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1. Implementation and prospective real-time evaluation of a generalized system for in-clinic deployment and validation of machine learning models in radiology.

Author

Majumdar, Sharmila, Crane, Jason, Hawkins, James, Olson, Marram, Harouni, Ahmed, Qin, Ming, and Hess, Christopher

Abstract

The medical imaging community has embraced Machine Learning (ML) as evidenced by the rapid increase in the number of ML models being developed, but validating and deploying these models in the clinic remains a challenge. The engineering involved in integrating and assessing the efficacy of ML models within the clinical workflow is complex. This paper presents a general-purpose, end-to-end, clinically integrated ML model deployment and validation system implemented at UCSF. Engineering and usability challenges and results from 3 use cases are presented. A generalized validation system based on free, open-source software (OSS) was implemented, connecting clinical imaging modalities, the Picture Archiving and Communication System (PACS), and an ML inference server. ML pipelines were implemented in NVIDIAs Clara Deploy framework with results and clinician feedback stored in a customized XNAT instance, separate from the clinical record but linked from within PACS. Prospective clinical validation studies of 3 ML models were conducted, with data routed from multiple clinical imaging modalities and PACS. Completed validation studies provided expert clinical feedback on model performance and usability, plus system reliability and performance metrics. Clinical validation of ML models entails assessing model performance, impact on clinical infrastructure, robustness, and usability. Study results must be easily accessible to participating clinicians but remain outside the clinical record. Building a system that generalizes and scales across multiple ML models takes the concerted effort of software engineers, clinicians, data scientists, and system administrators, and benefits from the use of modular OSS. The present work provides a template for institutions looking to translate and clinically validate ML models in the clinic, together with required resources and expected challenges.

Published
2023

2. NVIDIA FLARE: Federated Learning from Simulation to Real-World

Author

Roth, Holger R., Cheng, Yan, Wen, Yuhong, Yang, Isaac, Xu, Ziyue, Hsieh, Yuan-Ting, Kersten, Kristopher, Harouni, Ahmed, Zhao, Can, Lu, Kevin, Zhang, Zhihong, Li, Wenqi, Myronenko, Andriy, Yang, Dong, Yang, Sean, Rieke, Nicola, Quraini, Abood, Chen, Chester, Xu, Daguang, Ma, Nic, Dogra, Prerna, Flores, Mona, and Feng, Andrew

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Networking and Internet Architecture, Computer Science - Software Engineering
Abstract

Federated learning (FL) enables building robust and generalizable AI models by leveraging diverse datasets from multiple collaborators without centralizing the data. We created NVIDIA FLARE as an open-source software development kit (SDK) to make it easier for data scientists to use FL in their research and real-world applications. The SDK includes solutions for state-of-the-art FL algorithms and federated machine learning approaches, which facilitate building workflows for distributed learning across enterprises and enable platform developers to create a secure, privacy-preserving offering for multiparty collaboration utilizing homomorphic encryption or differential privacy. The SDK is a lightweight, flexible, and scalable Python package. It allows researchers to apply their data science workflows in any training libraries (PyTorch, TensorFlow, XGBoost, or even NumPy) in real-world FL settings. This paper introduces the key design principles of NVFlare and illustrates some use cases (e.g., COVID analysis) with customizable FL workflows that implement different privacy-preserving algorithms. Code is available at https://github.com/NVIDIA/NVFlare., Comment: Accepted at the International Workshop on Federated Learning, NeurIPS 2022, New Orleans, USA (https://federated-learning.org/fl-neurips-2022); Revised version v2: added Key Components list, system metrics for homomorphic encryption experiment; Extended v3 for journal submission

Published
2022
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3. Federated learning for predicting clinical outcomes in patients with COVID-19

Author

Dayan, Ittai, Roth, Holger R, Zhong, Aoxiao, Harouni, Ahmed, Gentili, Amilcare, Abidin, Anas Z, Liu, Andrew, Costa, Anthony Beardsworth, Wood, Bradford J, Tsai, Chien-Sung, Wang, Chih-Hung, Hsu, Chun-Nan, Lee, CK, Ruan, Peiying, Xu, Daguang, Wu, Dufan, Huang, Eddie, Kitamura, Felipe Campos, Lacey, Griffin, de Antônio Corradi, Gustavo César, Nino, Gustavo, Shin, Hao-Hsin, Obinata, Hirofumi, Ren, Hui, Crane, Jason C, Tetreault, Jesse, Guan, Jiahui, Garrett, John W, Kaggie, Joshua D, Park, Jung Gil, Dreyer, Keith, Juluru, Krishna, Kersten, Kristopher, Rockenbach, Marcio Aloisio Bezerra Cavalcanti, Linguraru, Marius George, Haider, Masoom A, AbdelMaseeh, Meena, Rieke, Nicola, Damasceno, Pablo F, e Silva, Pedro Mario Cruz, Wang, Pochuan, Xu, Sheng, Kawano, Shuichi, Sriswasdi, Sira, Park, Soo Young, Grist, Thomas M, Buch, Varun, Jantarabenjakul, Watsamon, Wang, Weichung, Tak, Won Young, Li, Xiang, Lin, Xihong, Kwon, Young Joon, Quraini, Abood, Feng, Andrew, Priest, Andrew N, Turkbey, Baris, Glicksberg, Benjamin, Bizzo, Bernardo, Kim, Byung Seok, Tor-Díez, Carlos, Lee, Chia-Cheng, Hsu, Chia-Jung, Lin, Chin, Lai, Chiu-Ling, Hess, Christopher P, Compas, Colin, Bhatia, Deepeksha, Oermann, Eric K, Leibovitz, Evan, Sasaki, Hisashi, Mori, Hitoshi, Yang, Isaac, Sohn, Jae Ho, Murthy, Krishna Nand Keshava, Fu, Li-Chen, de Mendonça, Matheus Ribeiro Furtado, Fralick, Mike, Kang, Min Kyu, Adil, Mohammad, Gangai, Natalie, Vateekul, Peerapon, Elnajjar, Pierre, Hickman, Sarah, Majumdar, Sharmila, McLeod, Shelley L, Reed, Sheridan, Gräf, Stefan, Harmon, Stephanie, Kodama, Tatsuya, Puthanakit, Thanyawee, Mazzulli, Tony, de Lavor, Vitor Lima, Rakvongthai, Yothin, Lee, Yu Rim, Wen, Yuhong, Gilbert, Fiona J, Flores, Mona G, and Li, Quanzheng

Subjects
Patient Safety, Good Health and Well Being, COVID-19, Electronic Health Records, Humans, Machine Learning, Outcome Assessment, Health Care, Prognosis, SARS-CoV-2, Medical and Health Sciences, Immunology
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.

Published
2021

4. Federated Learning for Breast Density Classification: A Real-World Implementation

Author

Roth, Holger R., Chang, Ken, Singh, Praveer, Neumark, Nir, Li, Wenqi, Gupta, Vikash, Gupta, Sharut, Qu, Liangqiong, Ihsani, Alvin, Bizzo, Bernardo C., Wen, Yuhong, Buch, Varun, Shah, Meesam, Kitamura, Felipe, Mendonça, Matheus, Lavor, Vitor, Harouni, Ahmed, Compas, Colin, Tetreault, Jesse, Dogra, Prerna, Cheng, Yan, Erdal, Selnur, White, Richard, Hashemian, Behrooz, Schultz, Thomas, Zhang, Miao, McCarthy, Adam, Yun, B. Min, Sharaf, Elshaimaa, Hoebel, Katharina V., Patel, Jay B., Chen, Bryan, Ko, Sean, Leibovitz, Evan, Pisano, Etta D., Coombs, Laura, Xu, Daguang, Dreyer, Keith J., Dayan, Ittai, Naidu, Ram C., Flores, Mona, Rubin, Daniel, and Kalpathy-Cramer, Jayashree

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Building robust deep learning-based models requires large quantities of diverse training data. In this study, we investigate the use of federated learning (FL) to build medical imaging classification models in a real-world collaborative setting. Seven clinical institutions from across the world joined this FL effort to train a model for breast density classification based on Breast Imaging, Reporting & Data System (BI-RADS). We show that despite substantial differences among the datasets from all sites (mammography system, class distribution, and data set size) and without centralizing data, we can successfully train AI models in federation. The results show that models trained using FL perform 6.3% on average better than their counterparts trained on an institute's local data alone. Furthermore, we show a 45.8% relative improvement in the models' generalizability when evaluated on the other participating sites' testing data., Comment: Accepted at the 1st MICCAI Workshop on "Distributed And Collaborative Learning"; add citation to Fig. 1 & 2 and update Fig. 5; fix typo in affiliations

Published
2020
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6. Integrating Deformable Modeling with 3D Deep Neural Network Segmentation

Author

Tang, Hui, Moradi, Mehdi, Wong, Ken C. L., Wang, Hongzhi, Harouni, Ahmed El, Syeda-Mahmood, Tanveer, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Stoyanov, Danail, editor, Taylor, Zeike, editor, Carneiro, Gustavo, editor, Syeda-Mahmood, Tanveer, editor, Martel, Anne, editor, Maier-Hein, Lena, editor, Tavares, João Manuel R.S., editor, Bradley, Andrew, editor, Papa, João Paulo, editor, Belagiannis, Vasileios, editor, Nascimento, Jacinto C., editor, Lu, Zhi, editor, Conjeti, Sailesh, editor, Moradi, Mehdi, editor, Greenspan, Hayit, editor, and Madabhushi, Anant, editor

Published
2018
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7. Federated Learning for Breast Density Classification: A Real-World Implementation

Author

Roth, Holger R., primary, Chang, Ken, additional, Singh, Praveer, additional, Neumark, Nir, additional, Li, Wenqi, additional, Gupta, Vikash, additional, Gupta, Sharut, additional, Qu, Liangqiong, additional, Ihsani, Alvin, additional, Bizzo, Bernardo C., additional, Wen, Yuhong, additional, Buch, Varun, additional, Shah, Meesam, additional, Kitamura, Felipe, additional, Mendonça, Matheus, additional, Lavor, Vitor, additional, Harouni, Ahmed, additional, Compas, Colin, additional, Tetreault, Jesse, additional, Dogra, Prerna, additional, Cheng, Yan, additional, Erdal, Selnur, additional, White, Richard, additional, Hashemian, Behrooz, additional, Schultz, Thomas, additional, Zhang, Miao, additional, McCarthy, Adam, additional, Yun, B. Min, additional, Sharaf, Elshaimaa, additional, Hoebel, Katharina V., additional, Patel, Jay B., additional, Chen, Bryan, additional, Ko, Sean, additional, Leibovitz, Evan, additional, Pisano, Etta D., additional, Coombs, Laura, additional, Xu, Daguang, additional, Dreyer, Keith J., additional, Dayan, Ittai, additional, Naidu, Ram C., additional, Flores, Mona, additional, Rubin, Daniel, additional, and Kalpathy-Cramer, Jayashree, additional

Published
2020
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9. Machine learning in action: Revolutionizing intracranial hematoma detection and patient transport decision-making.

Author

El Refaee, Ehab, Ali, Taher M., Al Menabbawy, Ahmed, Elfiky, Mahmoud, El Fiki, Ahmed, Mashhour, Shady, and Harouni, Ahmed

Subjects
MACHINE learning, INTRACRANIAL hematoma, TRANSPORTATION of patients, INTRACRANIAL hemorrhage, ARTIFICIAL intelligence, HOSPITAL care
Abstract

Objectives: Traumatic intracranial hematomas represent a critical clinical situation where early detection and management are of utmost importance. Machine learning has been recently used in the detection of neuroradiological findings. Hence, it can be used in the detection of intracranial hematomas and furtherly initiate a management cascade of patient transfer, diagnostics, admission, and emergency intervention. We aim, here, to develop a diagnostic tool based on artificial intelligence to detect hematomas instantaneously, and automatically start a cascade of actions that support the management protocol depending on the early diagnosis. Materials and Methods: A plot was designed as a staged model: The first stage of initiating and training the machine with the provisional evaluation of its accuracy and the second stage of supervised use in a tertiary care hospital and a third stage of its generalization in primary and secondary care hospitals. Two datasets were used: CQ500, a public dataset, and our dataset collected retrospectively from our tertiary hospital. Results: A mean dice score of 0.83 was achieved on the validation set of CQ500. Moreover, the detection of intracranial hemorrhage was successful in 94% of cases for the CQ500 test set and 93% for our local institute cases. Poor detection was present in only 6-7% of the total test set. Moderate false-positive results were encountered in 18% and major false positives reached 5% for the total test set. Conclusion: The proposed approach for the early detection of acute intracranial hematomas provides a reliable outset for generating an automatically initiated management cascade in high-flow hospitals. [ABSTRACT FROM AUTHOR]

Published
2024
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12. Finding the Optimal Compression Level for Strain-Encoded (SENC) Breast MRI; Simulations and Phantom Experiments

Author

Harouni, Ahmed A., Jacobs, Michael A., Osman, Nael F., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Fichtinger, Gabor, editor, Martel, Anne, editor, and Peters, Terry, editor

Published
2011
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13. Rapid artificial intelligence solutions in a pandemic—The COVID-19-20 Lung CT Lesion Segmentation Challenge

Author

Roth, Holger R., primary, Xu, Ziyue, additional, Tor-Díez, Carlos, additional, Sanchez Jacob, Ramon, additional, Zember, Jonathan, additional, Molto, Jose, additional, Li, Wenqi, additional, Xu, Sheng, additional, Turkbey, Baris, additional, Turkbey, Evrim, additional, Yang, Dong, additional, Harouni, Ahmed, additional, Rieke, Nicola, additional, Hu, Shishuai, additional, Isensee, Fabian, additional, Tang, Claire, additional, Yu, Qinji, additional, Sölter, Jan, additional, Zheng, Tong, additional, Liauchuk, Vitali, additional, Zhou, Ziqi, additional, Moltz, Jan Hendrik, additional, Oliveira, Bruno, additional, Xia, Yong, additional, Maier-Hein, Klaus H., additional, Li, Qikai, additional, Husch, Andreas, additional, Zhang, Luyang, additional, Kovalev, Vassili, additional, Kang, Li, additional, Hering, Alessa, additional, Vilaça, João L., additional, Flores, Mona, additional, Xu, Daguang, additional, Wood, Bradford, additional, and Linguraru, Marius George, additional

Published
2022
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14. Rapid artificial intelligence solutions in a pandemic—The COVID-19-20 Lung CT Lesion Segmentation Challenge

Author

Roth, Holger R., Xu, Ziyue, Diez, Carlos Tor, Jacob, Ramon Sanchez, Zember, Jonathan, Molto, Jose, Li, Wenqi, Xu, Sheng, Turkbey, Baris, Turkbey, Evrim, Yang, Dong, Harouni, Ahmed, Rieke, Nicola, Hu, Shishuai, Isensee, Fabian, Tang, Claire, Yu, Qinji, Sölter, Jan, Zheng, Tong, Liauchuk, Vitali, Zhou, Ziqi, Moltz, Jan Hendrik, Oliveira, Bruno, Xia, Yong, Maier-Hein, Klaus H., Li, Qikai, Husch, Andreas, Zhang, Luyang, Kovalev, Vassili, Kang, Li, Hering, Alessa, Vilaça, João L., Flores, Mona, Xu, Daguang, Wood, Bradford, Linguraru, Marius George, Roth, Holger R., Xu, Ziyue, Diez, Carlos Tor, Jacob, Ramon Sanchez, Zember, Jonathan, Molto, Jose, Li, Wenqi, Xu, Sheng, Turkbey, Baris, Turkbey, Evrim, Yang, Dong, Harouni, Ahmed, Rieke, Nicola, Hu, Shishuai, Isensee, Fabian, Tang, Claire, Yu, Qinji, Sölter, Jan, Zheng, Tong, Liauchuk, Vitali, Zhou, Ziqi, Moltz, Jan Hendrik, Oliveira, Bruno, Xia, Yong, Maier-Hein, Klaus H., Li, Qikai, Husch, Andreas, Zhang, Luyang, Kovalev, Vassili, Kang, Li, Hering, Alessa, Vilaça, João L., Flores, Mona, Xu, Daguang, Wood, Bradford, and Linguraru, Marius George

Abstract

Artificial intelligence (AI) methods for the automatic detection and quantification of COVID-19 lesions in chest computed tomography (CT) might play an important role in the monitoring and management of the disease. We organized an international challenge and competition for the development and comparison of AI algorithms for this task, which we supported with public data and state-of-the-art benchmark methods. Board Certified Radiologists annotated 295 public images from two sources (A and B) for algorithms training (n=199, source A), validation (n=50, source A) and testing (n=23, source A; n=23, source B). There were 1,096 registered teams of which 225 and 98 completed the validation and testing phases, respectively. The challenge showed that AI models could be rapidly designed by diverse teams with the potential to measure disease or facilitate timely and patient-specific interventions. This paper provides an overview and the major outcomes of the COVID-19 Lung CT Lesion Segmentation Challenge - 2020.

Published
2022

15. Integrating the OHIF Viewer into XNAT: Achievements, Challenges and Prospects for Quantitative Imaging Studies

Author

Doran, Simon J, Al Sa'd, Mohammad, Petts, James A, Darcy, James, Alpert, Kate, Cho, Woonchan, Sanchez, Lorena Escudero, Alle, Sachidanand, El Harouni, Ahmed, Genereaux, Brad, Ziegler, Erik, Harris, Gordon J, Aboagye, Eric O, Sala, Evis, Koh, Dow-Mu, Marcus, Dan, Doran, Simon J [0000-0001-8569-9188], Sanchez, Lorena Escudero [0000-0003-3464-9206], Ziegler, Erik [0000-0003-1857-8129], Aboagye, Eric O [0000-0003-2276-6771], Marcus, Dan [0000-0001-5662-5358], and Apollo - University of Cambridge Repository

Subjects
Diagnostic Imaging, XNAT, OHIF, web viewer, image visualisation, regions-of-interest, rapid reader, Archives, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Artificial Intelligence, Humans, Radiology, Nuclear Medicine and imaging, Software
Abstract

Purpose: XNAT is an informatics software platform to support imaging research, particularly in the context of large, multicentre studies of the type that are essential to validate quantitative imaging biomarkers. XNAT provides import, archiving, processing and secure distribution facilities for image and related study data. Until recently, however, modern data visualisation and annotation tools were lacking on the XNAT platform. We describe the background to, and implementation of, an integration of the Open Health Imaging Foundation (OHIF) Viewer into the XNAT environment. We explain the challenges overcome and discuss future prospects for quantitative imaging studies. Materials and methods: The OHIF Viewer adopts an approach based on the DICOM web protocol. To allow operation in an XNAT environment, a data-routing methodology was developed to overcome the mismatch between the DICOM and XNAT information models and a custom viewer panel created to allow navigation within the viewer between different XNAT projects, subjects and imaging sessions. Modifications to the development environment were made to allow developers to test new code more easily against a live XNAT instance. Major new developments focused on the creation and storage of regions-of-interest (ROIs) and included: ROI creation and editing tools for both contour- and mask-based regions; a “smart CT” paintbrush tool; the integration of NVIDIA’s Artificial Intelligence Assisted Annotation (AIAA); the ability to view surface meshes, fractional segmentation maps and image overlays; and a rapid image reader tool aimed at radiologists. We have incorporated the OHIF microscopy extension and, in parallel, introduced support for microscopy session types within XNAT for the first time. Results: Integration of the OHIF Viewer within XNAT has been highly successful and numerous additional and enhanced tools have been created in a programme started in 2017 that is still ongoing. The software has been downloaded more than 3700 times during the course of the development work reported here, demonstrating the impact of the work. Conclusions: The OHIF open-source, zero-footprint web viewer has been incorporated into the XNAT platform and is now used at many institutions worldwide. Further innovations are envisaged in the near future.

Published
2021

18. Integrating the OHIF Viewer into XNAT: Achievements, Challenges and Prospects for Quantitative Imaging Studies

Author

Doran, Simon, primary, Al Sa’d, Mohammad, additional, Petts, James, additional, Darcy, James, additional, Alpert, Kate, additional, Cho, Woonchan, additional, Escudero Sanchez, Lorena, additional, Alle, Sachidanand, additional, El Harouni, Ahmed, additional, Genereaux, Brad, additional, Ziegler, Erik, additional, Harris, Gordon, additional, Aboagye, Eric, additional, Sala, Evis, additional, Koh, Dow-Mu, additional, and Marcus, Dan, additional

Published
2022
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19. Rapid Artificial Intelligence Solutions in a Pandemic - The COVID-19-20 Lung CT Lesion Segmentation Challenge

Author

Roth, Holger, primary, Xu, Ziyue, additional, Diez, Carlos Tor, additional, Jacob, Ramon Sanchez, additional, Zember, Jonathan, additional, Molto, Jose, additional, Li, Wenqi, additional, Xu, Sheng, additional, Turkbey, Baris, additional, Turkbey, Evrim, additional, Yang, Dong, additional, Harouni, Ahmed, additional, Rieke, Nicola, additional, Hu, Shishuai, additional, Isensee, Fabian, additional, Tang, Claire, additional, Yu, Qinji, additional, Sölter, Jan, additional, Zheng, Tong, additional, Liauchuk, Vitali, additional, Zhou, Ziqi, additional, Moltz, Jan, additional, Oliveira, Bruno, additional, Xia, Yong, additional, Maier-Hein, Klaus, additional, Li, Qikai, additional, Husch, Andreas, additional, Zhang, Luyang, additional, Kovalev, Vassili, additional, Kang, Li, additional, Hering, Alessa, additional, Vilaça, João, additional, Flores, Mona, additional, Xu, Daguang, additional, Wood, Bradford, additional, and Linguraru, Marius, additional

Published
2021
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20. Rapid Artificial Intelligence Solutions in a Pandemic - The COVID-19-20 Lung CT Lesion Segmentation Challenge.

Author

Roth, Holger, Xu, Ziyue, Diez, Carlos Tor, Jacob, Ramon Sanchez, Zember, Jonathan, Molto, Jose, Li, Wenqi, Xu, Sheng, Turkbey, Baris, Turkbey, Evrim, Yang, Dong, Harouni, Ahmed, Rieke, Nicola, Hu, Shishuai, Isensee, Fabian, Tang, Claire, Yu, Qinji, Sölter, Jan, Zheng, Tong, Liauchuk, Vitali, Zhou, Ziqi, Moltz, Jan, Oliveira, Bruno, Xia, Yong, Maier-Hein, Klaus, Li, Qikai, Husch, Andreas, Zhang, Luyang, Kovalev, Vassili, Kang, Li, Hering, Alessa, Vilaça, João, Flores, Mona, Xu, Daguang, Wood, Bradford, Linguraru, Marius, Roth, Holger, Xu, Ziyue, Diez, Carlos Tor, Jacob, Ramon Sanchez, Zember, Jonathan, Molto, Jose, Li, Wenqi, Xu, Sheng, Turkbey, Baris, Turkbey, Evrim, Yang, Dong, Harouni, Ahmed, Rieke, Nicola, Hu, Shishuai, Isensee, Fabian, Tang, Claire, Yu, Qinji, Sölter, Jan, Zheng, Tong, Liauchuk, Vitali, Zhou, Ziqi, Moltz, Jan, Oliveira, Bruno, Xia, Yong, Maier-Hein, Klaus, Li, Qikai, Husch, Andreas, Zhang, Luyang, Kovalev, Vassili, Kang, Li, Hering, Alessa, Vilaça, João, Flores, Mona, Xu, Daguang, Wood, Bradford, and Linguraru, Marius

Abstract

Artificial intelligence (AI) methods for the automatic detection and quantification of COVID-19 lesions in chest computed tomography (CT) might play an important role in the monitoring and management of the disease. We organized an international challenge and competition for the development and comparison of AI algorithms for this task, which we supported with public data and state-of-the-art benchmark methods. Board Certified Radiologists annotated 295 public images from two sources (A and B) for algorithms training (n=199, source A), validation (n=50, source A) and testing (n=23, source A; n=23, source B). There were 1,096 registered teams of which 225 and 98 completed the validation and testing phases, respectively. The challenge showed that AI models could be rapidly designed by diverse teams with the potential to measure disease or facilitate timely and patient-specific interventions. This paper provides an overview and the major outcomes of the COVID-19 Lung CT Lesion Segmentation Challenge - 2020.

Published
2021

23. Federated Learning used for predicting outcomes in SARS-COV-2 patients

Author

Flores, Mona, primary, Dayan, Ittai, additional, Roth, Holger, additional, Zhong, Aoxiao, additional, Harouni, Ahmed, additional, Gentili, Amilcare, additional, Abidin, Anas, additional, Liu, Andrew, additional, Costa, Anthony, additional, Wood, Bradford, additional, Tsai, Chien-Sung, additional, Wang, Chih-Hung, additional, Hsu, Chun-Nan, additional, Lee, CK, additional, Ruan, Colleen, additional, Xu, Daguang, additional, Wu, Dufan, additional, Huang, Eddie, additional, Kitamura, Felipe, additional, Lacey, Griffin, additional, Corradi, Gustavo César de Antônio, additional, Shin, Hao-Hsin, additional, Obinata, Hirofumi, additional, Ren, Hui, additional, Crane, Jason, additional, Tetreault, Jesse, additional, Guan, Jiahui, additional, Garrett, John, additional, Park, Jung Gil, additional, Dreyer, Keith, additional, Juluru, Krishna, additional, Kersten, Kristopher, additional, Rockenbach, Marcio Aloisio Bezerra Cavalcanti, additional, Linguraru, Marius, additional, Haider, Masoom, additional, AbdelMaseeh, Meena, additional, Rieke, Nicola, additional, Damasceno, Pablo, additional, Silva, Pedro Mario Cruz e, additional, Wang, Pochuan, additional, Xu, Sheng, additional, Kawano, Shuichi, additional, Sriswa, Sira, additional, Park, Soo Young, additional, Grist, Thomas, additional, Buch, Varun, additional, Jantarabenjakul, Watsamon, additional, Wang, Weichung, additional, Tak, Won Young, additional, Li, Xiang, additional, Lin, Xihong, additional, Kwon, Fred, additional, Gilbert, Fiona, additional, Kaggie, Josh, additional, Li, Quanzheng, additional, Quraini, Abood, additional, Feng, Andrew, additional, Priest, Andrew, additional, Turkbey, Baris, additional, Glicksberg, Benjamin, additional, Bizzo, Bernardo, additional, Kim, Byung Seok, additional, Tor-Diez, Carlos, additional, Lee, Chia-Cheng, additional, Hsu, Chia-Jung, additional, Lin, Chin, additional, Lai, Chiu-Ling, additional, Hess, Christopher, additional, Compas, Colin, additional, Bhatia, Deepi, additional, Oermann, Eric, additional, Leibovitz, Evan, additional, Sasaki, Hisashi, additional, Mori, Hitoshi, additional, Yang, Isaac, additional, Sohn, Jae Ho, additional, Murthy, Krishna Nand Keshava, additional, Fu, Li-Chen, additional, de Mendonça, Matheus Ribeiro Furtado, additional, Fralick, Mike, additional, Kang, Min Kyu, additional, Adil, Mohammad, additional, Gangai, Natalie, additional, Vateekul, Peerapon, additional, Elnajjar, Pierre, additional, Hickman, Sarah, additional, Majumdar, Sharmila, additional, McLeod, Shelley, additional, Reed, Sheridan, additional, Graf, Stefan, additional, Harmon, Stephanie, additional, Kodama, Tatsuya, additional, Puthanakit, Thanyawee, additional, Mazzulli, Tony, additional, Lavor, Vitor de Lima, additional, Rakvongthai, Yothin, additional, Lee, Yu Rim, additional, and Wen, Yuhong, additional

Published
2021
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31. Assessment of Liver Fibrosis Using Fast Strain-Encoded (FSENC) MRI Driven by Inherent Cardiac Motion

Author

Harouni, Ahmed A., Gharib, Ahmed M., Osman, Nael F., Morse, Caryn, Heller, Theo, and Abd-Elmoniem, Khaled Z.

Subjects
Article
Abstract

An external driver-free MRI method for assessment of liver fibrosis offers a promising noninvasive tool for diagnosis and monitoring of liver disease. Lately, the heart's intrinsic motion and MR tagging have been utilized for the quantification of liver strain. However, MR tagging requires multiple breath-hold acquisitions and substantial postprocessing. In this study, we propose the use of a fast strain-encoded (FSENC) MRI method to measure the peak strain (SPhantom and in vivo experiments (11 healthy subjects and 11 patients with liver fibrosis) were conducted. Reproducibility experiments were performed in seven healthy subjects.Peak liver strain, SAccelerated external driver-free noninvasive assessment of left liver lobe strain and SWV is feasible using strain-encoded MRI. The two measures significantly separate healthy subjects from patients with fibrotic liver. Magn Reson Med 74:106-114, 2015. © 2014 Wiley Periodicals, Inc.

Published
2014

34. Regional and Global Biventricular Function in Pulmonary Arterial Hypertension: A Cardiac MR Imaging Study

Author

Shehata, Monda L., primary, Harouni, Ahmed A., additional, Skrok, Jan, additional, Basha, Tamer A., additional, Boyce, Danielle, additional, Lechtzin, Noah, additional, Mathai, Stephen C., additional, Girgis, Reda, additional, Osman, Nael F., additional, Lima, João A. C., additional, Bluemke, David A., additional, Hassoun, Paul M., additional, and Vogel-Claussen, Jens, additional

Published
2013
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36. Regional longitudinal bi-ventricular function in pulmonary hypertension: single heart-beat assessment of strain by fast-senc imaging

Author

Shehata, Monda L, primary, Basha, Tamer A, additional, Harouni, Ahmed A, additional, Skrok, Jan, additional, Singh, Sukhminder, additional, Mathai, Stephen, additional, Lima, Joao AC, additional, Osman, Nael F, additional, Bluemke, David A, additional, Hassoun, Paul M, additional, and Vogel-Claussen, Jens, additional

Published
2011
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40. Assessment of liver fibrosis using fast strain-encoded MRI driven by inherent cardiac motion.

Author

Harouni, Ahmed A., Gharib, Ahmed M., Osman, Nael F., Morse, Caryn, Heller, Theo, and Abd‐Elmoniem, Khaled Z.

Abstract

Purpose An external driver-free MRI method for assessment of liver fibrosis offers a promising noninvasive tool for diagnosis and monitoring of liver disease. Lately, the heart's intrinsic motion and MR tagging have been utilized for the quantification of liver strain. However, MR tagging requires multiple breath-hold acquisitions and substantial postprocessing. In this study, we propose the use of a fast strain-encoded (FSENC) MRI method to measure the peak strain (S p) in the liver's left lobe, which is in close proximity and caudal to the heart. Additionally, we introduce a new method of measuring heart-induced shear wave velocity (SWV) inside the liver. Methods Phantom and in vivo experiments (11 healthy subjects and 11 patients with liver fibrosis) were conducted. Reproducibility experiments were performed in seven healthy subjects. Results Peak liver strain, S p, decreased significantly in fibrotic liver compared with healthy liver (6.46% ± 2.27% vs 12.49% ± 1.76%; P < 0.05). Heart-induced SWV increased significantly in patients compared with healthy subjects (0.15 ± 0.04 m/s vs 0.63 ± 0.32 m/s; P < 0.05). Reproducibility analysis yielded no significant difference in S p ( P = 0.47) or SWV ( P = 0.56). Conclusion Accelerated external driver-free noninvasive assessment of left liver lobe strain and SWV is feasible using strain-encoded MRI. The two measures significantly separate healthy subjects from patients with fibrotic liver. Magn Reson Med 74:106-114, 2015. © 2014 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Published
2015
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41. Age-Related Changes in Aortic Arch Geometry Relationship With Proximal Aortic Function and Left Ventricular Mass and Remodeling

Author

Redheuil, Alban, Yu, Wen-Chung, Mousseaux, Elie, Harouni, Ahmed A., Kachenoura, Nadjia, Wu, Colin O., Bluemke, David, and Lima, Joao A.C.

Subjects
aortic geometry, left ventricular remodeling, aging, cardiovascular system, magnetic resonance imaging, elasticity
Abstract

ObjectivesWe sought to define age-related geometric changes of the aortic arch and determine their relationship to central aortic stiffness and left ventricular (LV) remodeling.BackgroundThe proximal aorta has been shown to thicken, enlarge in diameter, and lengthen with aging in humans. However, no systematic study has described age-related longitudinal and transversal remodeling of the aortic arch and their relationship with LV mass and remodeling.MethodsWe studied 100 subjects (55 women, 45 men, average age 46±16 years) free of overt cardiovascular disease using magnetic resonance imaging to determine aortic arch geometry (length, diameters, height, width, and curvature), aortic arch function (local aortic distensibility and arch pulse wave velocity [PWV]), and LV volumes and mass. Radial tonometry was used to calculate central blood pressure.ResultsAortic diameters and arch length increased significantly with age. The ascending aorta length increased most, with age leading to aortic arch widening and decreased curvature. These geometric changes of the aortic arch were significantly related to decreased ascending aortic distensibility, increased aortic arch PWV (p < 0.001), and increased central blood pressures (p < 0.001). Increased ascending aortic diameter, lengthening, and decreased curvature of the aortic arch (unfolding) were all significantly associated with increased LV mass and concentric remodeling independently of age, sex, body size, and central blood pressure (p < 0.01).ConclusionsAge-related unfolding of the aortic arch is related to increased proximal aortic stiffness in individuals without cardiovascular disease and associated with increased LV mass and mass-to-volume ratio independent of age, body size, central pressure, and cardiovascular risk factors.

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43. Rapid Artificial Intelligence Solutions in a Pandemic - The COVID-19-20 Lung CT Lesion Segmentation Challenge.

Author

Roth HR, Xu Z, Diez CT, Jacob RS, Zember J, Molto J, Li W, Xu S, Turkbey B, Turkbey E, Yang D, Harouni A, Rieke N, Hu S, Isensee F, Tang C, Yu Q, Sölter J, Zheng T, Liauchuk V, Zhou Z, Moltz JH, Oliveira B, Xia Y, Maier-Hein KH, Li Q, Husch A, Zhang L, Kovalev V, Kang L, Hering A, Vilaça JL, Flores M, Xu D, Wood B, and Linguraru MG

Abstract

Artificial intelligence (AI) methods for the automatic detection and quantification of COVID-19 lesions in chest computed tomography (CT) might play an important role in the monitoring and management of the disease. We organized an international challenge and competition for the development and comparison of AI algorithms for this task, which we supported with public data and state-of-the-art benchmark methods. Board Certified Radiologists annotated 295 public images from two sources (A and B) for algorithms training (n=199, source A), validation (n=50, source A) and testing (n=23, source A; n=23, source B). There were 1,096 registered teams of which 225 and 98 completed the validation and testing phases, respectively. The challenge showed that AI models could be rapidly designed by diverse teams with the potential to measure disease or facilitate timely and patient-specific interventions. This paper provides an overview and the major outcomes of the COVID-19 Lung CT Lesion Segmentation Challenge - 2020.

Published
2021
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44. Federated Learning used for predicting outcomes in SARS-COV-2 patients.

Author

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.

Published
2021
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