Pulmonary Embolism Mortality Prediction Using Multimodal Learning Based on Computed Tomography Angiography and Clinical Data
Rhode Island Hospital（罗德岛医院）；Warren Alpert Medical School of Brown University（布朗大学沃伦·阿尔珀特医学院）；Xidian University（西安电子科技大学）；Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）；University of Michigan（密歇根大学）
Zhusi Zhong, Helen Zhang, Fayez H. Fayad, Andrew C. Lancaster, John Sollee, Shreyas Kulkarni, Cheng Ting Lin, Jie Li, Xinbo Gao, Scott Collins, Colin Greineder,… 展开作者
Zhusi Zhong, Helen Zhang, Fayez H. Fayad, Andrew C. Lancaster, John Sollee, Shreyas Kulkarni, Cheng Ting Lin, Jie Li, Xinbo Gao, Scott Collins, Colin Greineder, Sun H. Ahn, Harrison X. Bai, Zhicheng Jiao, Michael K. Atalay
Purpose: Pulmonary embolism (PE) is a significant cause of mortality in the United States. The objective of this study is to implement deep learning (DL) models using Computed Tomography Pulmonary Angiography (CTPA), clinical data, and PE Severity Index (PESI) scores to predict PE mortality. Materials and Methods: 918 patients (median age 64 years, range 13-99 years, 52% female) with 3,978 CTPAs were identified via retrospective review across three institutions. To predict survival, an AI model was used to extract disease-related imaging features from CTPAs. Imaging features and/or clinical variables were then incorporated into DL models to predict survival outcomes. Four models were developed as follows: (1) using CTPA imaging features only; (2) using clinical variables only; (3) multimodal, integrating both CTPA and clinical variables; and (4) multimodal fused with calculated PESI score. Performance and contribution from each modality were evaluated using concordance index (c-index) and Net Reclassification Improvement, respectively. Performance was compared to PESI predictions using the Wilcoxon signed-rank test. Kaplan-Meier analysis was performed to stratify patients into high- and low-risk groups. Additional factor-risk analysis was conducted to account for right ventricular (RV) dysfunction. Results: For both data sets, the PESI-fused and multimodal models achieved higher c-indices than PESI alone. Following stratification of patients into high- and low-risk groups by multimodal and PESI-fused models, mortality outcomes differed significantly (both p<0.001). A strong correlation was found between high-risk grouping and RV dysfunction. Conclusions: Multiomic DL models incorporating CTPA features, clinical data, and PESI achieved higher c-indices than PESI alone for PE survival prediction.
Instituto Tecnológico de Estudios Superiores de Monterrey（蒙特雷高等技术学院）；Instituto Nacional de Cardiología Ignacio Chávez（伊格纳西奥·查韦斯国家心脏病学研究所）
Pulmonary embolism (PE) is a highly mortal disease, currently assessed by pulmonary CT angiography. True-FISP MRI has emerged as an innocuous alternative that does not hold many of the limitations of x-ray imaging. However, True-FISP MRI is very sensitive to turbulent blood flow, generating artifacts that may resemble fake clots in the pulmonary vasculature. These misinterpretations reduce its overall diagnostic accuracy to 94%, limiting a wider use in clinical environments. A new segmentation algorithm is proposed to confirm the presence of real pulmonary clots in True-FISP MR images by quant…
National Yang Ming Chiao Tung University（国立阳明交通大学）；National Cheng Kung University Hospital（国立成功大学医院）；National Cheng Kung University（国立成功大学）
The clinical symptoms of pulmonary embolism (PE) are very diverse and non-specific, which makes it difficult to diagnose. In addition, pulmonary embolism has multiple triggers and is one of the major causes of vascular death. Therefore, if it can be detected and treated quickly, it can significantly reduce the risk of death in hospitalized patients. In the detection process, the cost of computed tomography pulmonary angiography (CTPA) is high, and angiography requires the injection of contrast agents, which increase the risk of damage to the patient. Therefore, this study will use a deep learn…
With more than 60,000 deaths annually in the United States, Pulmonary Embolism (PE) is among the most fatal cardiovascular diseases. It is caused by an artery blockage in the lung; confirming its presence is time-consuming and is prone to over-diagnosis. The utilization of automated PE detection systems is critical for diagnostic accuracy and efficiency. In this study we propose a two-stage attention-based CNN-LSTM network for predicting PE, its associated type (chronic, acute) and corresponding location (leftsided, rightsided or central) on computed tomography (CT) examinations. We trained ou…
National Chiao-Tung University（国立交通大学）；National Cheng Kung University Hospital（国立成功大学附属医院）；College of Medicine, National Cheng Kung University（国立成功大学医学院）
This study was conducted to develop a computer-aided detection (CAD) system for triaging patients with pulmonary embolism (PE). The purpose of the system was to reduce the death rate during the waiting period. Computed tomography pulmonary angiography (CTPA) is used for PE diagnosis. Because CTPA reports require a radiologist to review the case and suggest further management, this creates a waiting period during which patients may die. Our proposed CAD method was thus designed to triage patients with PE from those without PE. In contrast to related studies involving CAD systems that identify k…
Stanford University（斯坦福大学）；Duke University（杜克大学）；Arizona State University（亚利桑那州立大学）
Despite the routine use of electronic health record (EHR) data by radiologists to contextualize clinical history and inform image interpretation, the majority of deep learning architectures for medical imaging are unimodal, i.e., they only learn features from pixel-level information. Recent research revealing how race can be recovered from pixel data alone highlights the potential for serious biases in models which fail to account for demographics and other key patient attributes. Yet the lack of imaging datasets which capture clinical context, inclusive of demographics and longitudinal medica…
Ferdowsi University of Mashhad（马什哈德菲尔多西大学）；Universidad de Alicante（阿利坎特大学）；Sierra Research S.L.（塞拉研究有限公司）；Unidad de Radiodiagnostico, Madrid（马德里放射诊断科）；CIBERBBN（西班牙生物医学网络研究中心）；Brigham and Women’s Hospital（布里格姆妇女医院）；Corades S.L.（科拉德斯有限公司）；LNM Institute of Information Technology（LNM信息技术学院）；Arizona State University（亚利桑那州立大学）
Rationale: Computer aided detection (CAD) algorithms for Pulmonary Embolism (PE) algorithms have been shown to increase radiologists' sensitivity with a small increase in specificity. However, CAD for PE has not been adopted into clinical practice, likely because of the high number of false positives current CAD software produces. Objective: To generate a database of annotated computed tomography pulmonary angiographies, use it to compare the sensitivity and false positive rate of current algorithms and to develop new methods that improve such metrics. Methods: 91 Computed tomography pulmonary…
Abn-BLIP: Abnormality-aligned Bootstrapping Language-Image Pre-training for Pulmonary Embolism Diagnosis and Report Generation from CTPA
Department of Diagnostic Imaging, Brown University Health（布朗大学健康中心诊断影像科）；Warren Alpert Medical School of Brown University（布朗大学沃伦·阿尔珀特医学院）；Department of Biomedical Engineering, Johns Hopkins University School of Medicine（约翰霍普金斯大学医学院生物医学工程系）；Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine（约翰霍普金斯大学医学院放射科）；Johns Hopkins University Division of Pulmonary and Critical Care Medicine（约翰霍普金斯大学肺科与重症医学科）；Department of Radiology, University of Colorado School of Medicine（科罗拉多大学医学院放射科）
PE-MVCNet: Multi-view and Cross-modal Fusion Network for Pulmonary Embolism Prediction
Hangzhou Dianzi University（杭州电子科技大学）；Dalian Polytechnic University（大连工业大学）；The Second Affiliated Hospital of The Chinese University of Hong Kong（香港中文大学第二附属医院）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shenzhen Institute of Information Technology（深圳信息职业技术学院）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；Hangzhou Institute of Advanced Technology（杭州高等研究院）
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；University Hospital Heidelberg（海德堡大学医院）；German Cancer Research Center（德国癌症研究中心）；University Hospital Essen（埃森大学医院）
Detector-based spectral computed tomography is a recent dual-energy CT (DECT) technology that offers the possibility of obtaining spectral information. From this spectral data, different types of images can be derived, amongst others virtual monoenergetic (monoE) images. MonoE images potentially exhibit decreased artifacts, improve contrast, and overall contain lower noise values, making them ideal candidates for better delineation and thus improved diagnostic accuracy of vascular abnormalities. In this paper, we are training convolutional neural networks~(CNN) that can emulate the generation …
Cross-Phase Mutual Learning Framework for Pulmonary Embolism Identification on Non-Contrast CT Scans
DAMO Academy, Alibaba Group（阿里巴巴达摩院）；School of Information Science and Technology, Fudan University（复旦大学信息科学与技术学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；The First Affiliated Hospital of Zhejiang University School of Medicine（浙江大学医学院附属第一医院）
This article describes the implementation of a system designed to automatically detect the presence of pulmonary embolism in lung scans. These images are firstly segmented, before alignment and feature extraction using PCA. The neural network was trained using the Hybrid Monte Carlo method, resulting in a committee of 250 neural networks and good results are obtained.
Ferdowsi University of Mashhad（马什哈德菲尔多西大学）；Mashhad University of Medical Sciences（马什哈德医科大学）
Todays, researchers in the field of Pulmonary Embolism (PE) analysis need to use a publicly available dataset to assess and compare their methods. Different systems have been designed for the detection of pulmonary embolism (PE), but none of them have used any public datasets. All papers have used their own private dataset. In order to fill this gap, we have collected 5160 slices of computed tomography angiography (CTA) images acquired from 20 patients, and after labeling the image by experts in this field, we provided a reliable dataset which is now publicly available. In some situation, PE d…
A Patient-Specific Pulmonary Arterial Tree Digital Twin to Extract Pulmonary Embolism Biomarkers
Univ Lyon, INSA‐Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1294（里昂大学、里昂国立应用科学学院、 Claude Bernard 里昂大学、 UJM-圣艾蒂安、 CNRS、 Inserm、 CREATIS UMR 5220、 U1294）；Department of Radiology, CHU Saint-Etienne, UJM Saint-Etienne, Saint-Etienne, France（放射科、圣艾蒂安大学医院、 UJM-圣艾蒂安、圣艾蒂安、法国）；IUF, Institut Universitaire de France, Paris（IUF、法国国家科学院、巴黎）
Athena-Research and Innovation Center in Information, Communication and Knowledge Technologies（雅典娜信息、通信与知识技术研究与创新中心）；Hellenic Open University（希腊开放大学）；Sismanogleio General Hospital（西斯马诺格利奥综合医院）
The main objective of this work is to utilize state-of-the-art deep learning approaches for the identification of pulmonary embolism in CTPA-Scans for COVID-19 patients, provide an initial assessment of their performance and, ultimately, provide a fast-track prototype solution (system). We adopted and assessed some of the most popular convolutional neural network architectures through transfer learning approaches, to strive to combine good model accuracy with fast training. Additionally, we exploited one of the most popular one-stage object detection models for the localization (through object…
National Yang Ming Chiao Tung University（阳明交通大学）；National Cheng Kung University Hospital（成功大学附属医院）；National Cheng Kung University（成功大学）
The purpose of this research is to develop a system that generates simulated computed tomography pulmonary angiography (CTPA) images clinically for pulmonary embolism diagnoses. Nowadays, CTPA images are the gold standard computerized detection method to determine and identify the symptoms of pulmonary embolism (PE), although performing CTPA is harmful for patients and also expensive. Therefore, we aim to detect possible PE patients through CT images. The system will simulate CTPA images with deep learning models for the identification of PE patients' symptoms, providing physicians with anothe…
Huazhong University of Science and Technology（华中科技大学）；The Central Hospital of Wuhan（武汉市中心医院）；JD AI Research（京东人工智能研究院）；Hong Kong University of Science and Technology（香港科技大学）
Automated methods for detecting pulmonary embolisms (PEs) on CT pulmonary angiography (CTPA) images are of high demand. Existing methods typically employ separate steps for PE candidate detection and false positive removal, without considering the ability of the other step. As a result, most existing methods usually suffer from a high false positive rate in order to achieve an acceptable sensitivity. This study presents an end-to-end trainable convolutional neural network (CNN) where the two steps are optimized jointly. The proposed CNN consists of three concatenated subnets: 1) a novel 3D can…
Arizona State University（亚利桑那州立大学）；Mayo Clinic（梅奥诊所）
Pulmonary embolism (PE) represents a thrombus ("blood clot"), usually originating from a lower extremity vein, that travels to the blood vessels in the lung, causing vascular obstruction and in some patients, death. This disorder is commonly diagnosed using CT pulmonary angiography (CTPA). Deep learning holds great promise for the computer-aided CTPA diagnosis (CAD) of PE. However, numerous competing methods for a given task in the deep learning literature exist, causing great confusion regarding the development of a CAD PE system. To address this confusion, we present a comprehensive analysis…
Yale University（耶鲁大学）；IBM Almaden Research Center（IBM阿尔马登研究中心）；New York University（纽约大学）
Pulmonary Embolism (PE) is a life-threatening disorder associated with high mortality and morbidity. Prompt diagnosis and immediate initiation of therapeutic action is important. We explored a deep learning model to detect PE on volumetric contrast-enhanced chest CT scans using a 2-stage training strategy. First, a residual convolutional neural network (ResNet) was trained using annotated 2D images. In addition to the classification loss, an attention loss was added during training to help the network focus attention on PE. Next, a recurrent network was used to scan sequentially through the fe…
Unified-protocol voxel-level pulmonary embolism annotations for three public CT angiography datasets
Technical University of Munich (TUM)（慕尼黑工业大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；The Affiliated Hospital of Xuzhou Medical University（徐州医科大学附属医院）；The Third Affiliated Hospital of Soochow University（苏州大学附属第三医院）；The Affiliated Taizhou People’s Hospital of Nanjing Medical University（南京医科大学附属泰州人民医院）
Pulmonary embolisms (PE) are known to be one of the leading causes for cardiac-related mortality. Due to inherent variabilities in how PE manifests and the cumbersome nature of manual diagnosis, there is growing interest in leveraging AI tools for detecting PE. In this paper, we build a two-stage detection pipeline that is accurate, computationally efficient, robust to variations in PE types and kernels used for CT reconstruction, and most importantly, does not require dense annotations. Given the challenges in acquiring expert annotations in large-scale datasets, our approach produces state-o…
Deep learning in computed tomography pulmonary angiography imaging: a dual-pronged approach for pulmonary embolism detection
University of Dhaka（达卡大学）；Qatar University（卡塔尔大学）；Hamad Medical Corporation（哈马德医疗公司）；Prince Sattam Bin Abdulaziz University（萨塔姆·本·阿卜杜勒阿齐兹王子大学）；Majmaah University（马吉马大学）
Digital Contrast CT Pulmonary Angiography Synthesis from Non-contrast CT for Pulmonary Vascular Disease
Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院北京协和医院）；China-Japan Friendship Hospital（中日友好医院）；Canon Medical Systems (China) CO., LTD（佳能医疗系统（中国）有限公司）
National Yang Ming Chiao Tung University（阳明交通大学）；National Cheng Kung University Hospital（国立成功大学医学院附设医院）；National Cheng Kung University（国立成功大学）
This study established a feature-enhanced adversarial semi-supervised semantic segmentation model to automatically annotate pulmonary embolism lesion areas in computed tomography pulmonary angiogram (CTPA) images. In current studies, all of the PE CTPA image segmentation methods are trained by supervised learning. However, the supervised learning models need to be retrained and the images need to be relabeled when the CTPA images come from different hospitals. This study proposed a semi-supervised learning method to make the model applicable to different datasets by adding a small amount of un…
X-ray2CTPA: Leveraging Diffusion Models to Enhance Pulmonary Embolism Classification
Faculty of Engineering, Tel Aviv University（工程学院，特拉维夫大学）；Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai（数据驱动与数字医学部，医学部，Mount Sinai医学院）；Department of Diagnostic Imaging, Sheba Medical Center, Ramat Gan, Israel（诊断影像科，Sheba 医疗中心，Ramat Gan，以色列）；Department of Radiology, Icahn School of Medicine, Mount Sinai（放射科，Icahn医学院，Mount Sinai）
SCUNet++: Swin-UNet and CNN Bottleneck Hybrid Architecture with Multi-Fusion Dense Skip Connection for Pulmonary Embolism CT Image Segmentation
Hangzhou Dianzi University（杭州电子科技大学）；Second Affiliated Hospital of the Chinese University of Hong Kong（香港中文大学第二附属医院）；Shenzhen Research Institute of Big Data（深圳市大数据研究院）
University of Sheffield（谢菲尔德大学）；Sheffield Teaching Hospitals NHS Foundation Trust（谢菲尔德教学医院NHS基金会信托）；INSIGNEO, Institute for In Silico Medicine, University of Sheffield（谢菲尔德大学INSIGNEO计算医学研究所）；NIHR Sheffield Biomedical Research Centre, University of Sheffield（谢菲尔德大学NIHR谢菲尔德生物医学研究中心）
Purpose: Lung disease assessment in precapillary pulmonary hypertension (PH) is essential for appropriate patient management. This study aims to develop an artificial intelligence (AI) deep learning model for lung texture classification in CT Pulmonary Angiography (CTPA), and evaluate its correlation with clinical assessment methods. Materials and Methods: In this retrospective study with external validation, 122 patients with pre-capillary PH were used to train (n=83), validate (n=17) and test (n=10 internal test, n=12 external test) a patch based DenseNet-121 classification model. "Normal", …
An Empirical Analysis for Zero-Shot Multi-Label Classification on COVID-19 CT Scans and Uncurated Reports
ARTORG Center for Biomedical Engineering Research（ARTORG生物医学工程研究中心）；University of Bern（伯尔尼大学）；Bern University Hospital（伯尔尼大学医院）；Lindenhofspital（林登霍夫医院）；Campus Stiftung Lindenhof Bern（伯尔尼林登霍夫基金会校区）
Deep learning-driven pulmonary artery and vein segmentation reveals demography-associated vasculature anatomical differences
King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）；The First Affiliated Hospital of Harbin Medical University（哈尔滨医科大学附属第一医院）；Northeast Forestry University（东北林业大学）；The Fourth Hospital of Harbin Medical University（哈尔滨医科大学附属第四医院）；China-Japan Friendship Hospital（中日友好医院）；Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School（南京大学医学院附属南京鼓楼医院）；Renji Hospital, School of Medicine, Shanghai Jiao Tong University（上海交通大学医学院附属仁济医院）
High Accuracy Pulmonary Vessel Segmentation for Contrast and Non-contrast CT Images and Clinical Evaluation
Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（北京协和医院，中国医学科学院 北京协和医学院）；Canon Medical Systems (China)（佳能医疗系统（中国））
Tel-Aviv University（特拉维夫大学）；Sheba Medical Center（舍巴医疗中心）
Pulmonary embolus (PE) refers to obstruction of pulmonary arteries by blood clots. PE accounts for approximately 100,000 deaths per year in the United States alone. The clinical presentation of PE is often nonspecific, making the diagnosis challenging. Thus, rapid and accurate risk stratification is of paramount importance. High-risk PE is caused by right ventricular (RV) dysfunction from acute pressure overload, which in return can help identify which patients require more aggressive therapy. Reconstructed four-chamber views of the heart on chest CT can detect right ventricular enlargement. C…
