The COVID-19 Pandemic's Effect on Preventive Imaging.

Introduction: This study assessed the effect of the COVID-19 pandemic on preventive care imaging and potential disparities because preventive care may be perceived as nonurgent. The objective was to identify the associations between the COVID-19 pandemic and changes in preventive imaging volumes for patients in general and as affected by race and ethnicities.

Methods: The authors performed a retrospective observational study by extracting the weekly volumes of all imaging studies between January 7, 2019 and May 1, 2022 from a radiology data warehouse at a tertiary care medical center (=92,105 preventive imaging studies and 3,493,063 total radiology imaging studies) and compared preshutdown with postshutdown periods using a 2-sample -test.

Early identification of patients at risk for iron-deficiency anemia using deep learning techniques.

Objectives: Iron-deficiency anemia (IDA) is a common health problem worldwide, and up to 10% of adult patients with incidental IDA may have gastrointestinal cancer. A diagnosis of IDA can be established through a combination of laboratory tests, but it is often underrecognized until a patient becomes symptomatic. Based on advances in machine learning, we hypothesized that we could reduce the time to diagnosis by developing an IDA prediction model.

Understanding and Mitigating Bias in Imaging Artificial Intelligence.

Artificial intelligence (AI) algorithms are prone to bias at multiple stages of model development, with potential for exacerbating health disparities. However, bias in imaging AI is a complex topic that encompasses multiple coexisting definitions. may refer to unequal preference to a person or group owing to preexisting attitudes or beliefs, either intentional or unintentional.

Testicular Radiomics To Predict Pathology At Time of Postchemotherapy Retroperitoneal Lymph Node Dissection for Nonseminomatous Germ Cell Tumor.

Introduction: Testicular germ cell tumors are the most common malignancy in young adult males. Patients with metastatic disease receive standard of care chemotherapy followed by retroperitoneal lymph node dissection for residual masses >1cm. However, there is a need for better preoperative tools to discern which patients will have persistent disease after chemotherapy given low rates of metastatic germ cell tumor after chemotherapy.

Use of Web-Based Calculator for the Implementation of ACR TI-RADS Risk-Stratification System.

In this article, we demonstrate the use of a software-based radiologist reporting tool for the implementation of American College of Radiology Thyroid Imaging, Reporting and Data System thyroid nodule risk-stratification. The technical details are described with emphasis on addressing the information security and patient privacy issues while allowing it to integrate with the electronic health record and radiology reporting dictation software. Its practical implementation is assessed in a quality improvement project in which guideline adherence and recommendation congruence were measured pre and post implementation.

Deep Learning-Based COVID-19 Pneumonia Classification Using Chest CT Images: Model Generalizability.

Since the outbreak of the COVID-19 pandemic, worldwide research efforts have focused on using artificial intelligence (AI) technologies on various medical data of COVID-19-positive patients in order to identify or classify various aspects of the disease, with promising reported results. However, concerns have been raised over their generalizability, given the heterogeneous factors in training datasets. This study aims to examine the severity of this problem by evaluating deep learning (DL) classification models trained to identify COVID-19-positive patients on 3D computed tomography (CT) datasets from different countries.

Active Reprioritization of the Reading Worklist Using Artificial Intelligence Has a Beneficial Effect on the Turnaround Time for Interpretation of Head CT with Intracranial Hemorrhage.

Purpose: To determine how to optimize the delivery of machine learning techniques in a clinical setting to detect intracranial hemorrhage (ICH) on non-contrast-enhanced CT images to radiologists to improve workflow.

Materials And Methods: In this study, a commercially available machine learning algorithm that flags abnormal noncontrast CT examinations for ICH was implemented in a busy academic neuroradiology practice between September 2017 and March 2019. The algorithm was introduced in three phases: as a "pop-up" widget on ancillary monitors, as a marked examination in reading worklists, and as a marked examination for reprioritization based on the presence of the flag.

Use of Spectral Detector Computed Tomography to Improve Liver Segmentation and Volumetry.

Introduction: Liver segmentation and volumetry have traditionally been performed using computed tomography (CT) attenuation to discriminate liver from other tissues. In this project, we evaluated if spectral detector CT (SDCT) can improve liver segmentation over conventional CT on 2 segmentation methods.

Materials And Methods: In this Health Insurance Portability and Accountability Act-compliant institutional review board-approved retrospective study, 30 contrast-enhanced SDCT scans with healthy livers were selected.

Chemical transfection of dye-conjugated microRNA precursors for microRNA functional analysis of M2 macrophages.

MicroRNAs (miRNAs) are short noncoding ribonucleic acids known to affect gene expression at the translational level and there is mounting evidence that miRNAs play a role in the function of tumor-associated macrophages (TAMs). To aid the functional analyses of miRNAs in an in-vitro model of TAMs known as M2 macrophages, a transfection method to introduce artificial miRNA constructs or miRNA molecules into primary human monocytes is needed. Unlike differentiated macrophages or dendritic cells, undifferentiated primary human monocytes have been known to show resistance to lentiviral transduction.