Article
Radiology, Nuclear Medicine & Medical Imaging
Matthew D. Li, Brent P. Little, Tarik K. Alkasab, Dexter P. Mendoza, Marc D. Succi, Jo-Anne O. Shepard, Michael H. Lev, Jayashree Kalpathy-Cramer
Summary: This study aimed to investigate whether an artificial intelligence system could improve the consistency of radiologist reporting of COVID-19 pneumonia on chest radiographs. The results showed that the use of an AI system led to improved interrater agreement among radiologists, with disease severity categories significantly associated with clinical outcomes.
ACADEMIC RADIOLOGY
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Toshimasa Matsumoto, Shoichi Ehara, Shannon L. Walston, Yasuhito Mitsuyama, Yukio Miki, Daiju Ueda
Summary: This study aimed to develop an artificial intelligence model to detect features of atrial fibrillation on chest radiographs. By training, tuning, and evaluating the model on different datasets, the study demonstrated the effectiveness and accuracy of the AI in identifying AF.
EUROPEAN RADIOLOGY
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Toshimasa Matsumoto, Shannon Leigh Walston, Michael Walston, Daijiro Kabata, Yukio Miki, Masatsugu Shiba, Daiju Ueda
Summary: Accurate estimation of mortality and time to death at admission for COVID-19 patients is important and can be achieved by using a deep learning model that combines chest radiographs and clinical data.
JOURNAL OF DIGITAL IMAGING
(2023)
Article
Medicine, General & Internal
Judith Becker, Josua A. Decker, Christoph Roemmele, Maria Kahn, Helmut Messmann, Markus Wehler, Florian Schwarz, Thomas Kroencke, Christian Scheurig-Muenkler
Summary: This study evaluates the performance of a commercially available deep learning algorithm in detecting pneumonia in chest radiographs in emergency departments, showing high accuracy and sensitivity compared to radiologists' assessment. The deep learning algorithm proves to be a promising tool for supporting the evaluation and early triage of chest radiographs.
Article
Medicine, General & Internal
Matthew D. Li, Nishanth T. Arun, Mehak Aggarwal, Sharut Gupta, Praveer Singh, Brent P. Little, Dexter P. Mendoza, Gustavo C. A. Corradi, Marcelo S. Takahashi, Suely F. Ferraciolli, Marc D. Succi, Min Lang, Bernardo C. Bizzo, Ittai Dayan, Felipe C. Kitamura, Jayashree Kalpathy-Cramer
Summary: This study tuned and tested a deep learning model for assessing COVID-19 lung disease severity on chest radiographs from different patient populations. The model showed good performance across multiple datasets, including outpatient and hospitalized patients from two continents.
Article
Medicine, General & Internal
Joseph Bae, Saarthak Kapse, Gagandeep Singh, Rishabh Gattu, Syed Ali, Neal Shah, Colin Marshall, Jonathan Pierce, Tej Phatak, Amit Gupta, Jeremy Green, Nikhil Madan, Prateek Prasanna
Summary: This study aimed to predict mechanical ventilation requirement and mortality for COVID-19 patients using computed modeling of chest radiographs. Various machine learning classifiers were trained and evaluated, with radiomic features showing improvement in model predictions and aiding in physician decision making during the pandemic.
Article
Cardiac & Cardiovascular Systems
Giuseppe D'Ancona, Mauro Massussi, Mattia Savardi, Alberto Signoroni, Lorenzo Di Bacco, Davide Farina, Marco Metra, Roberto Maroldi, Claudio Muneretto, Huseyin Ince, Davide Costabile, Monica Murero, Giuliano Chizzola, Salvatore Curello, Stefano Benussi
Summary: A deep learning solution using chest radiographs can be used to detect significant coronary artery disease, and it has potential as a predictive tool.
INTERNATIONAL JOURNAL OF CARDIOLOGY
(2023)
Article
Health Care Sciences & Services
Hyun Woo Lee, Hyun Jun Yang, Hyungjin Kim, Ue-Hwan Kim, Dong Hyun Kim, Soon Ho Yoon, Soo-Youn Ham, Bo Da Nam, Kum Ju Chae, Dabee Lee, Jin Young Yoo, So Hyeon Bak, Jin Young Kim, Jin Hwan Kim, Ki Beom Kim, Jung Im Jung, Jae-Kwang Lim, Jong Eun Lee, Myung Jin Chung, Young Kyung Lee, Young Seon Kim, Sang Min Lee, Woocheol Kwon, Chang Min Park, Yun-Hyeon Kim, Yeon Joo Jeong, Kwang Nam Jin, Jin Mo Goo
Summary: This study aimed to develop and validate a prediction model using chest radiography (CXR) and clinical variables to predict clinical outcomes in COVID-19 patients. The combined model using an AI model and clinical information showed good performance in predicting ARDS and need for oxygen supplementation in COVID-19 patients.
JOURNAL OF MEDICAL INTERNET RESEARCH
(2023)
Article
Medicine, General & Internal
Cheng-Yi Kao, Chiao-Yun Lin, Cheng-Chen Chao, Han-Sheng Huang, Hsing-Yu Lee, Chia-Ming Chang, Kang Sung, Ting-Rong Chen, Po-Chang Chiang, Li-Ting Huang, Bow Wang, Yi-Sheng Liu, Jung-Hsien Chiang, Chien-Kuo Wang, Yi-Shan Tsai
Summary: The study successfully designed a deep learning model for pneumothorax detection in chest radiographs and set up an ARAS with improved efficiency and overall diagnostic performance.
Article
Critical Care Medicine
Warren B. Gefter, Benjamin A. Post, Hiroto Hatabu
Summary: Chest radiography (CXR) is the most common imaging examination worldwide, but it often leads to interpretation errors. These errors can have adverse consequences for patients and result in medical malpractice law-suits. This article reviews and illustrates commonly missed CXR findings and their principal causes. Perceptual errors are the main cause of missed findings. The medi-colegal implications of these errors are explained. Understanding commonly missed CXR findings, their causes, and their consequences is crucial for developing strategies to reduce and mitigate these errors.
Article
Radiology, Nuclear Medicine & Medical Imaging
Eduardo J. Mortani Barbosa Jr, Warren B. Gefter, Florin C. Ghesu, Siqi Liu, Boris Mailhe, Awais Mansoor, Sasa Grbic, Sebastian Vogt
Summary: This study leveraged CT-derived volumetric quantification of airspace disease to train a CNN for quantifying AD on CXRs of patients with confirmed COVID-19. The CNN performed at a level comparable to expert human readers in quantifying AD on CXR.
INVESTIGATIVE RADIOLOGY
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Michael D. Kuo, Keith W. H. Chiu, David S. Wang, Anna Rita Larici, Dmytro Poplavskiy, Adele Valentini, Alessandro Napoli, Andrea Borghesi, Guido Ligabue, Xin Hao B. Fang, Hing Ki C. Wong, Sailong Zhang, John R. Hunter, Abeer Mousa, Amato Infante, Lorenzo Elia, Salvatore Golemi, Leung Ho P. Yu, Christopher K. M. Hui, Bradley J. Erickson
Summary: An AI system for COVID-19 detection on presenting CXR was developed and validated. The system demonstrated high accuracy and sensitivity on international test sets, providing valuable clinical implications for screening and prediction of COVID-19.
EUROPEAN RADIOLOGY
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Jong Hyuk Lee, Hyunsook Hong, Gunhee Nam, Eui Jin Hwang, Chang Min Park
Summary: This study assessed the impact of AI diagnostic performance and reader characteristics on the detection of malignant lung nodules during AI-assisted reading of chest radiographs. The use of a high accuracy AI model improved readers' detection performance to a greater extent than a low accuracy AI model, and readers using the high accuracy AI showed a higher susceptibility to changing their diagnosis based on AI suggestions.
Article
Radiology, Nuclear Medicine & Medical Imaging
Cherry Kim, Gaeun Lee, Hongmin Oh, Gyujun Jeong, Sun Won Kim, Eun Ju Chun, Young-Hak Kim, June-Goo Lee, Dong Hyun Yang
Summary: The study developed and validated a deep learning-based automatic CXR CB analysis algorithm (CB_auto) for diagnosing and quantitatively evaluating valvular heart disease (VHD). The CB_auto system provided highly reliable CB measurements that could be useful in both clinical practice and research.
EUROPEAN RADIOLOGY
(2022)
Article
Medicine, General & Internal
Shu-Tien Huang, Liong-Rung Liu, Hung-Wen Chiu, Ming-Yuan Huang, Ming-Feng Tsai
Summary: Rib fractures are a common injury among trauma patients, and accurate and timely diagnosis is crucial. This study explores the potential of deep convolutional neural networks (DCNNs) in identifying rib fractures on chest radiographs.
FRONTIERS IN MEDICINE
(2023)
Review
Hematology
Wouter S. Hoogenboom, Tharun T. Alamuri, Daniel M. McMahon, Nino Balanchivadze, Vrushali Dabak, William B. Mitchell, Kerry B. Morrone, Deepa Manwani, Tim Q. Duong
Summary: Individuals with sickle cell disease (SCD) and sickle cell trait (SCT) are more susceptible to severe COVID-19 illness and death compared to the general population. Studies have shown that adults with SCD have a milder disease course but an increased risk of hospitalization and death compared to those without SCD. There is also evidence of increased risk of hospitalization and death for individuals with SCT. Children with SCD generally have a mild COVID-19 course, but those with SCD-related comorbidities may require more care. SCD-directed therapies may be associated with better COVID-19 outcomes, but further research is needed for confirmation.
Article
Multidisciplinary Sciences
Wouter S. Hoogenboom, Joyce Q. Lu, Benjamin Musheyev, Lara Borg, Rebeca Janowicz, Stacey Pamlayne, Wei Hou, Tim Q. Duong
Summary: This study investigated the survival of critically ill COVID-19 patients who received prophylactic or therapeutic dose anticoagulation (AC) and found that the therapeutic AC group had a significantly higher death rate after 3 or more weeks of ICU stay. Acute kidney injury (AKI), age, lymphocyte count, and cardiovascular disease were identified as important risk factors for increased mortality.
Article
Medicine, General & Internal
Joyce Q. Lu, Justin Y. Lu, Weihao Wang, Yuhang Liu, Alexandra Buczek, Roman Fleysher, Wouter S. Hoogenboom, Wei Zhu, Wei Hou, Carlos J. Rodriguez, Tim Q. Duong
Summary: This study investigated the incidence of persistent acute cardiac injury (ACI) in COVID-19 survivors and identified clinical predictors of recovery. The results showed that ACI is common among COVID-19 survivors, and readily available patient data can accurately predict ACI recovery.
Article
Multidisciplinary Sciences
Eric R. Muir, Saurav B. Chandra, Divya Narayanan, Vincent Zhang, Ike Zhang, Zhao Jiang, Jeffrey W. Kiel, Timothy Q. Duong
Summary: This study investigated the effects of chronic mild hyperoxia on retinal function and retinal and choroidal blood flow in a mouse model of glaucoma. The results showed that despite a progressive decline in blood flow, retinal function was improved in the mice treated with oxygen compared to the untreated mice.
Article
Medicine, General & Internal
Benjamin Musheyev, Montek S. Boparai, Reona Kimura, Rebeca Janowicz, Stacey Pamlanye, Wei Hou, Tim Q. Duong
Summary: The medical specialty usage of COVID-19 survivors after hospital discharge was investigated in this study. The findings showed that a high incidence of persistent symptoms and medical specialty care needs were present in hospitalized COVID-19 survivors 1-24 months post-discharge. Some specialty care needs were COVID-19 related while others were associated with pre-existing medical conditions.
INTERNAL AND EMERGENCY MEDICINE
(2023)
Article
Multidisciplinary Sciences
Aaquib Q. Syed, Richard Adam, Thomas Ren, Jinyu Lu, Takouhie Maldjian, Tim Duong
Summary: Using extreme gradient boosting (XGBoost) with MRI and non-imaging data, it is possible to predict pathological complete response (pCR) after neoadjuvant chemotherapy. By analyzing texture features of DWI and DCE images, along with patient demographics and tumor data, pCR can be accurately predicted. The combination of MRI and non-MRI data from multiple treatment timepoints as inputs achieves the highest prediction accuracy.
Article
Multidisciplinary Sciences
Hongyi Dammu, Thomas Ren, Tim Q. Duong
Summary: The goal of this study was to use a novel deep-learning convolutional-neural-network (CNN) to predict pathological complete response (PCR), residual cancer burden (RCB), and progression-free survival (PFS) in breast cancer patients treated with neoadjuvant chemotherapy using longitudinal multiparametric MRI, demographics, and molecular subtypes as inputs. The results showed that the Integrated approach of CNN outperformed the Stack or Concatenation CNN, and the combination of MRI and non-MRI data performed better than either alone. The best model achieved an accuracy of 0.81 for PCR prediction, 0.80 for RCB prediction, and a mean absolute error of 24.6 months for PFS prediction.
Article
Medicine, General & Internal
Anna Eligulashvili, Megan Darrell, Carolyn Miller, Jeylin Lee, Seth Congdon, Jimmy S. Lee, Kevin Hsu, Judy Yee, Wei Hou, Marjan Islam, Tim Q. Duong
Summary: This study reports the symptoms and assessments of COVID-19 survivors up to five months post-acute infection. The results showed that many survivors experienced issues in pulmonary function, physical, emotional, and cognitive health. Furthermore, lung imaging abnormalities were more common than brain imaging abnormalities.
Review
Endocrinology & Metabolism
Tharun T. Alamuri, Sandhya Mahesh, Kevin Dell'Aquila, Taylor Jan Leong, Rebecca Jennings, Tim Q. Duong
Summary: SARS-CoV-2 infection may lead to endocrine dysfunction and dysregulation of blood sugar levels, causing diabetes mellitus. The relationship between COVID-19 and endocrine dysfunctions is still not completely understood. This review analyzed 27 publications on COVID-19 associated ketosis or diabetic ketoacidosis, suggesting that DKA in the setting of COVID-19 could increase the risk of death, especially in patients with new-onset diabetes. Larger studies with more specific variables are needed for better conclusions.
DIABETES OBESITY & METABOLISM
(2023)
Article
Medicine, General & Internal
Justin Y. Lu, Jack Wilson, Wei Hou, Roman Fleysher, Betsy C. Herold, Kevan C. Herold, Tim Q. Duong
Summary: This study compared the incidences and risk factors of new-onset persistent type-2 diabetes in COVID-19 patients to those in influenza patients. It was found that the incidence of type-2 diabetes was higher in COVID-19 patients compared to influenza patients. The risk of developing persistent type-2 diabetes was also higher in hospitalized COVID-19 patients compared to non-hospitalized COVID-19 patients and hospitalized influenza patients.
Article
Endocrinology & Metabolism
Alexander Y. Y. Xu, Stephen H. H. Wang, Tim Q. Q. Duong
Summary: This study aimed to investigate the incidence of new-onset diabetes in patients with prediabetes after COVID-19 and compared it with those not infected. The study found that hospitalized patients with prediabetes and COVID-19 had a higher incidence of in-hospital and post-infection diabetes compared to those without COVID-19. Critical illness, in-hospital steroid treatment, SARS-CoV-2 infection, and HbA1c were significant predictors of in-hospital diabetes.
BMJ OPEN DIABETES RESEARCH & CARE
(2023)
Article
Medicine, General & Internal
Beiyi Shen, Wei Hou, Zhao Jiang, Haifang Li, Adam J. J. Singer, Mahsa Hoshmand-Kochi, Almas Abbasi, Samantha Glass, Henry C. C. Thode, Jeffrey Levsky, Michael Lipton, Tim Q. Q. Duong
Summary: This study analyzed the temporal characteristics of lung chest X-ray (CXR) scores in COVID-19 patients during hospitalization and their correlation with other clinical variables and outcomes. The results showed that CXR scores have the potential to provide prognosis, guide treatment, and monitor disease progression in COVID-19 patients.
Article
Hematology
Avery Feit, Moshe Gordon, Tharun T. Alamuri, Wei Hou, William B. Mitchell, Deepa Manwani, Tim Q. Duong
Summary: This study examined whether patients with sickle cell disease (SCD) had increased risk of worse long-term outcomes and healthcare utilization 2.5 years after SARS-CoV-2 infection. The results showed that SCD patients with SARS-CoV-2 infection did not have additional risk of worse long-term outcomes compared to matched controls of SCD patients not infected by SARS-CoV-2.
EUROPEAN JOURNAL OF HAEMATOLOGY
(2023)
Article
Peripheral Vascular Disease
Vincent Zhang, Molly Fisher, Wei Hou, Lili Zhang, Tim Q. Duong
Summary: The incidence of new-onset persistent hypertension in patients with COVID-19 is higher than those with influenza, indicating a significant health burden associated with COVID-19.
Article
Rheumatology
Jai Mehrotra-Varma, Anand Kumthekar, Sonya Henry, Roman Fleysher, Wei Hou, Tim Q. Duong
Summary: This retrospective study examined the clinical outcomes of 361 patients with rheumatoid arthritis (RA) who were infected with COVID-19. The results showed that patients with RA and COVID-19 had higher rates of hospitalization, critical illness, and mortality compared to patients with RA without COVID-19. However, these adverse outcomes were not directly attributed to RA itself, but rather to age and preexisting medical conditions.
ACR OPEN RHEUMATOLOGY
(2023)