AIMC Topic: Connective Tissue Diseases
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Application of machine learning in depression risk prediction for connective tissue diseases.
Scientific reports
Jan 11, 2025
This study retrospectively collected clinical data from 480 patients with connective tissue diseases (CTDs) at Nanjing First Hospital between August 2019 and December 2023 to develop and validate a multi-classification machine learning (ML) model for...
Automatic classification of HEp-2 specimens by explainable deep learning and Jensen-Shannon reliability index.
Artificial intelligence in medicine
Nov 28, 2024
The Anti-Nuclear Antibodies (ANA) test using Human Epithelial type 2 (HEp-2) cells in the Indirect Immuno-Fluorescence (IIF) assay protocol is considered the gold standard for detecting Connective Tissue Diseases. Computer-assisted systems for HEp-2 ...
An explainable machine learning-based model to predict intensive care unit admission among patients with community-acquired pneumonia and connective tissue disease.
Respiratory research
Jun 18, 2024
BACKGROUND: There is no individualized prediction model for intensive care unit (ICU) admission on patients with community-acquired pneumonia (CAP) and connective tissue disease (CTD) so far. In this study, we aimed to establish a machine learning-ba...
Computed Tomography-Based Deep Learning Model for Assessing the Severity of Patients With Connective Tissue Disease-Associated Interstitial Lung Disease.
Journal of computer assisted tomography
Jul 7, 2023
OBJECTIVES: This study aimed to develop a computed tomography (CT)-based deep learning model for assessing the severity of patients with connective tissue disease (CTD)-associated interstitial lung disease (ILD).
Classification of pulmonary sounds through deep learning for the diagnosis of interstitial lung diseases secondary to connective tissue diseases.
Computers in biology and medicine
Apr 20, 2023
Early diagnosis of interstitial lung diseases secondary to connective tissue diseases is critical for the treatment and survival of patients. The symptoms, like dry cough and dyspnea, appear late in the clinical history and are not specific, moreover...
Prediction of long-term mortality by using machine learning models in Chinese patients with connective tissue disease-associated interstitial lung disease.
Respiratory research
Jan 7, 2022
BACKGROUND: The exact risk assessment is crucial for the management of connective tissue disease-associated interstitial lung disease (CTD-ILD) patients. In the present study, we develop a nomogram to predict 3‑ and 5-year mortality by using machine ...
Benchmarking human epithelial type 2 interphase cells classification methods on a very large dataset.
Artificial intelligence in medicine
Aug 13, 2015
OBJECTIVE: This paper presents benchmarking results of human epithelial type 2 (HEp-2) interphase cell image classification methods on a very large dataset. The indirect immunofluorescence method applied on HEp-2 cells has been the gold standard to i...
A computed tomography-based deep learning radiomics model for predicting the gender-age-physiology stage of patients with connective tissue disease-associated interstitial lung disease.
Computers in biology and medicine
Jun 1, 2025
OBJECTIVES: To explore the feasibility of using a diagnostic model constructed with deep learning-radiomics (DLR) features extracted from chest computed tomography (CT) images to predict the gender-age-physiology (GAP) stage of patients with connecti...
The Analysis of Proteomics by Machine Learning in Separating Idiopathic Pulmonary Fibrosis from Connective Tissue Disease-Interstitial Lung Disease.
American journal of respiratory and critical care medicine
Aug 15, 2024
Machine Learning of Plasma Proteomics Classifies Diagnosis of Interstitial Lung Disease.
American journal of respiratory and critical care medicine
Aug 15, 2024
Distinguishing connective tissue disease-associated interstitial lung disease (CTD-ILD) from idiopathic pulmonary fibrosis (IPF) can be clinically challenging. To identify proteins that separate and classify patients with CTD-ILD and those with IPF...
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