trained fusion models on full or partial combination of chest x-ray (CXRs) or chest radiograph and clinical variables. Materials and Methods: The initial CXRs, clinical variables and outcomes (i.e., mortality, intubation, hospital length of stay, ICU admission) were collected from February 2020 to April 2020 with reverse-transcription polymerase chain reaction (RT-PCR) test results as the reference standard…
Jadavpur University（贾达普大学）；Heritage Institute of Technology（赫里蒂奇理工学院）
Pneumonia is one of the major reasons for child mortality especially in income-deprived regions of the world. Although it can be detected and treated with very less sophisticated instruments and medication, Pneumonia detection still remains a major concern in developing countries. Computer-aided based diagnosis (CAD) systems can be used in such countries due to their lower operating costs than professional medical experts. In this paper, we propose a CAD system for Pneumonia detection from Chest X-rays, using the concepts of deep learning and a meta-heuristic algorithm. We first extract deep f…
Topcon Positioning Systems（拓普康定位系统公司）；University of Michigan（密歇根大学）；Shenzhen Research Institute of Big Data（深圳大数据研究院）
Pneumonia is the leading cause of death among young children and one of the top mortality causes worldwide. The pneumonia detection is usually performed through examine of chest X-ray radiograph by highly-trained specialists. This process is tedious and often leads to a disagreement between radiologists. Computer-aided diagnosis systems showed the potential for improving diagnostic accuracy. In this work, we develop the computational approach for pneumonia regions detection based on single-shot detectors, squeeze-and-excitation deep convolution neural networks, augmentations and multi-task lea…
National Tsing Hua University（国立清华大学）；Massachusetts Institute of Technology（麻省理工学院）；Emory University（埃默里大学）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Emory University School of Medicine（埃默里大学医学院）
Chronic obstructive pulmonary disease (COPD) is one of the most common chronic illnesses in the world and the third leading cause of mortality worldwide. It is often underdiagnosed or not diagnosed until later in the disease course. Spirometry tests are the gold standard for diagnosing COPD but can be difficult to obtain, especially in resource-poor countries. Chest X-rays (CXRs), however, are readily available and may serve as a screening tool to identify patients with COPD who should undergo further testing. Currently, no research applies deep learning (DL) algorithms that use large multi-si…
Automated diagnosis of lung diseases using vision transformer: a comparative study on chest x-ray classification
Centro de Investigación en Computación, Instituto Politécnico Nacional (CIC-IPN)（墨西哥国立理工学院计算机研究中心）；Grand Asian University of Sialkot（锡亚尔科特大亚洲大学）；The Islamia University of Bahawalpur（巴哈瓦尔布尔伊斯兰大学）；Zhejiang Normal University（浙江师范大学）；Leads University Lahore（拉合尔利兹大学）
Deep Omni-supervised Learning for Rib Fracture Detection from Chest Radiology Images
Imsight AI Research Lab（Imsight AI 研究实验室）；The Hong Kong University of Science and Technology（香港科技大学）；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute（香港科技大学深圳-香港协同创新研究院）；The Chinese University of Hong Kong（香港中文大学）
RADLogics Inc.（RADLogics公司）；Affiliated Taizhou Hospital of Wenzhou Medical University（温州医科大学附属台州医院）；Tel-Aviv University（特拉维夫大学）
The outbreak of the novel coronavirus, officially declared a global pandemic, has a severe impact on our daily lives. As of this writing there are approximately 197,188 confirmed cases of which 80,881 are in "Mainland China" with 7,949 deaths, a mortality rate of 3.4%. In order to support radiologists in this overwhelming challenge, we develop a deep learning based algorithm that can detect, localize and quantify severity of COVID-19 manifestation from chest CT scans. The algorithm is comprised of a pipeline of image processing algorithms which includes lung segmentation, 2D slice classificati…
Recent studies have shown that lung cancer screening using annual low-dose computed tomography (CT) reduces lung cancer mortality by 20% compared to traditional chest radiography. Therefore, CT lung screening has started to be used widely all across the world. However, analyzing these images is a serious burden for radiologists. In this study, we propose a novel and simple framework that analyzes CT lung screenings using convolutional neural networks (CNNs) and reduces false positives. Our framework shows that even non-complex architectures are very powerful to classify 3D nodule data when com…
Cardiovascular disease (CVD) is a common and strong threat to human beings, featuring high prevalence, disability and mortality. The amount of coronary artery calcification (CAC) is an effective factor for CVD risk evaluation. Conventionally, CAC is quantified using ECG-synchronized cardiac CT but rarely from general chest CT scans. However, compared with ECG-synchronized cardiac CT, chest CT is more prevalent and economical in clinical practice. To address this, we propose an automatic method based on Dense U-Net to segment coronary calcium pixels on both types of CT scans. Our contribution i…
People all over the globe are affected by pneumonia but deaths due to it are highest in Sub-Saharan Asia and South Asia. In recent years, the overall incidence and mortality rate of pneumonia regardless of the utilization of effective vaccines and compelling antibiotics has escalated. Thus, pneumonia remains a disease that needs spry prevention and treatment. The widespread prevalence of pneumonia has caused the research community to come up with a framework that helps detect, diagnose and analyze diseases accurately and promptly. One of the major hurdles faced by the Artificial Intelligence (…
University of Saskatchewan（萨斯喀彻温大学）；University of Stanford（斯坦福大学）
The newly identified Coronavirus pneumonia, subsequently termed COVID-19, is highly transmittable and pathogenic with no clinically approved antiviral drug or vaccine available for treatment. The most common symptoms of COVID-19 are dry cough, sore throat, and fever. Symptoms can progress to a severe form of pneumonia with critical complications, including septic shock, pulmonary edema, acute respiratory distress syndrome and multi-organ failure. While medical imaging is not currently recommended in Canada for primary diagnosis of COVID-19, computer-aided diagnosis systems could assist in the …
The University of British Columbia（不列颠哥伦比亚大学）；Xtract AI；Element AI；SapienML；AWS, Worldwide Public Sector（亚马逊云科技全球公共部门）
The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has rapidly spread into a global pandemic. A form of pneumonia, presenting as opacities with in a patient's lungs, is the most common presentation associated with this virus, and great attention has gone into how these changes relate to patient morbidity and mortality. In this work we provide open source models for the segmentation of patterns of pulmonary opacification on chest Computed Tomography (CT) scans which have been correlated with various stages and severities of infection. We have collected 663 chest CT scans of COVID-…
Explainable Cross-Disease Reasoning for Cardiovascular Risk Assessment from Low-Dose Computed Tomography
Department of Computer Science, Emory University（埃默里大学计算机科学系）；Department of Computer Science, Johns Hopkins University（约翰霍普金斯大学计算机科学系）；Department of Radiation Oncology（放射肿瘤学部）；Winship Cancer Institute, Emory University（埃默里大学Winship癌症研究所）
Benchmarking Foundation Models and Parameter-Efficient Fine-Tuning for Prognosis Prediction in Medical Imaging
Unit of Artificial Intelligence and Computer Systems, Department of Engineering, Università Campus Bio-Medico di Roma（人工智能与计算机系统单位，工程系，罗马生物医学大学）；College of Computer Science and Software Engineering, Shenzhen University（计算机科学与软件工程学院，深圳大学）；Department of Diagnostics and Intervention, Radiation Physics, Biomedical Engineering, Umeå University（诊断与介入系，辐射物理，生物医学工程，乌梅大学）
Pneumonia is one of the most acute respiratory diseases having remarkably high prevalence and mortality rate. Chest X-ray (CXR) has been widely utilized for the diagnosis of this disease owing to its availability, diagnostic speed and accuracy. However, even for an expert radiologist, it is quite challenging to readily determine pneumonia opacity by examining CXRs. Therefore, this study has been structured to automate the pneumonia detection process by introducing a robust deep learning framework. The proposed network comprises of Process Convolution (Pro_Conv) blocks for feature accumulation …
MOSCARD -- Causal Reasoning and De-confounding for Multimodal Opportunistic Screening of Cardiovascular Adverse Events
Department of Data Science & Engineering, Arizona State University（数据科学与工程系，亚利桑那州立大学）；Department of Cardiovascular Medicine, Mayo Clinic（心血管医学系，梅奥诊所）；Department of Radiology, Mayo Clinic（放射科，梅奥诊所）；Catholic kwandong university international st. mary’s hospital（韩国天主教 kwandong 大学国际圣玛丽医院）
MSAD-Net: Multiscale and Spatial Attention-based Dense Network for Lung Cancer Classification
Computer Science and Engineering Department, NIIT University, Jaipur, India（尼特大学计算机科学与工程系，印度斋普尔）；CSE department, Christ (Deemed to be University), Bangalore, India（基督大学（认定为大学）计算机科学与工程系，印度班加罗尔）；Faculty of Computing, Engineering and the Built Environment, Birmingham City University, UK（伯明翰城市大学计算、工程与建筑环境学院，英国）
University of Science and Technology of China（中国科学技术大学）；The First Affiliated Hospital, Division of Life Sciences and Medicine, University of Science and Technology of China（中国科学技术大学生命科学与医学部附属第一医院）
The ongoing global pandemic of Coronavirus Disease 2019 (COVID-19) poses a serious threat to public health and the economy. Rapid and accurate diagnosis of COVID-19 is crucial to prevent the further spread of the disease and reduce its mortality. Chest Computed tomography (CT) is an effective tool for the early diagnosis of lung diseases including pneumonia. However, detecting COVID-19 from CT is demanding and prone to human errors as some early-stage patients may have negative findings on images. Recently, many deep learning methods have achieved impressive performance in this regard. Despite…
Zebra Medical Vision Ltd（斑马医疗视觉有限公司）；Rabin Medical Center（拉宾医疗中心）
Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of morbidity and mortality worldwide. Identifying those at highest risk of deterioration would allow more effective distribution of preventative and surveillance resources. Secondary pulmonary hypertension is a manifestation of advanced COPD, which can be reliably diagnosed by the main Pulmonary Artery (PA) to Ascending Aorta (Ao) ratio. In effect, a PA diameter to Ao diameter ratio of greater than 1 has been demonstrated to be a reliable marker of increased pulmonary arterial pressure. Although clinically valuable and readily vis…
Technical University of Munich（慕尼黑工业大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
During the first wave of COVID-19, hospitals were overwhelmed with the high number of admitted patients. An accurate prediction of the most likely individual disease progression can improve the planning of limited resources and finding the optimal treatment for patients. However, when dealing with a newly emerging disease such as COVID-19, the impact of patient- and disease-specific factors (e.g. body weight or known co-morbidities) on the immediate course of disease is by and large unknown. In the case of COVID-19, the need for intensive care unit (ICU) admission of pneumonia patients is ofte…
