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1. Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study

Author

Hirten, Robert P, Danieletto, Matteo, Tomalin, Lewis, Choi, Katie Hyewon, Zweig, Micol, Golden, Eddye, Kaur, Sparshdeep, Helmus, Drew, Biello, Anthony, Pyzik, Renata, Charney, Alexander, Miotto, Riccardo, Glicksberg, Benjamin S, Levin, Matthew, Nabeel, Ismail, Aberg, Judith, Reich, David, Charney, Dennis, Bottinger, Erwin P, Keefer, Laurie, Suarez-Farinas, Mayte, Nadkarni, Girish N, and Fayad, Zahi A

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundChanges in autonomic nervous system function, characterized by heart rate variability (HRV), have been associated with infection and observed prior to its clinical identification. ObjectiveWe performed an evaluation of HRV collected by a wearable device to identify and predict COVID-19 and its related symptoms. MethodsHealth care workers in the Mount Sinai Health System were prospectively followed in an ongoing observational study using the custom Warrior Watch Study app, which was downloaded to their smartphones. Participants wore an Apple Watch for the duration of the study, measuring HRV throughout the follow-up period. Surveys assessing infection and symptom-related questions were obtained daily. ResultsUsing a mixed-effect cosinor model, the mean amplitude of the circadian pattern of the standard deviation of the interbeat interval of normal sinus beats (SDNN), an HRV metric, differed between subjects with and without COVID-19 (P=.006). The mean amplitude of this circadian pattern differed between individuals during the 7 days before and the 7 days after a COVID-19 diagnosis compared to this metric during uninfected time periods (P=.01). Significant changes in the mean and amplitude of the circadian pattern of the SDNN was observed between the first day of reporting a COVID-19–related symptom compared to all other symptom-free days (P=.01). ConclusionsLongitudinally collected HRV metrics from a commonly worn commercial wearable device (Apple Watch) can predict the diagnosis of COVID-19 and identify COVID-19–related symptoms. Prior to the diagnosis of COVID-19 by nasal swab polymerase chain reaction testing, significant changes in HRV were observed, demonstrating the predictive ability of this metric to identify COVID-19 infection.

Published
2021
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2. Polymersomes with splenic avidity target red pulp myeloid cells for cancer immunotherapy

Author

Wauters, Annelies C., Scheerstra, Jari F., van Leent, Mandy M. T., Teunissen, Abraham J. P., Priem, Bram, Beldman, Thijs J., Rother, Nils, Duivenvoorden, Raphaël, Prévot, Geoffrey, Munitz, Jazz, Toner, Yohana C., Deckers, Jeroen, van Elsas, Yuri, Mora-Raimundo, Patricia, Chen, Gal, Nauta, Sheqouia A., Verschuur, Anna Vera D., Griffioen, Arjan W., Schrijver, David P., Anbergen, Tom, Li, Yudong, Wu, Hanglong, Mason, Alexander F., van Stevendaal, Marleen H. M. E., Kluza, Ewelina, Post, Richard A. J., Joosten, Leo A. B., Netea, Mihai G., Calcagno, Claudia, Fayad, Zahi A., van der Meel, Roy, Schroeder, Avi, Abdelmohsen, Loai K. E. A., Mulder, Willem J. M., and van Hest, Jan C. M.

Published
2024
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3. Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach

Author

Vaid, Akhil, Jaladanki, Suraj K, Xu, Jie, Teng, Shelly, Kumar, Arvind, Lee, Samuel, Somani, Sulaiman, Paranjpe, Ishan, De Freitas, Jessica K, Wanyan, Tingyi, Johnson, Kipp W, Bicak, Mesude, Klang, Eyal, Kwon, Young Joon, Costa, Anthony, Zhao, Shan, Miotto, Riccardo, Charney, Alexander W, Böttinger, Erwin, Fayad, Zahi A, Nadkarni, Girish N, Wang, Fei, and Glicksberg, Benjamin S

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

BackgroundMachine learning models require large datasets that may be siloed across different health care institutions. Machine learning studies that focus on COVID-19 have been limited to single-hospital data, which limits model generalizability. ObjectiveWe aimed to use federated learning, a machine learning technique that avoids locally aggregating raw clinical data across multiple institutions, to predict mortality in hospitalized patients with COVID-19 within 7 days. MethodsPatient data were collected from the electronic health records of 5 hospitals within the Mount Sinai Health System. Logistic regression with L1 regularization/least absolute shrinkage and selection operator (LASSO) and multilayer perceptron (MLP) models were trained by using local data at each site. We developed a pooled model with combined data from all 5 sites, and a federated model that only shared parameters with a central aggregator. ResultsThe LASSOfederated model outperformed the LASSOlocal model at 3 hospitals, and the MLPfederated model performed better than the MLPlocal model at all 5 hospitals, as determined by the area under the receiver operating characteristic curve. The LASSOpooled model outperformed the LASSOfederated model at all hospitals, and the MLPfederated model outperformed the MLPpooled model at 2 hospitals. ConclusionsThe federated learning of COVID-19 electronic health record data shows promise in developing robust predictive models without compromising patient privacy.

Published
2021
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4. Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation

Author

Vaid, Akhil, Somani, Sulaiman, Russak, Adam J, De Freitas, Jessica K, Chaudhry, Fayzan F, Paranjpe, Ishan, Johnson, Kipp W, Lee, Samuel J, Miotto, Riccardo, Richter, Felix, Zhao, Shan, Beckmann, Noam D, Naik, Nidhi, Kia, Arash, Timsina, Prem, Lala, Anuradha, Paranjpe, Manish, Golden, Eddye, Danieletto, Matteo, Singh, Manbir, Meyer, Dara, O'Reilly, Paul F, Huckins, Laura, Kovatch, Patricia, Finkelstein, Joseph, Freeman, Robert M., Argulian, Edgar, Kasarskis, Andrew, Percha, Bethany, Aberg, Judith A, Bagiella, Emilia, Horowitz, Carol R, Murphy, Barbara, Nestler, Eric J, Schadt, Eric E, Cho, Judy H, Cordon-Cardo, Carlos, Fuster, Valentin, Charney, Dennis S, Reich, David L, Bottinger, Erwin P, Levin, Matthew A, Narula, Jagat, Fayad, Zahi A, Just, Allan C, Charney, Alexander W, Nadkarni, Girish N, and Glicksberg, Benjamin S

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundCOVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking. ObjectiveThe aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points. MethodsWe used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19–positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions. ResultsUpon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction. ConclusionsWe externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.

Published
2020
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5. Artificial intelligence applied to coronary artery calcium scans (AI-CAC) significantly improves cardiovascular events prediction

Author

Naghavi, Morteza, Reeves, Anthony P., Atlas, Kyle, Zhang, Chenyu, Atlas, Thomas, Henschke, Claudia I., Yankelevitz, David F., Budoff, Matthew J., Li, Dong, Roy, Sion K., Nasir, Khurram, Molloi, Sabee, Fayad, Zahi, McConnell, Michael V., Kakadiaris, Ioannis, Maron, David J., Narula, Jagat, Williams, Kim, Shah, Prediman K., Levy, Daniel, and Wong, Nathan D.

Published
2024
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6. Macrophage PET imaging in mouse models of cardiovascular disease and cancer with an apolipoprotein-inspired radiotracer

Author

Toner, Yohana C., Prévot, Geoffrey, van Leent, Mandy M. T., Munitz, Jazz, Oosterwijk, Roderick, Verschuur, Anna Vera D., van Elsas, Yuri, Peric, Vedran, Maas, Rianne J. F., Ranzenigo, Anna, Morla-Folch, Judit, Wang, William, Umali, Martin, de Dreu, Anne, Fernandes, Jessica Chimene, Sullivan, Nathaniel A. T., Maier, Alexander, Mason, Christian, Reiner, Thomas, Fayad, Zahi A., Mulder, Willem J. M., Teunissen, Abraham J. P., and Pérez-Medina, Carlos

Published
2024
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7. Trained immunity suppression determines kidney allograft survival

Author

Jonkman, Inge, Jacobs, Maaike M.E., Negishi, Yutaka, Yanginlar, Cansu, Martens, Joost H.A., Baltissen, Marijke, Vermeulen, Michiel, van den Hoogen, Martijn W.F., Baas, Marije, van der Vlag, Johan, Fayad, Zahi A., Teunissen, Abraham J.P., Madsen, Joren C., Ochando, Jordi, Joosten, Leo A.B., Netea, Mihai G., Mulder, Willem J.M., Mhlanga, Musa M., Hilbrands, Luuk B., Rother, Nils, and Duivenvoorden, Raphaël

Published
2024
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8. Resolving sepsis-induced immunoparalysis via trained immunity by targeting interleukin-4 to myeloid cells

Author

Schrijver, David P., Röring, Rutger J., Deckers, Jeroen, de Dreu, Anne, Toner, Yohana C., Prevot, Geoffrey, Priem, Bram, Munitz, Jazz, Nugraha, Eveline G., van Elsas, Yuri, Azzun, Anthony, Anbergen, Tom, Groh, Laszlo A., Becker, Anouk M. D., Pérez-Medina, Carlos, Oosterwijk, Roderick S., Novakovic, Boris, Moorlag, Simone J. C. F. M., Jansen, Aron, Pickkers, Peter, Kox, Matthijs, Beldman, Thijs J., Kluza, Ewelina, van Leent, Mandy M. T., Teunissen, Abraham J. P., van der Meel, Roy, Fayad, Zahi A., Joosten, Leo A. B., Fisher, Edward A., Merkx, Maarten, Netea, Mihai G., and Mulder, Willem J. M.

Published
2023
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10. Hybrid Magnetic Resonance Positron Emission Tomography Is Associated With Cardiac-Related Outcomes in Cardiac Sarcoidosis

Author

Trivieri, Maria Giovanna, Robson, Philip M., Vergani, Vittoria, LaRocca, Gina, Romero-Daza, Angelica M., Abgral, Ronan, Devesa, Ana, Azoulay, Levi-Dan, Karakatsanis, Nicolas A., Parikh, Aditya, Panagiota, Christia, Palmisano, Anna, DePalo, Louis, Chang, Helena L., Rothstein, Joseph H., Fayad, Rima A., Miller, Marc A., Fuster, Valentin, Narula, Jagat, Dweck, Marc R., Morgenthau, Adam, Jacobi, Adam, Padilla, Maria, Kovacic, Jason C., and Fayad, Zahi A.

Published
2024
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11. 18F-FDG PET/CT in left ventricular assist device infections: In-depth characterization and clinical implications

Author

Devesa, Ana, Rashed, Eman, Moss, Noah, Robson, Philip M., Pyzik, Renata, Roldan, Julie, Taimur, Sarah, Rana, Meenakshi M., Ashley, Kimberly, Young, Anna, Patel, Gopi, Mahmood, Kiran, Mitter, Sumeet S., Lala, Anuradha, Barghash, Maya, Fox, Arieh, Correa, Ashish, Pirlamarla, Preethi, Contreras, Johanna, Parikh, Aditya, Mancini, Donna, Jacobi, Adam, Ghesani, Nasrin, Gavane, Somali C., Ghesani, Munir, Itagaki, Shinobu, Anyanwu, Anelechi, Fayad, Zahi A., and Trivieri, Maria Giovanna

Published
2024
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13. Systems immunology-based drug repurposing framework to target inflammation in atherosclerosis

Author

Amadori, Letizia, Calcagno, Claudia, Fernandez, Dawn M., Koplev, Simon, Fernandez, Nicolas, Kaur, Ravneet, Mury, Pauline, Khan, Nayaab S, Sajja, Swathy, Shamailova, Roza, Cyr, Yannick, Jeon, Minji, Hill, Christopher A., Chong, Peik Sean, Naidu, Sonum, Sakurai, Ken, Ghotbi, Adam Ali, Soler, Raphael, Eberhardt, Natalia, Rahman, Adeeb, Faries, Peter, Moore, Kathryn J., Fayad, Zahi A., Ma’ayan, Avi, and Giannarelli, Chiara

Published
2023
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16. Acid ceramidase regulates innate immune memory

Author

Rother, Nils, Yanginlar, Cansu, Prévot, Geoffrey, Jonkman, Inge, Jacobs, Maaike, van Leent, Mandy M.T., van Heck, Julia, Matzaraki, Vasiliki, Azzun, Anthony, Morla-Folch, Judit, Ranzenigo, Anna, Wang, William, van der Meel, Roy, Fayad, Zahi A., Riksen, Niels P., Hilbrands, Luuk B., Lindeboom, Rik G.H., Martens, Joost H.A., Vermeulen, Michiel, Joosten, Leo A.B., Netea, Mihai G., Mulder, Willem J.M., van der Vlag, Johan, Teunissen, Abraham J.P., and Duivenvoorden, Raphaël

Published
2023
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19. Inhibition of thrombin-activatable fibrinolysis inhibitor via DS-1040 to accelerate clot lysis in patients with acute pulmonary embolism: a randomized phase 1b study

Author

Huisman, Menno, Tapson, Victor, Grosso, Michael, Segers, Annelise, Ageno, Walter, Brodmann, Marianne, Jimenez, David, Meyer, Guy, Middeldorp, Saskia, Rosovsky, Rachel, Schellong, Sebastian, Verhamme, Peter, Büller, Harry, Bossuyt, Patrick, van Es, Nick, Vanassche, Thomas, Rosovsky, Rachel P., Moustafa, Fares, Büller, Harry R., Patel, Indu, Shi, Minggao, Miyoshi, Naoki, Mani, Venkatesh, Fayad, Zahi, Stephan, Dominique, Schmidt, Jeannot, Grosso, Michael A., Tapson, Victor F., and Huisman, Menno V.

Published
2023
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21. Multiparametric Immunoimaging Maps Inflammatory Signatures in Murine Myocardial Infarction Models

Author

Maier, Alexander, Toner, Yohana C., Munitz, Jazz, Sullivan, Nathaniel A.T., Sakurai, Ken, Meerwaldt, Anu E., Brechbühl, Eliane E.S., Prévot, Geoffrey, van Elsas, Yuri, Maas, Rianne J.F., Ranzenigo, Anna, Soultanidis, Georgios, Rashidian, Mohammad, Pérez-Medina, Carlos, Heo, Gyu Seong, Gropler, Robert J., Liu, Yongjian, Reiner, Thomas, Nahrendorf, Matthias, Swirski, Filip K., Strijkers, Gustav J., Teunissen, Abraham J.P., Calcagno, Claudia, Fayad, Zahi A., Mulder, Willem J.M., and van Leent, Mandy M.T.

Published
2023
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23. Somatostatin Receptor PET/MR Imaging of Inflammation in Patients With Large Vessel Vasculitis and Atherosclerosis

Author

Ćorović, Andrej, Wall, Christopher, Nus, Meritxell, Gopalan, Deepa, Huang, Yuan, Imaz, Maria, Zulcinski, Michal, Peverelli, Marta, Uryga, Anna, Lambert, Jordi, Bressan, Dario, Maughan, Robert T., Pericleous, Charis, Dubash, Suraiya, Jordan, Natasha, Jayne, David R., Hoole, Stephen P., Calvert, Patrick A., Dean, Andrew F., Rassl, Doris, Barwick, Tara, Iles, Mark, Frontini, Mattia, Hannon, Greg, Manavaki, Roido, Fryer, Tim D., Aloj, Luigi, Graves, Martin J., Gilbert, Fiona J., Dweck, Marc R., Newby, David E., Fayad, Zahi A., Reynolds, Gary, Morgan, Ann W., Aboagye, Eric O., Davenport, Anthony P., Jørgensen, Helle F., Mallat, Ziad, Bennett, Martin R., Peters, James E., Rudd, James H.F., Mason, Justin C., and Tarkin, Jason M.

Published
2023
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27. Clonally expanded CD8 T cells characterize amyotrophic lateral sclerosis-4

Author

Campisi, Laura, Chizari, Shahab, Ho, Jessica S. Y., Gromova, Anastasia, Arnold, Frederick J., Mosca, Lorena, Mei, Xueyan, Fstkchyan, Yesai, Torre, Denis, Beharry, Cindy, Garcia-Forn, Marta, Jiménez-Alcázar, Miguel, Korobeynikov, Vladislav A., Prazich, Jack, Fayad, Zahi A., Seldin, Marcus M., De Rubeis, Silvia, Bennett, Craig L., Ostrow, Lyle W., Lunetta, Christian, Squatrito, Massimo, Byun, Minji, Shneider, Neil A., Jiang, Ning, La Spada, Albert R., and Marazzi, Ivan

Published
2022
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30. Systematically evaluating DOTATATE and FDG as PET immuno-imaging tracers of cardiovascular inflammation

Author

Toner, Yohana C., Ghotbi, Adam A., Naidu, Sonum, Sakurai, Ken, van Leent, Mandy M. T., Jordan, Stefan, Ordikhani, Farideh, Amadori, Letizia, Sofias, Alexandros Marios, Fisher, Elizabeth L., Maier, Alexander, Sullivan, Nathaniel, Munitz, Jazz, Senders, Max L., Mason, Christian, Reiner, Thomas, Soultanidis, Georgios, Tarkin, Jason M., Rudd, James H. F., Giannarelli, Chiara, Ochando, Jordi, Pérez-Medina, Carlos, Kjaer, Andreas, Mulder, Willem J. M., Fayad, Zahi A., and Calcagno, Claudia

Published
2022
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37. GAMER MRI: Gated-attention mechanism ranking of multi-contrast MRI in brain pathology

Author

Lu, Po-Jui, Yoo, Youngjin, Rahmanzadeh, Reza, Galbusera, Riccardo, Weigel, Matthias, Ceccaldi, Pascal, Nguyen, Thanh D., Spincemaille, Pascal, Wang, Yi, Daducci, Alessandro, La Rosa, Francesco, Bach Cuadra, Meritxell, Sandkühler, Robin, Nael, Kambiz, Doshi, Amish, Fayad, Zahi A., Kuhle, Jens, Kappos, Ludwig, Odry, Benjamin, Cattin, Philippe, Gibson, Eli, and Granziera, Cristina

Published
2021
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38. An assessment of PET and CMR radiomic features for the detection of cardiac sarcoidosis.

Author

Mushari, Nouf A., Soultanidis, Georgios, Duff, Lisa, Trivieri, Maria G., Fayad, Zahi A., Robson, Philip, and Tsoumpas, Charalampos

Subjects
LEFT heart ventricle, RANDOM forest algorithms, POISSON distribution, STATISTICAL correlation, CARDIOMYOPATHIES, RADIOPHARMACEUTICALS, DIAGNOSTIC imaging, RESEARCH funding, RECEIVER operating characteristic curves, DATA analysis, RADIOMICS, DEOXY sugars, LOGISTIC regression analysis, SARCOIDOSIS, POSITRON emission tomography, MAGNETIC resonance imaging, DESCRIPTIVE statistics, SEVERITY of illness index, RETROSPECTIVE studies, MANN Whitney U Test, SUPPORT vector machines, MEDICAL records, ACQUISITION of data, STATISTICS, RESEARCH, INFLAMMATION, MACHINE learning, AUTOMATION, DATA analysis software, CONFIDENCE intervals, COVID-19, SENSITIVITY & specificity (Statistics)
Abstract

Background: Visual interpretation of PET and CMR may fail to identify cardiac sarcoidosis (CS) with high specificity. This study aimed to evaluate the role of [18F]FDG PET and late gadolinium enhancement (LGE)-CMR radiomic features in differentiating CS from another cause of myocardial inflammation, in this case patients with cardiac-related clinical symptoms following COVID-19. Methods: [18F]FDG PET and LGE-CMR were treated separately in this work. There were 35 post-COVID-19 (PC) and 40 CS datasets. Regions of interest were delineated manually around the entire left ventricle for the PET and LGE-CMR datasets. Radiomic features were then extracted. The ability of individual features to correctly identify image data as CS or PC was tested to predict the clinical classification of CS vs. PC using Mann--Whitney U-tests and logistic regression. Features were retained if the P-value was <0.00053, the AUC was >0.5, and the accuracy was >0.7. After applying the correlation test, uncorrelated features were used as a signature ( joint features) to train machine learning classifiers. For LGE-CMR analysis, to further improve the results, different classifiers were used for individual features besides logistic regression, and the results of individual features of each classifier were screened to create a signature that included all features that followed the previously mentioned criteria and used it them as input for machine learning classifiers. Results: The Mann--Whitney U-tests and logistic regression were trained on individual features to build a collection of features. For [18F]FDG PET analysis, the maximum target-to-background ratio (TBRmax) showed a high area under the curve (AUC) and accuracy with small P-values (<0.00053), but the signature performed better (AUC 0.98 and accuracy 0.91). For LGE-CMR analysis, the Gray Level Dependence Matrix (gldm)-Dependence Non-Uniformity showed good results with small error bars (accuracy 0.75 and AUC 0.87). However, by applying a Support Vector Machine classifier to individual LGE-CMR features and creating a signature, a Random Forest classifier displayed better AUC and accuracy (0.91 and 0.84, respectively). Conclusion: Using radiomic features may prove useful in identifying individuals with CS. Some features showed promising results in differentiating between PC and CS. By automating the analysis, the patient management process can be accelerated and improved. [ABSTRACT FROM AUTHOR]

Published
2024
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39. A review of self‐supervised, generative, and few‐shot deep learning methods for data‐limited magnetic resonance imaging segmentation.

Author

Liu, Zelong, Kainth, Komal, Zhou, Alexander, Deyer, Timothy W., Fayad, Zahi A., Greenspan, Hayit, and Mei, Xueyan

Subjects
MAGNETIC resonance imaging, DEEP learning, IMAGE segmentation, SUPERVISED learning, DIAGNOSTIC imaging
Abstract

Magnetic resonance imaging (MRI) is a ubiquitous medical imaging technology with applications in disease diagnostics, intervention, and treatment planning. Accurate MRI segmentation is critical for diagnosing abnormalities, monitoring diseases, and deciding on a course of treatment. With the advent of advanced deep learning frameworks, fully automated and accurate MRI segmentation is advancing. Traditional supervised deep learning techniques have advanced tremendously, reaching clinical‐level accuracy in the field of segmentation. However, these algorithms still require a large amount of annotated data, which is oftentimes unavailable or impractical. One way to circumvent this issue is to utilize algorithms that exploit a limited amount of labeled data. This paper aims to review such state‐of‐the‐art algorithms that use a limited number of annotated samples. We explain the fundamental principles of self‐supervised learning, generative models, few‐shot learning, and semi‐supervised learning and summarize their applications in cardiac, abdomen, and brain MRI segmentation. Throughout this review, we highlight algorithms that can be employed based on the quantity of annotated data available. We also present a comprehensive list of notable publicly available MRI segmentation datasets. To conclude, we discuss possible future directions of the field—including emerging algorithms, such as contrastive language‐image pretraining, and potential combinations across the methods discussed—that can further increase the efficacy of image segmentation with limited labels. [ABSTRACT FROM AUTHOR]

Published
2024
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40. Hydroxychloroquine Inhibits the Trained Innate Immune Response to Interferons

Author

Rother, Nils, Yanginlar, Cansu, Lindeboom, Rik G.H., Bekkering, Siroon, van Leent, Mandy M.T., Buijsers, Baranca, Jonkman, Inge, de Graaf, Mark, Baltissen, Marijke, Lamers, Lieke A., Riksen, Niels P., Fayad, Zahi A., Mulder, Willem J.M., Hilbrands, Luuk B., Joosten, Leo A.B., Netea, Mihai G., Vermeulen, Michiel, van der Vlag, Johan, and Duivenvoorden, Raphaël

Published
2020
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42. Immune Checkpoint Inhibitor Therapy Aggravates T Cell–Driven Plaque Inflammation in Atherosclerosis

Author

Poels, Kikkie, van Leent, Mandy M.T., Boutros, Celine, Tissot, Hubert, Roy, Séverine, Meerwaldt, Anu E., Toner, Yohana C.A., Reiche, Myrthe E., Kusters, Pascal J.H., Malinova, Tsveta, Huveneers, Stephan, Kaufman, Audrey E., Mani, Venkatesh, Fayad, Zahi A., de Winther, Menno P.J., Marabelle, Aurelien, Mulder, Willem J.M., Robert, Caroline, Seijkens, Tom T.P., and Lutgens, Esther

Published
2020
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44. Trained Immunity-Promoting Nanobiologic Therapy Suppresses Tumor Growth and Potentiates Checkpoint Inhibition

Author

Priem, Bram, van Leent, Mandy M.T., Teunissen, Abraham J.P., Sofias, Alexandros Marios, Mourits, Vera P., Willemsen, Lisa, Klein, Emma D., Oosterwijk, Roderick S., Meerwaldt, Anu E., Munitz, Jazz, Prévot, Geoffrey, Vera Verschuur, Anna, Nauta, Sheqouia A., van Leeuwen, Esther M., Fisher, Elizabeth L., de Jong, Karen A.M., Zhao, Yiming, Toner, Yohana C., Soultanidis, Georgios, Calcagno, Claudia, Bomans, Paul H.H., Friedrich, Heiner, Sommerdijk, Nico, Reiner, Thomas, Duivenvoorden, Raphaël, Zupančič, Eva, Di Martino, Julie S., Kluza, Ewelina, Rashidian, Mohammad, Ploegh, Hidde L., Dijkhuizen, Rick M., Hak, Sjoerd, Pérez-Medina, Carlos, Bravo-Cordero, Jose Javier, de Winther, Menno P.J., Joosten, Leo A.B., van Elsas, Andrea, Fayad, Zahi A., Rialdi, Alexander, Torre, Denis, Guccione, Ernesto, Ochando, Jordi, Netea, Mihai G., Griffioen, Arjan W., and Mulder, Willem J.M.

Published
2020
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48. Artificial intelligence–enabled rapid diagnosis of patients with COVID-19

Author

Mei, Xueyan, Lee, Hao-Chih, Diao, Kai-yue, Huang, Mingqian, Lin, Bin, Liu, Chenyu, Xie, Zongyu, Ma, Yixuan, Robson, Philip M., Chung, Michael, Bernheim, Adam, Mani, Venkatesh, Calcagno, Claudia, Li, Kunwei, Li, Shaolin, Shan, Hong, Lv, Jian, Zhao, Tongtong, Xia, Junli, Long, Qihua, Steinberger, Sharon, Jacobi, Adam, Deyer, Timothy, Luksza, Marta, Liu, Fang, Little, Brent P., Fayad, Zahi A., and Yang, Yang

Published
2020
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49. Probing myeloid cell dynamics in ischaemic heart disease by nanotracer hot-spot imaging

Author

Senders, Max L., Meerwaldt, Anu E., van Leent, Mandy M. T., Sanchez-Gaytan, Brenda L., van de Voort, Jan C., Toner, Yohana C., Maier, Alexander, Klein, Emma D., Sullivan, Nathaniel A. T., Sofias, Alexandros Marios, Groenen, Hannah, Faries, Christopher, Oosterwijk, Roderick S., van Leeuwen, Esther M., Fay, Francois, Chepurko, Elena, Reiner, Thomas, Duivenvoorden, Raphael, Zangi, Lior, Dijkhuizen, Rick M., Hak, Sjoerd, Swirski, Filip K., Nahrendorf, Matthias, Pérez-Medina, Carlos, Teunissen, Abraham J. P., Fayad, Zahi A., Calcagno, Claudia, Strijkers, Gustav J., and Mulder, Willem J. M.

Published
2020
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