Pulmonology

Uncertainty CNNs: A path to enhanced medical image classification performance.

The automated detection of tumors using medical imaging data has garnered significant attention over the past decade due to the critical need for early and accurate diagnoses. This interest is fueled by advancements in computationally efficient modeling techniques and enhanced data storage capabilities. However, methodologies that account for the uncertainty of predictions remain relatively uncomm...

[Exploration of the Predictive Value of Peripheral Blood-related Indicators for EGFR 
Mutations and Prognosis in Non-small Cell Lung Cancer Using Machine Learning].

BACKGROUND: Epidermal growth factor receptor (EGFR) sensitive mutation is one of the effective targets of targeted therapy for non-small cell lung cancer (NSCLC). However, due to the difficulty of obtaining some primary tissues and the economic factors in some underdeveloped areas, some patients cannot undergo traditional genetic testing. The aim of this study is to establish a machine learning (M...

Prediction of Clinical Complication Onset using Neural Point Processes

Predicting medical events in advance within critical care settings is paramount for patient outcomes and resource management. Utilizing predictive m...

A Guide for Implementing an A.I-Driven Initiative in Rural Northern Ontario.

Diagnosing pulmonary embolism (PE) often requires specialized expertise in interpreting x-rays and radiographic images, resources that are mostly limi...

Pulmonary Tuberculosis Edge Diagnosis System Based on MindSpore Framework: Low-cost and High-precision Implementation with Ascend 310 Chip

Pulmonary Tuberculosis (PTB) remains a major challenge for global health, especially in areas with poor medical resources, where access to specializ...

Evaluation of risk factors for thromboembolic events in multiple myeloma patients using multiple machine learning models.

Venous thromboembolic events (VTE) is a frequent complication in multiple myeloma (MM) patients, raising mortality. This study aims to use machine lea...

[Application of artificial intelligence in combination with CT radiomics in chronic obstructive pulmonary disease].

As CT imaging is increasingly used for the evaluation of lung nodules and the diagnosis and screening of lung cancer in smokers, we have more opportun...

Enhancing Pneumonia Diagnosis and Severity Assessment through Deep Learning: A Comprehensive Approach Integrating CNN Classification and Infection Segmentation

Lung disease poses a substantial global health challenge, with pneumonia being a prevalent concern. This research focuses on leveraging deep learnin...

4D VQ-GAN: Synthesising Medical Scans at Any Time Point for Personalised Disease Progression Modelling of Idiopathic Pulmonary Fibrosis

Understanding the progression trajectories of diseases is crucial for early diagnosis and effective treatment planning. This is especially vital for...

AI-Driven Electronic Health Records System for Enhancing Patient Data Management and Diagnostic Support in Egypt

Digital healthcare infrastructure is crucial for global medical service delivery. Egypt faces EHR adoption barriers: only 314 hospitals had such sys...

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors

Neural networks have become the standard technique for medical diagnostics, especially in cancer detection and classification. This work evaluates t...

Investigating the impact of kernel harmonization and deformable registration on inspiratory and expiratory chest CT images for people with COPD

Paired inspiratory-expiratory CT scans enable the quantification of gas trapping due to small airway disease and emphysema by analyzing lung tissue ...

Rule-Based Modeling of Low-Dimensional Data with PCA and Binary Particle Swarm Optimization (BPSO) in ANFIS

Fuzzy rule-based systems interpret data in low-dimensional domains, providing transparency and interpretability. In contrast, deep learning excels i...

Towards Fair Medical AI: Adversarial Debiasing of 3D CT Foundation Embeddings

Self-supervised learning has revolutionized medical imaging by enabling efficient and generalizable feature extraction from large-scale unlabeled da...

AI-Based Thermal Video Analysis in Privacy-Preserving Healthcare: A Case Study on Detecting Time of Birth

Approximately 10% of newborns need some assistance to start breathing and 5\% proper ventilation. It is crucial that interventions are initiated as ...

Test Time Training for 4D Medical Image Interpolation

4D medical image interpolation is essential for improving temporal resolution and diagnostic precision in clinical applications. Previous works igno...

PNL: a software to build polygenic risk scores using a super learner approach based on PairNet, a Convolutional Neural Network.

SUMMARY: Polygenic risk scores (PRSs) hold promise for early disease diagnosis and personalized treatment, but their overall discriminative power rema...

CycleGuardian: A Framework for Automatic RespiratorySound classification Based on Improved Deep clustering and Contrastive Learning

Auscultation plays a pivotal role in early respiratory and pulmonary disease diagnosis. Despite the emergence of deep learning-based methods for aut...

Machine Learning Model Predictors of Intrapleural Tissue Plasminogen Activator and DNase Failure in Pleural Infection: A Multicenter Study.

Intrapleural enzyme therapy (IET) with tissue plasminogen activator (tPA) and DNase has been shown to reduce the need for surgical intervention for c...

Detection of periodontal bone loss and periodontitis from 2D dental radiographs via machine learning and deep learning: systematic review employing APPRAISE-AI and meta-analysis.

OBJECTIVES: Periodontitis is a serious periodontal infection that damages the soft tissues and bone around teeth and is linked to systemic conditions....