Conventional machine learning-based prediction models did not outperform the International IgA Nephropathy Prediction Tool.

Background: Immunoglobulin A nephropathy (IgAN) is a major cause of end-stage kidney disease (ESKD). The International IgA Nephropathy Prediction Tool (IIgAN-PT) predicts IgAN prognosis, but improvement in the prediction performance using machine learning (ML)-based methods is needed.

Methods: We analyzed 4,425 biopsy-confirmed patients with IgAN and ≥6 months of follow-up from nine tertiary university hospitals in Korea.

Deep learning-based fully automatic Risser stage assessment model using abdominal radiographs.

Background: Artificial intelligence has been increasingly used in medical imaging and has demonstrated expert level performance in image classification tasks.

Objective: To develop a fully automatic approach for determining the Risser stage using deep learning on abdominal radiographs.

Materials And Methods: In this multicenter study, 1,681 supine abdominal radiographs (age range, 9-18 years, 50% female) obtained between January 2019 and April 2022 were collected retrospectively from three medical institutions and graded manually using the United States Risser staging system.

A Deep Learning Model Using Chest Radiographs for Prediction of 30-Day Mortality in Patients With Community-Acquired Pneumonia: Development and External Validation.

Chest radiography is an essential tool for diagnosing community-acquired pneumonia (CAP), but it has an uncertain prognostic role in the care of patients with CAP. The purpose of this study was to develop a deep learning (DL) model to predict 30-day mortality from diagnosis among patients with CAP by use of chest radiographs to validate the performance model in patients from different time periods and institutions. In this retrospective study, a DL model was developed from data on 7105 patients from one institution from March 2013 to December 2019 (3:1:1 allocation to training, validation, and internal test sets) to predict the risk of all-cause mortality within 30 days after CAP diagnosis by use of patients' initial chest radiographs.

Development and validation of common data model-based fracture prediction model using machine learning algorithm.

Unlabelled: The need for an accurate country-specific real-world-based fracture prediction model is increasing. Thus, we developed scoring systems for osteoporotic fractures from hospital-based cohorts and validated them in an independent cohort in Korea. The model includes history of fracture, age, lumbar spine and total hip T-score, and cardiovascular disease.

Applications of artificial intelligence in the thorax: a narrative review focusing on thoracic radiology.

Objective: This review will focus on how AI-and, specifically, deep learning-can be applied to complement aspects of the current healthcare system. We describe how AI-based tools can augment existing clinical workflows by discussing the applications of AI to worklist prioritization and patient triage, the performance-boosting effects of AI as a second reader, and the use of AI to facilitate complex quantifications. We also introduce prominent examples of recent AI applications, such as tuberculosis screening in resource-constrained environments, the detection of lung cancer with screening CT, and the diagnosis of COVID-19.

ESRRA (estrogen-related receptor α) is a key coordinator of transcriptional and post-translational activation of autophagy to promote innate host defense.

The orphan nuclear receptor ESRRA (estrogen-related receptor α) is a key regulator of energy homeostasis and mitochondrial function. Macroautophagy/autophagy, an intracellular degradation process, is a critical innate effector against intracellular microbes. Here, we demonstrate that ESRRA is required for the activation of autophagy to promote innate antimicrobial defense against mycobacterial infection.