Pulmonology

Development and evaluation of a multivariable prediction model for overall survival in advanced stage pulmonary carcinoid using machine learning.

BACKGROUND: Evidence is limited on whether patients with advanced pulmonary carcinoid (APC) benefit from comprehensive pulmonary resection (CPR), chemotherapy, or radiotherapy. Existing prognostic models for APC are limited and do not guide treatment selection. This study aims to develop and evaluate a multivariable machine learning model to predict overall survival in APC patients and provide a w...

Histological proven AI performance in the UKLS CT lung cancer screening study: Potential for workload reduction.

PURPOSE: Artificial intelligence (AI) could reduce lung cancer screening computer tomography (CT)-reading workload if used as a first-reader, ruling-out negative CT-scans at baseline. Evidence is lacking to support AI performance when compared to gold-standard lung cancer outcomes. This study validated the performance of a commercially available AI software in the UK lung cancer screening (UKLS) t...

Continuous non-contact monitoring of neonatal activity.

PURPOSE: Neonatal activity is an important physiological parameter in the neonatal intensive care unit (NICU). The degree of neonatal activity is asso...

The feasibility and cost-effectiveness of implementing mobile low-dose computed tomography with an AI-based diagnostic system in underserved populations.

BACKGROUND: Low-dose computed tomography (LDCT) significantly increases early detection rates of lung cancer and reduces lung cancer-related mortality...

Prediction of clinical deterioration within one year in chronic obstructive pulmonary disease using the systemic coagulation-inflammation index: a retrospective study employing multiple machine learning method.

BACKGROUND: Inflammatory response and the coagulation system are pivotal in the pathogenesis of clinical deterioration in chronic obstructive pulmonar...

Constitutive neural networks for main pulmonary arteries: discovering the undiscovered.

Accurate modeling of cardiovascular tissues is crucial for understanding and predicting their behavior in various physiological and pathological condi...

Machine learning selection of basement membrane-associated genes and development of a predictive model for kidney fibrosis.

This study investigates the role of basement membrane-related genes in kidney fibrosis, a significant factor in the progression of chronic kidney dise...

Development of a Machine Learning-Powered Optimized Lung Allocation System for Maximum Benefits in Lung Transplantation: A Korean National Data.

BACKGROUND: An ideal lung allocation system should reduce waiting list deaths, improve transplant survival, and ensure equitable organ allocation. Thi...

Optimizing Bi-LSTM networks for improved lung cancer detection accuracy.

Lung cancer remains a leading cause of cancer-related deaths worldwide, with low survival rates often attributed to late-stage diagnosis. To address t...

Intraoperative stenosis detection in X-ray coronary angiography via temporal fusion and attention-based CNN.

BACKGROUND AND OBJECTIVE: Coronary artery disease (CAD), the leading cause of mortality, is caused by atherosclerotic plaque buildup in the arteries. ...

MERIT: Multi-view evidential learning for reliable and interpretable liver fibrosis staging.

Accurate staging of liver fibrosis from magnetic resonance imaging (MRI) is crucial in clinical practice. While conventional methods often focus on a ...

A hybrid inception-dilated-ResNet architecture for deep learning-based prediction of COVID-19 severity.

Chest computed tomography (CT) scans are essential for accurately assessing the severity of the novel Coronavirus (COVID-19), facilitating appropriate...

Disease diagnostics using machine learning of B cell and T cell receptor sequences.

Clinical diagnosis typically incorporates physical examination, patient history, various laboratory tests, and imaging studies but makes limited use o...

Advanced applications in chronic disease monitoring using IoT mobile sensing device data, machine learning algorithms and frame theory: a systematic review.

The escalating demand for chronic disease management has presented substantial challenges to traditional methods. However, the emergence of Internet o...

Robust Myocardial Perfusion MRI Quantification With DeepFermi.

Stress perfusion cardiac magnetic resonance is an important technique for examining and assessing the blood supply of the myocardium. Currently, the m...

Convolutional long short-term memory neural network integrated with classifier in classifying type of asynchrony breathing in mechanically ventilated patients.

BACKGROUND AND OBJECTIVE: Asynchronous breathing (AB) occurs when a mechanically ventilated patient's breathing does not align with the mechanical ven...

C-UQ: Conflict-based uncertainty quantification-A case study in lung cancer classification.

Uncertainty quantification is crucial in deep learning, especially in medical diagnostics, to measure model prediction confidence and ensure reliable ...

AI-facilitated home monitoring for cystic fibrosis exacerbations across pediatric and adult populations.

BACKGROUND: AI-aided home stethoscopes offer the opportunity of continuous remote monitoring of cystic fibrosis (CF) patients, reducing the need for c...

Multi-omics and single-cell analysis reveals machine learning-based pyrimidine metabolism-related signature in the prognosis of patients with lung adenocarcinoma.

Pyrimidine metabolism is a hallmark of tumor metabolic reprogramming, while its significance in the prognostic and therapeutic implications of patien...

Application of Artificial Intelligence in Acute Ischemic Stroke: A Scoping Review.

Artificial intelligence (AI) is revolutionizing stroke care by enhancing diagnosis, treatment, and outcome prediction. This review examines 505 origin...