Pulmonology

Explainable Artificial Intelligence for Deep-Learning Based Classification of Cystic Fibrosis Lung Changes in MRI.

Algorithms increasing the transparence and explain ability of neural networks are gaining more popularity. Applying them to custom neural network architectures and complex medical problems remains challenging. In this work, several algorithms such as integrated gradients and grad came were used to generate additional explainable outputs for the classification of lung perfusion changes and mucus pl...

Standigm ASK™: knowledge graph and artificial intelligence platform applied to target discovery in idiopathic pulmonary fibrosis.

Standigm ASK™ revolutionizes healthcare by addressing the critical challenge of identifying pivotal target genes in disease mechanisms-a fundamental aspect of drug development success. Standigm ASK™ integrates a unique combination of a heterogeneous knowledge graph (KG) database and an attention-based neural network model, providing interpretable subgraph evidence. Empowering users through an inte...

Identification of candidate biomarkers and molecular networks associated with Pulmonary Arterial Hypertension using machine learning and plasma multi-Omics analysis

Pulmonary arterial hypertension (PAH) is a rare but severe and life- threatening condition that primarily affects the pulmonary blood vessels and the ...

Large Language Model Symptom Identification from Clinical Text: A Multi-Center Study

Recognition of patient symptoms is core to medicine, research, and public health. We tested four large language models (LLMs) identifying 11 symptoms ...

Meta-Learning on Augmented Gene Expression Profiles for Enhanced Lung Cancer Detection.

Gene expression profiles obtained through DNA microarray have proven successful in providing critical information for cancer detection classifiers. Ho...

Deep Learning-based Time-to-event Analysis of Depression and Asthma using the All of Us Research Program.

While there is a growing recognition of the association between depression and asthma, few studies have leveraged deep learning-based (DL-based) model...

Narrative Feature or Structured Feature? A Study of Large Language Models to Identify Cancer Patients at Risk of Heart Failure.

Cancer treatments are known to introduce cardiotoxicity, negatively impacting outcomes and survivorship. Identifying cancer patients at risk of heart ...

Enhancing Antibiotic Stewardship: A Machine Learning Approach to Predicting Antibiotic Resistance in Inpatient Care.

Antibiotics have been crucial in advancing medical treatments, but the growing threat of antibiotic resistance challenges these achievements and empha...

Artificial intelligence versus semi-automatic segmentation of the inferior alveolar canal on cone-beam computed tomography scans: A pilot study.

BACKGROUND: The inferior alveolar canal (IAC) is a fundamental mandibular structure. It is important to conduct a precise pre-surgical evaluation of t...

Enhancing Organizing Pneumonia Diagnosis: A Novel Super-token Transformer Approach for Masson Body Segmentation.

BACKGROUND/AIM: In this study, we introduce an innovative deep-learning model architecture aimed at enhancing the accuracy of detecting and classifyin...

Machine learning approaches for asthma disease prediction among adults in Sri Lanka.

Addressing the challenge of cost-effective asthma diagnosis amidst diverse symptom patterns among patients, this study aims to develop a machine lear...

Artificial Intelligence and Family Medicine.

Artificial intelligence (AI) is certainly going to have a large, potentially huge, impact on the practice of family medicine. The specialty is fortuna...

Artificial Intelligence-suggested Predictive Model of Survival in Patients Treated With Stereotactic Radiotherapy for Early Lung Cancer.

BACKGROUND/AIM: Overall survival (OS)-predictive models to clinically stratify patients with stage I Non-Small Cell Lung Cancer (NSCLC) undergoing ste...

Machine learning-based prediction of cerebral oxygen saturation based on multi-modal cerebral oximetry data.

This study develops machine learning-based algorithms that facilitate accurate prediction of cerebral oxygen saturation using waveform data in the nea...

Pentagon Found Daily, Metagenomic Detection of Novel Bioaerosol Threats to Be Cost-Prohibitive: Can Virtualization and AI Make It Cost-Effective?

In 2022, the Pentagon Force Protection Agency found threat agnostic detection of novel bioaerosol threats to be "not feasible for daily operations" du...

Importance of GWAS Risk Loci and Clinical Data in Predicting Asthma Using Machine-learning Approaches.

INTRODUCTION: To understand the risk factors of asthma, we combined genome-wide association study (GWAS) risk loci and clinical data in predicting ast...

Evaluation of the efficacy and safety of robot-assisted and video assisted thoracic surgery for early non-small cell lung cancer: A meta-analysis.

BACKGROUND: Radical resection of lung cancer and chemotherapy are the main methods for the treatment of early lung cancer, but surgical treatment is s...

Lung cancer detection based on computed tomography image using convolutional neural networks.

BACKGROUND: Lung cancer is the most common type of cancer, accounting for 12.8% of cancer cases worldwide. As initially non-specific symptoms occur, i...

Enhancing Spatial Transcriptomics Analysis by Integrating Image-Aware Deep Learning Methods.

Spatial transcriptomics (ST) represents a pivotal advancement in biomedical research, enabling the transcriptional profiling of cells within their mor...

Diagnosis of Covid-19 from CT slices using Whale Optimization Algorithm, Support Vector Machine and Multi-Layer Perceptron.

BACKGROUND: The coronavirus disease 2019 is a serious and highly contagious disease caused by infection with a newly discovered virus, named severe ac...