16 results on '"Dayan, Ittai"'
Search Results
2. Automatic detection of decreased ejection fraction and left ventricular hypertrophy on 4D cardiac CTA: Use of artificial intelligence with transfer learning to facilitate multi-site operations
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Rockenbach, Marcio A.B.C., Buch, Varun, Gupta, Vikash, Kotecha, Gopal K., Laur, Olga, Erdal, Barbaros S., Yang, Dong, Xu, Daguang, Ghoshhajra, Brian B., Flores, Mona G., Dayan, Ittai, Roth, Holger, and White, Richard D.
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- 2022
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3. Federated learning for predicting clinical outcomes in patients with COVID-19
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Dayan, Ittai, Roth, Holger R., Zhong, Aoxiao, Harouni, Ahmed, Gentili, Amilcare, Abidin, Anas Z., and Liu, Andrew
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Artificial intelligence -- Usage ,Treatment outcome -- Analysis ,Emergency medical services -- Usage ,Health risk assessment -- Methods ,Artificial intelligence ,Biological sciences ,Health - Abstract
Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe to train a FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts the future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays. EXAM achieved an average area under the curve (AUC) >0.92 for predicting outcomes at 24 and 72 h from the time of initial presentation to the emergency room, and it provided 16% improvement in average AUC measured across all participating sites and an average increase in generalizability of 38% when compared with models trained at a single site using that site's data. For prediction of mechanical ventilation treatment or death at 24 h at the largest independent test site, EXAM achieved a sensitivity of 0.950 and specificity of 0.882. In this study, FL facilitated rapid data science collaboration without data exchange and generated a model that generalized across heterogeneous, unharmonized datasets for prediction of clinical outcomes in patients with COVID-19, setting the stage for the broader use of FL in healthcare. Federated learning, a method for training artificial intelligence algorithms that protects data privacy, was used to predict future oxygen requirements of symptomatic patients with COVID-19 using data from 20 different institutes across the globe., Author(s): Ittai Dayan [sup.1] , Holger R. Roth [sup.2] , Aoxiao Zhong [sup.3] [sup.4] , Ahmed Harouni [sup.2] , Amilcare Gentili [sup.5] , Anas Z. Abidin [sup.2] , Andrew Liu [...]
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- 2021
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4. Multi-population generalizability of a deep learning-based chest radiograph severity score for COVID-19
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Li, Matthew D., Arun, Nishanth T., Aggarwal, Mehak, Gupta, Sharut, Singh, Praveer, Little, Brent P., Mendoza, Dexter P., Corradi, Gustavo C.A., Takahashi, Marcelo S., Ferraciolli, Suely F., Succi, Marc D., Lang, Min, Bizzo, Bernardo C., Dayan, Ittai, Kitamura, Felipe C., and Kalpathy-Cramer, Jayashree
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- 2022
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5. Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation: a cohort study
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Ebrahimian, Shadi, Homayounieh, Fatemeh, Rockenbach, Marcio A. B. C., Putha, Preetham, Raj, Tarun, Dayan, Ittai, Bizzo, Bernardo C., Buch, Varun, Wu, Dufan, Kim, Kyungsang, Li, Quanzheng, Digumarthy, Subba R., and Kalra, Mannudeep K.
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- 2021
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6. Development and Validation of a Deep Learning Model for Prediction of Severe Outcomes in Suspected COVID-19 Infection
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Buch, Varun, Zhong, Aoxiao, Li, Xiang, Rockenbach, Marcio Aloisio Bezerra Cavalcanti, Wu, Dufan, Ren, Hui, Guan, Jiahui, Liteplo, Andrew, Dutta, Sayon, Dayan, Ittai, and Li, Quanzheng
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FOS: Computer and information sciences ,Computer Science - Machine Learning ,Statistics - Machine Learning ,Machine Learning (stat.ML) ,Machine Learning (cs.LG) - Abstract
COVID-19 patient triaging with predictive outcome of the patients upon first present to emergency department (ED) is crucial for improving patient prognosis, as well as better hospital resources management and cross-infection control. We trained a deep feature fusion model to predict patient outcomes, where the model inputs were EHR data including demographic information, co-morbidities, vital signs and laboratory measurements, plus patient's CXR images. The model output was patient outcomes defined as the most insensitive oxygen therapy required. For patients without CXR images, we employed Random Forest method for the prediction. Predictive risk scores for COVID-19 severe outcomes ("CO-RISK" score) were derived from model output and evaluated on the testing dataset, as well as compared to human performance. The study's dataset (the "MGB COVID Cohort") was constructed from all patients presenting to the Mass General Brigham (MGB) healthcare system from March 1st to June 1st, 2020. ED visits with incomplete or erroneous data were excluded. Patients with no test order for COVID or confirmed negative test results were excluded. Patients under the age of 15 were also excluded. Finally, electronic health record (EHR) data from a total of 11060 COVID-19 confirmed or suspected patients were used in this study. Chest X-ray (CXR) images were also collected from each patient if available. Results show that CO-RISK score achieved area under the Curve (AUC) of predicting MV/death (i.e. severe outcomes) in 24 hours of 0.95, and 0.92 in 72 hours on the testing dataset. The model shows superior performance to the commonly used risk scores in ED (CURB-65 and MEWS). Comparing with physician's decisions, CO-RISK score has demonstrated superior performance to human in making ICU/floor decisions., Varun Buch, Aoxiao Zhong and Xiang Li contribute equally to this work
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- 2021
7. Democratizing Artificial Intelligence in Healthcare: A Study of Model Development Across Two Institutions Incorporating Transfer Learning
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Gupta1, Vikash, Roth, Holger, Buch3, Varun, Rockenbach, Marcio A. B. C., White, Richard D, Yang, Dong, Laur, Olga, Ghoshhajra, Brian, Dayan, Ittai, Xu, Daguang, Flores, Mona G., and Erdal, Barbaros Selnur
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FOS: Computer and information sciences ,I.2.10 ,Computer Vision and Pattern Recognition (cs.CV) ,Image and Video Processing (eess.IV) ,FOS: Electrical engineering, electronic engineering, information engineering ,Computer Science - Computer Vision and Pattern Recognition ,Electrical Engineering and Systems Science - Image and Video Processing - Abstract
The training of deep learning models typically requires extensive data, which are not readily available as large well-curated medical-image datasets for development of artificial intelligence (AI) models applied in Radiology. Recognizing the potential for transfer learning (TL) to allow a fully trained model from one institution to be fine-tuned by another institution using a much small local dataset, this report describes the challenges, methodology, and benefits of TL within the context of developing an AI model for a basic use-case, segmentation of Left Ventricular Myocardium (LVM) on images from 4-dimensional coronary computed tomography angiography. Ultimately, our results from comparisons of LVM segmentation predicted by a model locally trained using random initialization, versus one training-enhanced by TL, showed that a use-case model initiated by TL can be developed with sparse labels with acceptable performance. This process reduces the time required to build a new model in the clinical environment at a different institution., 8 pages, 5 figures, pre-print
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- 2020
8. Breaking Down Siloes: Federated learning models help disperse data.
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Dayan, Ittai
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ARTIFICIAL intelligence ,WORKFLOW ,DATABASE management ,LEARNING strategies ,PHILOSOPHY of education ,QUALITY assurance ,INTERPROFESSIONAL relations ,HOSPITAL radiological services ,ALGORITHMS - Published
- 2023
9. Deep metric learning-based image retrieval system for chest radiograph and its clinical applications in COVID-19.
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Zhong, Aoxiao, Li, Xiang, Wu, Dufan, Ren, Hui, Kim, Kyungsang, Kim, Younggon, Buch, Varun, Neumark, Nir, Bizzo, Bernardo, Tak, Won Young, Park, Soo Young, Lee, Yu Rim, Kang, Min Kyu, Park, Jung Gil, Kim, Byung Seok, Chung, Woo Jin, Guo, Ning, Dayan, Ittai, Kalra, Mannudeep K., and Li, Quanzheng
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COVID-19 , *IMAGE retrieval , *CHEST X rays , *PANDEMICS , *COMPUTER-aided diagnosis , *DEEP learning , *DYNAMIC testing , *LABELS - Abstract
• Development of a content-based image retrieval system for chest radiograph based on a novel deep metric learning algorithm. • Validation on an international multi-site COVID-19 dataset with superior performance in image retrieval, diagnosis and prognosis tasks. • Good transferability and generalizability on other clinical decision support tasks in the treatment and management of COVID-19 patient. • Being implemented in clinical workflow at Partners Healthcare due to high clinical impact for COVID-19 diagnosis, prognosis and patient management. In recent years, deep learning-based image analysis methods have been widely applied in computer-aided detection, diagnosis and prognosis, and has shown its value during the public health crisis of the novel coronavirus disease 2019 (COVID-19) pandemic. Chest radiograph (CXR) has been playing a crucial role in COVID-19 patient triaging, diagnosing and monitoring, particularly in the United States. Considering the mixed and unspecific signals in CXR, an image retrieval model of CXR that provides both similar images and associated clinical information can be more clinically meaningful than a direct image diagnostic model. In this work we develop a novel CXR image retrieval model based on deep metric learning. Unlike traditional diagnostic models which aim at learning the direct mapping from images to labels, the proposed model aims at learning the optimized embedding space of images, where images with the same labels and similar contents are pulled together. The proposed model utilizes multi-similarity loss with hard-mining sampling strategy and attention mechanism to learn the optimized embedding space, and provides similar images, the visualizations of disease-related attention maps and useful clinical information to assist clinical decisions. The model is trained and validated on an international multi-site COVID-19 dataset collected from 3 different sources. Experimental results of COVID-19 image retrieval and diagnosis tasks show that the proposed model can serve as a robust solution for CXR analysis and patient management for COVID-19. The model is also tested on its transferability on a different clinical decision support task for COVID-19, where the pre-trained model is applied to extract image features from a new dataset without any further training. The extracted features are then combined with COVID-19 patient's vitals, lab tests and medical histories to predict the possibility of airway intubation in 72 hours, which is strongly associated with patient prognosis, and is crucial for patient care and hospital resource planning. These results demonstrate our deep metric learning based image retrieval model is highly efficient in the CXR retrieval, diagnosis and prognosis, and thus has great clinical value for the treatment and management of COVID-19 patients. [Display omitted] [ABSTRACT FROM AUTHOR]
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- 2021
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10. CAFES: Chest X-ray Analysis using Federated Self-supervised Learning for Pediatric COVID-19 Detection.
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Parida A, Anwar SM, Patel MP, Blom M, Einat TT, Tonetti A, Baror Y, Dayan I, and Linguraru MG
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Chest X-rays (CXRs) play a pivotal role in cost-effective clinical assessment of various heart and lung related conditions. The urgency of COVID-19 diagnosis prompted their use in identifying conditions like lung opacity, pneumonia, and acute respiratory distress syndrome in pediatric patients. We propose an AI-driven solution for binary COVID-19 versus non-COVID-19 classification in pediatric CXRs. We present a Federated Self-Supervised Learning (FSSL) framework to enhance Vision Transformer (ViT) performance for COVID-19 detection in pediatric CXRs. ViT's prowess in vision-related binary classification tasks, combined with self-supervised pre-training on adult CXR data, forms the basis of the FSSL approach. We implement our strategy on the Rhino Health Federated Computing Platform (FCP), which ensures privacy and scalability for distributed data. The chest X-ray analysis using the federated SSL (CAFES) model, utilizes the FSSL-pre-trained ViT weights and demonstrated gains in accurately detecting COVID-19 when compared with a fully supervised model. Our FSSL-pre-trained ViT showed an area under the precision-recall curve (AUPR) of 0.952, which is 0.231 points higher than the fully supervised model for COVID-19 diagnosis using pediatric data. Our contributions include leveraging vision transformers for effective COVID-19 diagnosis from pediatric CXRs, employing distributed federated learning-based self-supervised pre-training on adult data, and improving pediatric COVID-19 diagnosis performance. This privacy-conscious approach aligns with HIPAA guidelines, paving the way for broader medical imaging applications.
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- 2024
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11. Accelerating artificial intelligence: How federated learning can protect privacy, facilitate collaboration, and improve outcomes.
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Patel M, Dayan I, Fishman EK, Flores M, Gilbert FJ, Guindy M, Koay EJ, Rosenthal M, Roth HR, and Linguraru MG
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- Humans, Privacy, Learning, Pancreatic Neoplasms, Artificial Intelligence, Pancreatic Neoplasms
- Abstract
Cross-institution collaborations are constrained by data-sharing challenges. These challenges hamper innovation, particularly in artificial intelligence, where models require diverse data to ensure strong performance. Federated learning (FL) solves data-sharing challenges. In typical collaborations, data is sent to a central repository where models are trained. With FL, models are sent to participating sites, trained locally, and model weights aggregated to create a master model with improved performance. At the 2021 Radiology Society of North America's (RSNA) conference, a panel was conducted titled "Accelerating AI: How Federated Learning Can Protect Privacy, Facilitate Collaboration and Improve Outcomes." Two groups shared insights: researchers from the EXAM study (EMC CXR AI Model) and members of the National Cancer Institute's Early Detection Research Network's (EDRN) pancreatic cancer working group. EXAM brought together 20 institutions to create a model to predict oxygen requirements of patients seen in the emergency department with COVID-19 symptoms. The EDRN collaboration is focused on improving outcomes for pancreatic cancer patients through earlier detection. This paper describes major insights from the panel, including direct quotes. The panelists described the impetus for FL, the long-term potential vision of FL, challenges faced in FL, and the immediate path forward for FL., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
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- 2023
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12. Key considerations for the use of artificial intelligence in healthcare and clinical research.
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Lovejoy CA, Arora A, Buch V, and Dayan I
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Interest in artificial intelligence (AI) has grown exponentially in recent years, attracting sensational headlines and speculation. While there is considerable potential for AI to augment clinical practice, there remain numerous practical implications that must be considered when exploring AI solutions. These range from ethical concerns about algorithmic bias to legislative concerns in an uncertain regulatory environment. In the absence of established protocols and examples of best practice, there is a growing need for clear guidance both for innovators and early adopters. Broadly, there are three stages to the innovation process: invention, development and implementation. In this paper, we present key considerations for innovators at each stage and offer suggestions along the AI development pipeline, from bench to bedside., (© Royal College of Physicians 2022. All rights reserved.)
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- 2022
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13. A multi-center study of COVID-19 patient prognosis using deep learning-based CT image analysis and electronic health records.
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Gong K, Wu D, Arru CD, Homayounieh F, Neumark N, Guan J, Buch V, Kim K, Bizzo BC, Ren H, Tak WY, Park SY, Lee YR, Kang MK, Park JG, Carriero A, Saba L, Masjedi M, Talari H, Babaei R, Mobin HK, Ebrahimian S, Guo N, Digumarthy SR, Dayan I, Kalra MK, and Li Q
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- Electronic Health Records, Humans, Lung, Prognosis, SARS-CoV-2, Tomography, X-Ray Computed, COVID-19, Deep Learning
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Purpose: As of August 30th, there were in total 25.1 million confirmed cases and 845 thousand deaths caused by coronavirus disease of 2019 (COVID-19) worldwide. With overwhelming demands on medical resources, patient stratification based on their risks is essential. In this multi-center study, we built prognosis models to predict severity outcomes, combining patients' electronic health records (EHR), which included vital signs and laboratory data, with deep learning- and CT-based severity prediction., Method: We first developed a CT segmentation network using datasets from multiple institutions worldwide. Two biomarkers were extracted from the CT images: total opacity ratio (TOR) and consolidation ratio (CR). After obtaining TOR and CR, further prognosis analysis was conducted on datasets from INSTITUTE-1, INSTITUTE-2 and INSTITUTE-3. For each data cohort, generalized linear model (GLM) was applied for prognosis prediction., Results: For the deep learning model, the correlation coefficient of the network prediction and manual segmentation was 0.755, 0.919, and 0.824 for the three cohorts, respectively. The AUC (95 % CI) of the final prognosis models was 0.85(0.77,0.92), 0.93(0.87,0.98), and 0.86(0.75,0.94) for INSTITUTE-1, INSTITUTE-2 and INSTITUTE-3 cohorts, respectively. Either TOR or CR exist in all three final prognosis models. Age, white blood cell (WBC), and platelet (PLT) were chosen predictors in two cohorts. Oxygen saturation (SpO2) was a chosen predictor in one cohort., Conclusion: The developed deep learning method can segment lung infection regions. Prognosis results indicated that age, SpO2, CT biomarkers, PLT, and WBC were the most important prognostic predictors of COVID-19 in our prognosis model., (Copyright © 2021. Published by Elsevier B.V.)
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- 2021
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14. Federated Learning used for predicting outcomes in SARS-COV-2 patients.
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Flores M, Dayan I, Roth H, Zhong A, Harouni A, Gentili A, Abidin A, Liu A, Costa A, Wood B, Tsai CS, Wang CH, Hsu CN, Lee CK, Ruan C, Xu D, Wu D, Huang E, Kitamura F, Lacey G, César de Antônio Corradi G, Shin HH, Obinata H, Ren H, Crane J, Tetreault J, Guan J, Garrett J, Park JG, Dreyer K, Juluru K, Kersten K, Bezerra Cavalcanti Rockenbach MA, Linguraru M, Haider M, AbdelMaseeh M, Rieke N, Damasceno P, Cruz E Silva PM, Wang P, Xu S, Kawano S, Sriswasdi S, Park SY, Grist T, Buch V, Jantarabenjakul W, Wang W, Tak WY, Li X, Lin X, Kwon F, Gilbert F, Kaggie J, Li Q, Quraini A, Feng A, Priest A, Turkbey B, Glicksberg B, Bizzo B, Kim BS, Tor-Diez C, Lee CC, Hsu CJ, Lin C, Lai CL, Hess C, Compas C, Bhatia D, Oermann E, Leibovitz E, Sasaki H, Mori H, Yang I, Sohn JH, Keshava Murthy KN, Fu LC, Furtado de Mendonça MR, Fralick M, Kang MK, Adil M, Gangai N, Vateekul P, Elnajjar P, Hickman S, Majumdar S, McLeod S, Reed S, Graf S, Harmon S, Kodama T, Puthanakit T, Mazzulli T, de Lima Lavor V, Rakvongthai Y, Lee YR, and Wen Y
- Abstract
'Federated Learning' (FL) is a method to train Artificial Intelligence (AI) models with data from multiple sources while maintaining anonymity of the data thus removing many barriers to data sharing. During the SARS-COV-2 pandemic, 20 institutes collaborated on a healthcare FL study to predict future oxygen requirements of infected patients using inputs of vital signs, laboratory data, and chest x-rays, constituting the "EXAM" (EMR CXR AI Model) model. EXAM achieved an average Area Under the Curve (AUC) of over 0.92, an average improvement of 16%, and a 38% increase in generalisability over local models. The FL paradigm was successfully applied to facilitate a rapid data science collaboration without data exchange, resulting in a model that generalised across heterogeneous, unharmonized datasets. This provided the broader healthcare community with a validated model to respond to COVID-19 challenges, as well as set the stage for broader use of FL in healthcare.
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- 2021
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15. Severity and Consolidation Quantification of COVID-19 From CT Images Using Deep Learning Based on Hybrid Weak Labels.
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Wu D, Gong K, Arru CD, Homayounieh F, Bizzo B, Buch V, Ren H, Kim K, Neumark N, Xu P, Liu Z, Fang W, Xie N, Tak WY, Park SY, Lee YR, Kang MK, Park JG, Carriero A, Saba L, Masjedi M, Talari H, Babaei R, Mobin HK, Ebrahimian S, Dayan I, Kalra MK, and Li Q
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- Algorithms, COVID-19 virology, Female, Humans, Male, Retrospective Studies, SARS-CoV-2 isolation & purification, Severity of Illness Index, COVID-19 diagnostic imaging, Deep Learning, Tomography, X-Ray Computed methods
- Abstract
Early and accurate diagnosis of Coronavirus disease (COVID-19) is essential for patient isolation and contact tracing so that the spread of infection can be limited. Computed tomography (CT) can provide important information in COVID-19, especially for patients with moderate to severe disease as well as those with worsening cardiopulmonary status. As an automatic tool, deep learning methods can be utilized to perform semantic segmentation of affected lung regions, which is important to establish disease severity and prognosis prediction. Both the extent and type of pulmonary opacities help assess disease severity. However, manually pixel-level multi-class labelling is time-consuming, subjective, and non-quantitative. In this article, we proposed a hybrid weak label-based deep learning method that utilize both the manually annotated pulmonary opacities from COVID-19 pneumonia and the patient-level disease-type information available from the clinical report. A UNet was firstly trained with semantic labels to segment the total infected region. It was used to initialize another UNet, which was trained to segment the consolidations with patient-level information using the Expectation-Maximization (EM) algorithm. To demonstrate the performance of the proposed method, multi-institutional CT datasets from Iran, Italy, South Korea, and the United States were utilized. Results show that our proposed method can predict the infected regions as well as the consolidation regions with good correlation to human annotation.
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- 2020
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16. Improvement and Multi-Population Generalizability of a Deep Learning-Based Chest Radiograph Severity Score for COVID-19.
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Li MD, Arun NT, Aggarwal M, Gupta S, Singh P, Little BP, Mendoza DP, Corradi GCA, Takahashi MS, Ferraciolli SF, Succi MD, Lang M, Bizzo BC, Dayan I, Kitamura FC, and Kalpathy-Cramer J
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Purpose: To improve and test the generalizability of a deep learning-based model for assessment of COVID-19 lung disease severity on chest radiographs (CXRs) from different patient populations., Materials and Methods: A published convolutional Siamese neural network-based model previously trained on hospitalized patients with COVID-19 was tuned using 250 outpatient CXRs. This model produces a quantitative measure of COVID-19 lung disease severity (pulmonary x-ray severity (PXS) score). The model was evaluated on CXRs from four test sets, including 3 from the United States (patients hospitalized at an academic medical center (N=154), patients hospitalized at a community hospital (N=113), and outpatients (N=108)) and 1 from Brazil (patients at an academic medical center emergency department (N=303)). Radiologists from both countries independently assigned reference standard CXR severity scores, which were correlated with the PXS scores as a measure of model performance (Pearson r). The Uniform Manifold Approximation and Projection (UMAP) technique was used to visualize the neural network results., Results: Tuning the deep learning model with outpatient data improved model performance in two United States hospitalized patient datasets (r=0.88 and r=0.90, compared to baseline r=0.86). Model performance was similar, though slightly lower, when tested on the United States outpatient and Brazil emergency department datasets (r=0.86 and r=0.85, respectively). UMAP showed that the model learned disease severity information that generalized across test sets., Conclusions: Performance of a deep learning-based model that extracts a COVID-19 severity score on CXRs improved using training data from a different patient cohort (outpatient versus hospitalized) and generalized across multiple populations.
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- 2020
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