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2,346 results on '"Fayad, Zahi"'

52. Publisher Correction: 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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53. PET/MR Imaging of Malondialdehyde-Acetaldehyde Epitopes With a Human Antibody Detects Clinically Relevant Atherothrombosis.

Author

Senders, Max L, Que, Xuchu, Cho, Young Seok, Yeang, Calvin, Groenen, Hannah, Fay, Francois, Calcagno, Claudia, Meerwaldt, Anu E, Green, Simone, Miu, Phuong, Lobatto, Mark E, Reiner, Thomas, Fayad, Zahi A, Witztum, Joseph L, Mulder, Willem JM, Pérez-Medina, Carlos, and Tsimikas, Sotirios

Subjects
Animals, Mice, Knockout, Rabbits, Humans, Mice, Thrombosis, Acetaldehyde, Malondialdehyde, Epitopes, Positron-Emission Tomography, Magnetic Resonance Imaging, Tissue Distribution, Atherosclerosis, PET/MR imaging, atherosclerosis, natural antibodies, oxidation-specific epitopes, Biomedical Imaging, Bioengineering, Cardiovascular, PET/MR imaging, Cardiorespiratory Medicine and Haematology, Public Health and Health Services, Cardiovascular System & Hematology
Abstract

BackgroundOxidation-specific epitopes (OSEs) are proinflammatory, and elevated levels in plasma predict cardiovascular events.ObjectivesThe purpose of this study was to develop novel positron emission tomography (PET) probes to noninvasively image OSE-rich lesions.MethodsAn antigen-binding fragment (Fab) antibody library was constructed from human fetal cord blood. After multiple rounds of screening against malondialdehyde-acetaldehyde (MAA) epitopes, the Fab LA25 containing minimal nontemplated insertions in the CDR3 region was identified and characterized. In mice, pharmacokinetics, biodistribution, and plaque specificity studies were performed with Zirconium-89 (89Zr)-labeled LA25. In rabbits, 89Zr-LA25 was used in combination with an integrated clinical PET/magnetic resonance (MR) system. 18F-fluorodeoxyglucose PET and dynamic contrast-enhanced MR imaging were used to evaluate vessel wall inflammation and plaque neovascularization, respectively. Extensive ex vivo validation was carried out through a combination of gamma counting, near infrared fluorescence, autoradiography, immunohistochemistry, and immunofluorescence.ResultsLA25 bound specifically to MAA epitopes in advanced and ruptured human atherosclerotic plaques with accompanying thrombi and in debris from distal protection devices. PET/MR imaging 24 h after injection of 89Zr-LA25 showed increased uptake in the abdominal aorta of atherosclerotic rabbits compared with nonatherosclerotic control rabbits, confirmed by ex vivo gamma counting and autoradiography. 18F-fluorodeoxyglucose PET, dynamic contrast-enhanced MR imaging, and near-infrared fluorescence signals were also significantly higher in atherosclerotic rabbit aortas compared with control aortas. Enhanced liver uptake was also noted in atherosclerotic animals, confirmed by the presence of MAA epitopes by immunostaining.Conclusions89Zr-LA25 is a novel PET radiotracer that may allow noninvasive phenotyping of high-risk OSE-rich lesions.

Published
2018

56. 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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57. 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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59. 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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61. 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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66. Remote Short Sessions of Heart Rate Variability Biofeedback Monitored With Wearable Technology: Open-Label Prospective Feasibility Study

Author

Hirten, Robert P, primary, Danieletto, Matteo, additional, Landell, Kyle, additional, Zweig, Micol, additional, Golden, Eddye, additional, Pyzik, Renata, additional, Kaur, Sparshdeep, additional, Chang, Helena, additional, Helmus, Drew, additional, Sands, Bruce E, additional, Charney, Dennis, additional, Nadkarni, Girish, additional, Bagiella, Emilia, additional, Keefer, Laurie, additional, and Fayad, Zahi A, additional

Published
2024
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69. Effect of Stress-Related Neural Pathways on the Cardiovascular Benefit of Physical Activity

Author

Zureigat, Hadil, primary, Osborne, Michael T., additional, Abohashem, Shady, additional, Mezue, Kenechukwu, additional, Gharios, Charbel, additional, Grewal, Simran, additional, Cardeiro, Alex, additional, Naddaf, Nicki, additional, Civieri, Giovanni, additional, Abbasi, Taimur, additional, Radfar, Azar, additional, Aldosoky, Wesam, additional, Seligowski, Antonia V., additional, Wasfy, Meagan M., additional, Guseh, James Sawalla, additional, Churchill, Timothy W., additional, Rosovsky, Rachel P., additional, Fayad, Zahi, additional, Rosenzweig, Anthony, additional, Baggish, Aaron, additional, Pitman, Roger K., additional, Choi, Karmel W., additional, Smoller, Jordan, additional, Shin, Lisa M., additional, and Tawakol, Ahmed, additional

Published
2024
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71. Trained immunity, tolerance, priming and differentiation: distinct immunological processes

Author

Divangahi, Maziar, Aaby, Peter, Khader, Shabaana Abdul, Barreiro, Luis B., Bekkering, Siroon, Chavakis, Triantafyllos, van Crevel, Reinout, Curtis, Nigel, DiNardo, Andrew R., Dominguez-Andres, Jorge, Duivenvoorden, Raphael, Fanucchi, Stephanie, Fayad, Zahi, Fuchs, Elaine, Hamon, Melanie, Jeffrey, Kate L., Khan, Nargis, Joosten, Leo A. B., Kaufmann, Eva, Latz, Eicke, Matarese, Giuseppe, van der Meer, Jos W. M., Mhlanga, Musa, Moorlag, Simone J. C. F. M., Mulder, Willem J. M., Naik, Shruti, Novakovic, Boris, O’Neill, Luke, Ochando, Jordi, Ozato, Keiko, Riksen, Niels P., Sauerwein, Robert, Sherwood, Edward R., Schlitzer, Andreas, Schultze, Joachim L., Sieweke, Michael H., Benn, Christine Stabell, Stunnenberg, Henk, Sun, Joseph, van de Veerdonk, Frank L., Weis, Sebastian, Williams, David L., Xavier, Ramnik, and Netea, Mihai G.

Published
2021
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72. VISION-MAE: A Foundation Model for Medical Image Segmentation and Classification

Author

Liu, Zelong, Tieu, Andrew, Patel, Nikhil, Zhou, Alexander, Soultanidis, George, Fayad, Zahi A., Deyer, Timothy, Mei, Xueyan, Liu, Zelong, Tieu, Andrew, Patel, Nikhil, Zhou, Alexander, Soultanidis, George, Fayad, Zahi A., Deyer, Timothy, and Mei, Xueyan

Abstract

Artificial Intelligence (AI) has the potential to revolutionize diagnosis and segmentation in medical imaging. However, development and clinical implementation face multiple challenges including limited data availability, lack of generalizability, and the necessity to incorporate multi-modal data effectively. A foundation model, which is a large-scale pre-trained AI model, offers a versatile base that can be adapted to a variety of specific tasks and contexts. Here, we present a novel foundation model, VISION-MAE, specifically designed for medical imaging. Specifically, VISION-MAE is trained on a dataset of 2.5 million unlabeled images from various modalities (CT, MR, PET, X-rays, and ultrasound), using self-supervised learning techniques. It is then adapted to classification and segmentation tasks using explicit labels. VISION-MAE has high label efficiency, outperforming several benchmark models in both in-domain and out-of-domain applications, and achieves high performance even with reduced availability of labeled data. This model represents a significant advancement in medical imaging AI, offering a generalizable and robust solution for improving segmentation and classification tasks while reducing the data annotation workload.

Published
2024

73. 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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74. 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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78. 18F-FDG PET/CT In Left Ventricular Assist Device Infections: In-depth Characterization And Clinical Implications

Author

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

Published
2024
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79. Effects of the high-density lipoprotein mimetic agent CER-001 on coronary atherosclerosis in patients with acute coronary syndromes: a randomized trial

Author

Tardif, Jean-Claude, Ballantyne, Christie M, Barter, Philip, Dasseux, Jean-Louis, Fayad, Zahi A, Guertin, Marie-Claude, Kastelein, John JP, Keyserling, Constance, Klepp, Heather, Koenig, Wolfgang, L'Allier, Philippe L, Lespérance, Jacques, Lüscher, Thomas F, Paolini, John F, Tawakol, Ahmed, Waters, David D, Pfeffer, M, Brown, V, Rouleau, J, Watkins, P, Wei, LJ, Gosselin, G, Chayer, C, Lanthier, S, Pelletier, GB, Racine, N, Agarwal, H, Brilakis, E, Cannon, L, Carrié, D, Corbelli, J, Coste, P, de Winter, R, Diaz, A, Eisenberg, S, Ennis, B, Fajadet, J, Fam, N, Fortuin, D, Gessler, C, Grines, C, Guerra, D, Gum, H, Haldis, T, Heestermans, T, Herrman, JP, Huynh, T, Kedhi, E, Koren, M, Kouz, S, Krolick, M, Kumkumian, G, Lavi, S, Li, RJ, Masud, ARZ, McAlhany, C, McGrew, FA, O'Shaughnessy, C, Ophuis, AJM Oude, Parr, K, Penny, W, Pesant, Y, Post, H, Robinson, S, Rodes-Cabau, J, Roy, A, Schulman, S, Spence, F, Stouffer, G, Stys, T, Sussex, B, Tahirkheli, N, Tardif, J-C, Grégoire, J, Berg, J ten, van Boven, AJ, von Birgelen, C, and Weinstein, D

Subjects
Aging, Clinical Trials and Supportive Activities, Atherosclerosis, Clinical Research, Cardiovascular, Heart Disease - Coronary Heart Disease, Heart Disease, 6.1 Pharmaceuticals, Evaluation of treatments and therapeutic interventions, Acute Coronary Syndrome, Adult, Aged, Aged, 80 and over, Apolipoprotein A-I, Cardiovascular Agents, Coronary Angiography, Coronary Artery Disease, Female, Humans, Infusions, Intravenous, Male, Middle Aged, Phospholipids, Prospective Studies, Recombinant Proteins, Treatment Outcome, Ultrasonography, Coronary disease, High-density lipoproteins, Clinical trial, Can HDL Infusions Significantly QUicken Atherosclerosis REgression (CHI-SQUARE) Investigators, Cardiorespiratory Medicine and Haematology, Clinical Sciences, Cardiovascular System & Hematology
Abstract

AimHigh-density lipoproteins (HDLs) have several potentially protective vascular effects. Most clinical studies of therapies targeting HDL have failed to show benefits vs. placebo.ObjectiveTo investigate the effects of an HDL-mimetic agent on atherosclerosis by intravascular ultrasonography (IVUS) and quantitative coronary angiography (QCA).Design and settingA prospective, double-blinded, randomized trial was conducted at 51 centres in the USA, the Netherlands, Canada, and France. Intravascular ultrasonography and QCA were performed to assess coronary atherosclerosis at baseline and 3 (2-5) weeks after the last study infusion.PatientsFive hundred and seven patients were randomized; 417 and 461 had paired IVUS and QCA measurements, respectively.InterventionPatients were randomized to receive 6 weekly infusions of placebo, 3 mg/kg, 6 mg/kg, or 12 mg/kg CER-001.Main outcome measuresThe primary efficacy parameter was the nominal change in the total atheroma volume. Nominal changes in per cent atheroma volume on IVUS and coronary scores on QCA were also pre-specified endpoints.ResultsThe nominal change in the total atheroma volume (adjusted means) was -2.71, -3.13, -1.50, and -3.05 mm(3) with placebo, CER-001 3 mg/kg, 6 mg/kg, and 12 mg/kg, respectively (primary analysis of 12 mg/kg vs. placebo: P = 0.81). There was also no difference among groups for the nominal change in per cent atheroma volume (0.02, -0.02, 0.01, and 0.19%; nominal P = 0.53 for 12 mg/kg vs. placebo). Change in the coronary artery score was -0.022, -0.036, -0.022, and -0.015 mm (nominal P = 0.25, 0.99, 0.55), and change in the cumulative coronary stenosis score was -0.51, 2.65, 0.71, and -0.77% (compared with placebo, nominal P = 0.85 for 12 mg/kg and nominal P = 0.01 for 3 mg/kg). The number of patients with major cardiovascular events was 10 (8.3%), 16 (13.3%), 17 (13.7%), and 12 (9.8%) in the four groups.ConclusionCER-001 infusions did not reduce coronary atherosclerosis on IVUS and QCA when compared with placebo. Whether CER-001 administered in other regimens or to other populations could favourably affect atherosclerosis must await further study. Name of the trial registry: Clinicaltrials.gov; Registry's URL: http://clinicaltrials.gov/ct2/show/NCT01201837?term=cer-001&rank=2;Trial registration numberNCT01201837.

Published
2014

80. 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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81. Development and Validation of a Deep Learning Classifier Using Chest Radiographs to Predict Extubation Success in Patients Undergoing Invasive Mechanical Ventilation.

Author

Tandon, Pranai, Nguyen, Kim-Anh-Nhi, Edalati, Masoud, Parchure, Prathamesh, Raut, Ganesh, Reich, David L., Freeman, Robert, Levin, Matthew A., Timsina, Prem, Powell, Charles A., Fayad, Zahi A., and Kia, Arash

Subjects
VENTILATION, DEEP learning, ARTIFICIAL respiration, CHEST X rays, EXTUBATION, CLINICAL decision support systems, DEEP brain stimulation
Abstract

The decision to extubate patients on invasive mechanical ventilation is critical; however, clinician performance in identifying patients to liberate from the ventilator is poor. Machine Learning-based predictors using tabular data have been developed; however, these fail to capture the wide spectrum of data available. Here, we develop and validate a deep learning-based model using routinely collected chest X-rays to predict the outcome of attempted extubation. We included 2288 serial patients admitted to the Medical ICU at an urban academic medical center, who underwent invasive mechanical ventilation, with at least one intubated CXR, and a documented extubation attempt. The last CXR before extubation for each patient was taken and split 79/21 for training/testing sets, then transfer learning with k-fold cross-validation was used on a pre-trained ResNet50 deep learning architecture. The top three models were ensembled to form a final classifier. The Grad-CAM technique was used to visualize image regions driving predictions. The model achieved an AUC of 0.66, AUPRC of 0.94, sensitivity of 0.62, and specificity of 0.60. The model performance was improved compared to the Rapid Shallow Breathing Index (AUC 0.61) and the only identified previous study in this domain (AUC 0.55), but significant room for improvement and experimentation remains. [ABSTRACT FROM AUTHOR]

Published
2024
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83. Remote Short Sessions of Heart Rate Variability Biofeedback Monitored With Wearable Technology: Open-Label Prospective Feasibility Study (Preprint)

Author

Hirten, Robert P, primary, Danieletto, Matteo, additional, Landell, Kyle, additional, Zweig, Micol, additional, Golden, Eddye, additional, Pyzik, Renata, additional, Kaur, Sparshdeep, additional, Chang, Helena, additional, Helmus, Drew, additional, Sands, Bruce E, additional, Charney, Dennis, additional, Nadkarni, Girish, additional, Bagiella, Emilia, additional, Keefer, Laurie, additional, and Fayad, Zahi A, additional

Published
2023
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89. Combined PET/DCE-MRI in a Rabbit Model of Atherosclerosis: Integrated Quantification of Plaque Inflammation, Permeability, and Burden During Treatment With a Leukotriene A4 Hydrolase Inhibitor

Author

Calcagno, Claudia, Lairez, Olivier, Hawkins, Julie, Kerr, Steven W., Dugas, Melanie S., Simpson, Thomas, Epskamp, Jelle, Robson, Philip M., Eldib, Mootaz, Bander, Ilda, K-Raman, Purushothaman, Ramachandran, Sarayu, Pruzan, Alison, Kaufman, Audrey, Mani, Venkatesh, Ehlgen, Alexander, Niessen, Heiko G., Broadwater, John, and Fayad, Zahi A.

Published
2018
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92. Development of the ehive Digital Health App: Protocol for a Centralized Research Platform

Author

Hirten, Robert P, primary, Danieletto, Matteo, additional, Landell, Kyle, additional, Zweig, Micol, additional, Golden, Eddye, additional, Orlov, Georgy, additional, Rodrigues, Jovita, additional, Alleva, Eugenia, additional, Ensari, Ipek, additional, Bottinger, Erwin, additional, Nadkarni, Girish N, additional, Fuchs, Thomas J, additional, and Fayad, Zahi A, additional

Published
2023
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93. A Hybrid Decision Tree and Deep Learning Approach Combining Medical Imaging and Electronic Medical Records to Predict Intubation Among Hospitalized Patients With COVID-19: Algorithm Development and Validation

Author

Nguyen, Kim-Anh-Nhi, primary, Tandon, Pranai, additional, Ghanavati, Sahar, additional, Cheetirala, Satya Narayana, additional, Timsina, Prem, additional, Freeman, Robert, additional, Reich, David, additional, Levin, Matthew A, additional, Mazumdar, Madhu, additional, Fayad, Zahi A, additional, and Kia, Arash, additional

Published
2023
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95. Implications of the Use of Artificial Intelligence Predictive Models in Health Care Settings

Author

Vaid, Akhil, primary, Sawant, Ashwin, additional, Suarez-Farinas, Mayte, additional, Lee, Juhee, additional, Kaul, Sanjeev, additional, Kovatch, Patricia, additional, Freeman, Robert, additional, Jiang, Joy, additional, Jayaraman, Pushkala, additional, Fayad, Zahi, additional, Argulian, Edgar, additional, Lerakis, Stamatios, additional, Charney, Alexander W., additional, Wang, Fei, additional, Levin, Matthew, additional, Glicksberg, Benjamin, additional, Narula, Jagat, additional, Hofer, Ira, additional, Singh, Karandeep, additional, and Nadkarni, Girish N., additional

Published
2023
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