BACKGROUND/AIM: Sarcoidosis is a multisystem disorder that affects many organs, including the kidneys. This single-center retrospective study investigated the clinical, pathological, and laboratory findings of patients with kidney sarcoidosis who wer...
Sarcoidosis is a chronic granulomatous disorder characterized by unknown etiology, undetermined mechanisms, and non-specific therapies except TNF blockade. To improve our understanding of the pathogenicity and to predict the outcomes of the disease, ...
Circulation journal : official journal of the Japanese Circulation Society
34176867
BACKGROUND: Because the early diagnosis of subclinical cardiac sarcoidosis (CS) remains difficult, we developed a deep learning algorithm to distinguish CS patients from healthy subjects using echocardiographic movies.
The international journal of cardiovascular imaging
35194707
Recognizing early cardiac sarcoidosis (CS) imaging phenotypes can help identify opportunities for effective treatment before irreversible myocardial pathology occurs. We aimed to characterize regional CS myocardial remodeling features correlating wit...
BMC medical informatics and decision making
36266674
BACKGROUND: In this work, we developed many machine learning classifiers to assist in diagnosing respiratory changes associated with sarcoidosis, based on results from the Forced Oscillation Technique (FOT), a non-invasive method used to assess pulmo...
Electronic nose (eNose) technology is an emerging diagnostic application, using artificial intelligence to classify human breath patterns. These patterns can be used to diagnose medical conditions. Sarcoidosis is an often difficult to diagnose diseas...
PURPOSE: To determine the reliability of an artificial intelligence, deep learning (AI/DL)-based method of chest computer tomography (CT) scan analysis to distinguish pulmonary sarcoidosis from negative lung cancer screening chest CT scans (Lung Imag...
OBJECTIVES: To investigate the usefulness of machine learning (ML) models using pretreatment F-FDG-PET-based radiomic features for predicting adverse clinical events (ACEs) in patients with cardiac sarcoidosis (CS).
PURPOSE OF REVIEW: Sarcoidosis is a systemic, granulomatous disease of uncertain cause. Diagnosis may be difficult, prognosis uncertain and response to treatment unpredictable. The application of artificial intelligence to sarcoidosis may provide cli...
Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
39368659
BACKGROUND: Fluorodeoxyglucose positron emission tomography (FDG PET) with suppression of myocardial glucose utilization plays a pivotal role in diagnosing cardiac sarcoidosis. Reorientation of images to match perfusion datasets and myocardial segmen...