Explanipedia

Evaluation of an artificial intelligence model for opportunistic Agatston scoring on non-gated chest computed tomography Open

Sarah Mercaldo, John K. Chin, Ankita Ghatak, Sandeep Hedgire, Brian Ghoshhajra , et al. · 2025

The Agatston score is a measure of cardiovascular disease traditionally calculated on cardiac gated computed tomography (CT) of the chest. Cardiac gated CT is resource-intensive, can be hard to access, and involves extra radiation exposure…

Cardiac Measurement Calculation on Point-of-Care Ultrasonography with Artificial Intelligence Open

Sarah Mercaldo, Bernardo C. Bizzo, Tsion Sadore, Madeleine A Halle, Ashley L MacDonald , et al. · 2025

Introduction Point-of-care ultrasonography (POCUS) enables clinicians to obtain critical diagnostic information at the bedside especially in resource limited settings. This information may include 2D cardiac quantitative data, although mea…

Evaluation of an artificial intelligence model for identification of obstructive hydrocephalus on computed tomography of the head Open

Ankita Ghatak, Isabella Newbury‐Chaet, Sarah Mercaldo, John K. Chin, Madeleine Halle , et al. · 2024

Introduction: Obstructive hydrocephalus is a critical radiographic finding requiring emergent treatment. Its identification on head computed tomography (CT) by an artificial intelligence (AI) model could facilitate sooner life-saving inter…

Evaluation of an artificial intelligence model for identification of obstructive hydrocephalus on computed tomography of the head Open

James Hillis, Ankita Ghatak, Isabella Newbury‐Chaet, Sarah Mercaldo, John K. Chin , et al. · 2024

Introduction: Obstructive hydrocephalus is a critical radiographic finding requiring emergent treatment. Its identification on head computed tomography (CT) by an artificial intelligence (AI) model could facilitate sooner life-saving inter…

Evaluation of an artificial intelligence model for opportunistic calculation of Agatston score on non-gated computed tomography of the chest Open

Suzannah E. McKinney, Sarah Mercaldo, John K. Chin, Ankita Ghatak, Madeleine A Halle , et al. · 2024

Importance The Agatston score is a measure of cardiovascular disease traditionally calculated on cardiac gated computed tomography (CT) of the chest. Cardiac gated CT is resource-intensive, can be hard to access, and involves extra radiati…

Detection of hypertrophic cardiomyopathy on electrocardiogram using artificial intelligence Open

James Hillis, Bernardo C. Bizzo, Sarah Mercaldo, Ankita Ghatak, Ashley L MacDonald , et al. · 2024

Background Hypertrophic cardiomyopathy (HCM) is associated with significant morbidity and mortality including sudden cardiac death in the young. Its prevalence is estimated to be 1 in 500, although many people are undiagnosed. The ability …

Evaluation of an Artificial Intelligence Model for Identification of Intracranial Hemorrhage Subtypes on Computed Tomography of the Head Open

James Hillis, Bernardo C. Bizzo, Isabella Newbury‐Chaet, Sarah Mercaldo, John K. Chin , et al. · 2024

Background Intracranial hemorrhage is a critical finding on computed tomography (CT) of the head. This study compared the accuracy of an artificial intelligence (AI) model (Annalise Enterprise CTB Triage Trauma) to consensus neuroradiologi…

Prediction of Surgical Upstaging Risk of Ductal Carcinoma In Situ Using Machine Learning Models Open

Kimberlee A. Hashiba, Sarah Mercaldo, Sheila L Venkatesh, Manisha Bahl · 2023

Objective The purpose of this study was to build machine learning models to predict surgical upstaging risk of ductal carcinoma in situ (DCIS) to invasive cancer and to compare model performance to eligibility criteria used by the Comparis…

Evaluation of an artificial intelligence model for identification of intracranial hemorrhage subtypes on computed tomography of the head Open

James Hillis, Bernardo C. Bizzo, Isabella Newbury‐Chaet, Sarah Mercaldo, John K. Chin , et al. · 2023

Importance Intracranial hemorrhage is a critical finding on computed tomography (CT) of the head. Objective This study compared the accuracy of an AI model (Annalise Enterprise CTB) to consensus neuroradiologist interpretations in detectin…

Assessing the Relationship Between Race, Language, and Surgical Admissions in the Emergency Department Open

Grant Rigney, Soham Ghoshal, Sarah Mercaldo, Debby Cheng, Jonathan Parks , et al. · 2023

Introduction: English proficiency and race are both independently known to affect surgical access and quality, but relatively little is known about the impact of race and limited English proficiency (LEP) on admission for emergency surgery…

Enhanced physician performance when using an artificial intelligence model to detect ischemic stroke on computed tomography Open

James Hillis, Bernardo C. Bizzo, Romane Gauriau, Christopher P. Bridge, John K. Chin , et al. · 2023

Acute ischemic stroke can be subtle to detect on non-contrast computed tomography imaging. We show that a novel artificial intelligence model significantly improves the performance of physicians, including ED physicians, neurologists and r…

Evaluation of an Artificial Intelligence Model for Detection of Pneumothorax and Tension Pneumothorax in Chest Radiographs Open

James Hillis, Bernardo C. Bizzo, Sarah Mercaldo, John K. Chin, Isabella Newbury‐Chaet , et al. · 2022

Importance Early detection of pneumothorax, most often via chest radiography, can help determine need for emergent clinical intervention. The ability to accurately detect and rapidly triage pneumothorax with an artificial intelligence (AI)…

Deep Learning vs Traditional Breast Cancer Risk Models to Support Risk-Based Mammography Screening Open

Constance D. Lehman, Sarah Mercaldo, Leslie R. Lamb, Tari A. King, Leif W. Ellisen , et al. · 2022

Background Deep learning breast cancer risk models demonstrate improved accuracy compared with traditional risk models but have not been prospectively tested. We compared the accuracy of a deep learning risk score derived from the patient’…

Evaluation of an artificial intelligence model for detection of pneumothorax and tension pneumothorax on chest radiograph Open

James Hillis, Bernardo C. Bizzo, Sarah Mercaldo, John K. Chin, Isabella Newbury‐Chaet , et al. · 2022

Importance Early detection of pneumothorax, most often on chest radiograph (CXR), can help determine need for emergent clinical intervention. The ability to accurately detect and rapidly triage pneumothorax with an artificial intelligence …

Dynamic contrast-enhanced magnetic resonance imaging of the lung reveals important pathobiology in idiopathic pulmonary fibrosis Open

Sydney B. Montesi, Iris Y. Zhou, Lloyd L. Liang, Subba R. Digumarthy, Sarah Mercaldo , et al. · 2021

Introduction Evidence suggests that abnormalities occur in the lung microvasculature in idiopathic pulmonary fibrosis (IPF). We hypothesised that dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) could detect alterations in …

A Clinical Prediction Tool for MRI in Emergency Department Patients with Spinal Infection Open

Steven Shroyer, William T. Davis, Michael D. April, Brit Long, Greg Boys , et al. · 2021

Introduction: Patients with pyogenic spinal Infection (PSI) are often not diagnosed at their initial presentation, and diagnostic delay is associated with increased morbidity and medical-legal risk. We derived a decision tool to estimate t…

Practical application and validation of the 2018 ATS/ERS/JRS/ALAT and Fleischner Society guidelines for the diagnosis of idiopathic pulmonary fibrosis Open

Angela Shih, Chayanin Nitiwarangkul, Brent P. Little, Benjamin W. Roop, Sreyankar Nandy , et al. · 2021

Body Composition on Chest Computed Tomography Predicts Hospital Length of Stay and Complications Following Lobectomy for Lung Cancer: A Multicenter Study Open

Florian J. Fintelmann, Till D. Best, Sarah Mercaldo, J. Peter Marquardt, Ashok Muniappan , et al. · 2020

Obesity and breast cancer screening: Cross‐sectional survey results from the behavioral risk factor surveillance system Open

Randy C. Miles, Constance D. Lehman, Sarah Mercaldo, Rulla M. Tamimi, Brian N. Dontchos , et al. · 2019

Background Postmenopausal obese women demonstrate an elevated breast cancer risk and experience increased breast cancer morbidity and mortality compared with women with a normal body mass index (BMI). However, to the authors' knowledge, pr…

Evaluation of USPSTF Lung Cancer Screening Guidelines Among African American Adult Smokers Open

Melinda C. Aldrich, Sarah Mercaldo, Kim L. Sandler, William J. Blot, Eric L. Grogan , et al. · 2019

Current USPSTF lung cancer screening guidelines may be too conservative for African American smokers. The findings suggest that race-specific adjustment of pack-year criteria in lung cancer screening guidelines would result in more equitab…

Prevention of Prescription Opioid Misuse and Projected Overdose Deaths in the United States Open

Qiushi Chen, Marc R. Larochelle, Davis T. Weaver, Anna P. Lietz, Peter P. Mueller , et al. · 2019

This study's findings suggest that interventions targeting prescription opioid misuse such as prescription monitoring programs may have a modest effect, at best, on the number of opioid overdose deaths in the near future. Additional policy…

Testing for Verification Bias in Reported Malignancy Risks for Side-Branch Intraductal Papillary Mucinous Neoplasms: A Simulation Modeling Approach Open

Davis T. Weaver, Anna P. Lietz, Sarah Mercaldo, Mary Linton B. Peters, Chin Hur , et al. · 2019

Our results suggest that reported malignancy risks associated with side-branch IPMNs are likely to be overestimates and imply the presence of verification bias.

MA20.05 Who Gets Screened for Lung Cancer? A Simple Adjustment to Current Guidelines to Reduce Racial Disparities Open

Melinda C. Aldrich, Sarah Mercaldo, Kim L. Sandler, William J. Blot, Eric L. Grogan , et al. · 2018

Missing data and prediction: the pattern submodel Open

Sarah Mercaldo, Jeffrey D. Blume · 2018

SUMMARY Missing data are a common problem for both the construction and implementation of a prediction algorithm. Pattern submodels (PS)—a set of submodels for every missing data pattern that are fit using only data from that pattern—are a…

Racial Disparities in Lung Cancer Survival: The Contribution of Stage, Treatment, and Ancestry Open

Carissa Jones, Sarah Mercaldo, Jeffrey D. Blume, Angela S. Wenzlaff, Ann G. Schwartz , et al. · 2018

Assessment of Fluorodeoxyglucose F18–Labeled Positron Emission Tomography for Diagnosis of High-Risk Lung Nodules Open

Amelia W. Maiga, Stephen A. Deppen, Sarah Mercaldo, Jeffrey D. Blume, Chandler Montgomery , et al. · 2017

High false-positive rates were observed across a range of cancer prevalence. Normal PET/CT scans were not found to be reliable indicators of the absence of disease in patients with a high probability of lung cancer. In this population, agg…

Bagged Empirical Null p-values: A Method to Account for Model Uncertainty in Large Scale Inference Open

Sarah Mercaldo, Jeffrey D. Blume · 2017

When conducting large scale inference, such as genome-wide association studies or image analysis, nominal $p$-values are often adjusted to improve control over the family-wise error rate (FWER). When the majority of tests are null, procedu…

Missing Data and Prediction Open

Sarah Mercaldo, Jeffrey D. Blume · 2017

Missing data are a common problem for both the construction and implementation of a prediction algorithm. Pattern mixture kernel submodels (PMKS) - a series of submodels for every missing data pattern that are fit using only data from that…

Sarah Mercaldo YOU? Author Swipe