Pulmonology

Radiomics-based machine learning for automated detection of Pneumothorax in CT scans.

The increasing complexity of diagnostic imaging often leads to misinterpretations and diagnostic errors, particularly in critical conditions such as pneumothorax. This study addresses the pressing need for improved diagnostic accuracy in CT scans by developing an intelligent model that leverages radiomics features and machine learning techniques. By enhancing the detection of pneumothorax, this re...

Deep learning using histological images for gene mutation prediction in lung cancer: a multicentre retrospective study.

BACKGROUND: Accurate detection of driver gene mutations is crucial for treatment planning and predicting prognosis for patients with lung cancer. Conventional genomic testing requires high-quality tissue samples and is time-consuming and resource-consuming, and as a result, is not available for most patients, especially those in low-resource settings. We aimed to develop an annotation-free Deep le...

A machine learning-based risk score for prediction of mechanical ventilation in children with dengue shock syndrome: A retrospective cohort study.

BACKGROUND: Patients with severe dengue who develop severe respiratory failure requiring mechanical ventilation (MV) support have significantly increa...

Diagnostic modalities in the mediastinum and the role of bronchoscopy in mediastinal assessment: a narrative review.

BACKGROUND AND OBJECTIVE: Diagnosis of pathology in the mediastinum has proven quite challenging, given the wide variability of both benign and malign...

Machine learning-based prediction of antibiotic resistance in Mycobacterium tuberculosis clinical isolates from Uganda.

BACKGROUND: Efforts toward tuberculosis management and control are challenged by the emergence of Mycobacterium tuberculosis (MTB) resistance to exist...

Machine learning-aided discovery of T790M-mutant EGFR inhibitor CDDO-Me effectively suppresses non-small cell lung cancer growth.

BACKGROUND: Epidermal growth factor receptor (EGFR) T790M mutation often occurs during long durational erlotinib treatment of non-small cell lung canc...

Roadmap for the evolution of monitoring: developing and evaluating waveform-based variability-derived artificial intelligence-powered predictive clinical decision support software tools.

BACKGROUND: Continuous waveform monitoring is standard-of-care for patients at risk for or with critically illness. Derived from waveforms, heart rate...

MLVICX: Multi-Level Variance-Covariance Exploration for Chest X-Ray Self-Supervised Representation Learning.

Self-supervised learning (SSL) reduces the need for manual annotation in deep learning models for medical image analysis. By learning the representati...

Using machine learning for personalized prediction of longitudinal coronavirus disease 2019 vaccine responses in transplant recipients.

The coronavirus disease 2019 pandemic has underscored the importance of vaccines, especially for immunocompromised populations like solid organ transp...

Advancing cancer diagnosis and prognostication through deep learning mastery in breast, colon, and lung histopathology with ResoMergeNet.

Cancer, a global health threat, demands effective diagnostic solutions to combat its impact on public health, particularly for breast, colon, and lung...

Predicting cancer content in tiles of lung squamous cell carcinoma tumours with validation against pathologist labels.

BACKGROUND: A growing body of research is using deep learning to explore the relationship between treatment biomarkers for lung cancer patients and ca...

Prediction of prolonged mechanical ventilation in the intensive care unit via machine learning: a COVID-19 perspective.

Early recognition of risk factors for prolonged mechanical ventilation (PMV) could allow for early clinical interventions, prevention of secondary com...

Automated derivation of diagnostic criteria for lung cancer using natural language processing on electronic health records: a pilot study.

BACKGROUND: The digitisation of healthcare records has generated vast amounts of unstructured data, presenting opportunities for improvements in disea...

Diverse regulated cell death patterns and immune traits in kidney allograft with fibrosis: a prediction of renal allograft failure based on machine learning, single-nucleus RNA sequencing and molecular docking.

Post-transplant allograft fibrosis remains a challenge in prolonging allograft survival. Regulated cell death has been widely implicated in various k...

Research Trends of Artificial Intelligence in Lung Cancer: A Combined Approach of Analysis With Latent Dirichlet Allocation and HJ-Biplot Statistical Methods.

Lung cancer (LC) remains one of the leading causes of cancer-related mortality worldwide. With recent technological advances, artificial intelligence ...

Endothelial progenitor cells as an angiogenic biomarker for the diagnosis and prognosis of lung cancer.

BACKGROUND: Angiogenesis is mediated by endothelial progenitor cells (EPCs) derived from bone-marrow. In this prospective study, we tried to investiga...

PREDICTING IN-HOSPITAL MORTALITY IN CRITICAL ORTHOPEDIC TRAUMA PATIENTS WITH SEPSIS USING MACHINE LEARNING MODELS.

Purpose: This study aims to establish and validate machine learning-based models to predict death in hospital among critical orthopedic trauma patient...

Identification of structural stability and fragility of mouse liver glycogen via label-free Raman spectroscopy coupled with convolutional neural network algorithm.

Glycogen structure is closely associated with its physiological functions. Previous studies confirmed that liver glycogen structure had two dominant s...

Identification of diagnostic biomarkers and molecular subtype analysis associated with m6A in Tuberculosis immunopathology using machine learning.

Tuberculosis (TB), ranking just below COVID-19 in global mortality, is a highly complex infectious disease involving intricate immunological molecules...

Accurate Lung Nodule Segmentation With Detailed Representation Transfer and Soft Mask Supervision.

Accurate lung lesion segmentation from computed tomography (CT) images is crucial to the analysis and diagnosis of lung diseases, such as COVID-19 and...