Pulmonology

Explainable Cryobiopsy AI Model, CRAI, to Predict Disease Progression for Transbronchial Lung Cryobiopsies with Interstitial Pneumonia

Interstitial lung disease (ILD) encompasses diverse pulmonary disorders with varied prognoses. Current pathological diagnoses suffer from inter-observer variability,necessitating more standardized approaches. We developed an ensemble model AI for cryobiopsy, CRAI, an artificial intelligence model to analyze transbronchial lung cryobiopsy (TBLC) specimens and predict patient outcomes. We developed ...

Increasing Value in the Veterans Affairs Healthcare System (VA) with Precision Health: A Continuing Landmark Collaboration with the Department of Energy

By personalizing healthcare to an individual’s specific requirements, precision health promises to maximize benefit and minimize harm, thereby maximizing value. We describe here, how in Phase 2 of the Million Veteran Program–Computational Health Analytics for Medical Precision to Improve Outcomes Now (MVP-CHAMPION), artificial intelligence (AI) and high performance computing (HPC) have been applie...

Closing the Lung Cancer Screening Gap in FQHCs with AI-Powered Clinical Decision Support

Lung cancer remains the leading cause of cancer-related mortality in the United States, with screening adherence rates below 16% nationally and even l...

Epigenetic patient stratification reveals a sub-endotype of type 2 asthma with altered B-cell response

Despite biomarker-guided treatment strategies, clinical outcomes among patients with type 2 (T2)-high asthma remain heterogeneous, with some patients ...

Chronic Obstructive Pulmonary Disease Prediction Using Deep Convolutional Network

Artificial intelligence and deep learning are increasingly applied in the clinical domain, particularly for early and accurate disease detection using...

Risk prediction for lung cancer screening: a systematic review and meta-regression

Lung cancer (LC) is the leading cause of cancer mortality, often diagnosed at advanced stages. Screening reduces mortality in high-risk individuals, b...

Deep learning-based prediction of cardiopulmonary disease in retinal images of premature infants

Bronchopulmonary dysplasia (BPD) and pulmonary hypertension (PH) are leading causes of morbidity and mortality in premature infants. To determine whet...

Bayesian machine learning enables discovery of risk factors for hepatosplenic multimorbidity related to schistosomiasis

One in 25 deaths worldwide is related to liver disease, and often with multiple hepatosplenic conditions. Yet, little is understood of the risk factor...

Plasma Proteomics Linking Primary and Secondary diseases: Insights into Molecular Mediation from UK Biobank Data

Diabetes, hypertension, and dyslipidemia are major risk factors for cardiovascular (CVD), cerebral, and renal diseases (RD). However, the underlying m...

Combining blood transcriptomic signatures improves the prediction of progression to tuberculosis among household contacts in Brazil

Tuberculosis remains a major health threat, infecting nearly a third of the world’s population. Of those infected, 5-10% progress from latent infectio...

Predicting Vaping Cessation in Young Adults: A Machine Learning and Explainable Artificial Intelligence (XAI) Approach to Public Health Intervention

The public health impact of vaping in the United States reflects a complex balance of potential benefits and emerging risks. While e-cigarettes can su...

Using machine learning to discover correlations in MIMIC high-cadence data sets

Using the MIMIC-IV clinical database and, specifically, the related preliminary MIMIC-IV waveform data release, we endeavored to create a machine lear...

How do clinician and parent reported data differ? An analysis of similarity and difference in the datasets from a cross-syndrome genetics cohort study(GenROC)

Parent/patient-reported datasets provide ready access to phenotypic data for monogenic neurodevelopmental disorders yet their concordance with clinica...

Development and Validation of a Machine Learning Model That Uses Voice to Predict Aspiration Risk

Aspiration causes or aggravates a variety of respiratory diseases. Subjective bedside evaluations of aspiration are limited by poor inter-and intra-ra...

Non-contiguous Computed Tomography Lung Scans Can be Manipulated to Permit Artificial Intelligence Analyses for Interstitial Lung Disease in Systemic Sclerosis

Artificial Intelligence can analyse high resolution CT lung scans (HRCT) in various interstitial lung diseases (ILD) including Systemic Sclerosis (SSc...

Body composition and melanoma incidence risk: insights from a longitudinal lung cancer screening cohort

This study explored the association between low-dose computed tomography (LDCT)-derived body composition and melanoma incidence risk. LDCT scans from ...

Using Hourly Aggregated Respiratory Rate and Expiratory Time with Machine Learning to Identify Remote COPD Exacerbations

Exacerbations of chronic obstructive pulmonary disease (COPD) are a major cause of morbidity and mortality. Various models for identifying exacerbatio...

Machine learning predicts treatment response to nusinersen in non-sitter Spinal Muscular Atrophy (SMA)

Nusinersen has substantially increased survival and improved disease progression in Spinal Muscular Atrophy (SMA) patients. However, treatment respons...

Proteomic signatures and machine learning based-prediction models for cardiovascular risk in survivors of myocardial infarction

Survivors of myocardial infarction (MI) are still at risk for adverse long-term outcomes such as all-cause mortality, heart failure (HF), and ischemic...

Thymus Composition, Disease Control, and Toxicity in Locally Advanced Lung Cancer

Thymic involution, characterized by adipose replacement of functional thymic tissue, is a broadly recognized feature of age-related immunosenescence. ...