Pulmonology

Investigating Quantum Feature Maps in Quantum Support Vector Machines for Lung Cancer Classification

In recent years, quantum machine learning has emerged as a promising intersection between quantum physics and artificial intelligence, particularly in domains requiring advanced pattern recognition such as healthcare. This study investigates the effectiveness of Quantum Support Vector Machines (QSVM), which leverage quantum mechanical phenomena like superposition and entanglement to construct hi...

Boolean-network simplification and rule fitting to unravel chemotherapy resistance in non-small cell lung cancer

Boolean networks are powerful frameworks for capturing the logic of gene-regulatory circuits, yet their combinatorial explosion hampers exhaustive analyses. Here, we present a systematic reduction of a 31-node Boolean model that describes cisplatin- and pemetrexed-resistance in non-small-cell lung cancer to a compact 9-node core that exactly reproduces the original attractor landscape. The strea...

Adaptive Differential Denoising for Respiratory Sounds Classification

Automated respiratory sound classification faces practical challenges from background noise and insufficient denoising in existing systems. We pro...

Highlights 2024.

publishes research, technical developments, and reviews related to cardiac, vascular, and thoracic imaging. The current review article, led by the t...

Rapid and Noninvasive Early Detection of Lung Cancer by Integration of Machine Learning and Salivary Metabolic Fingerprints Using MS LOC Platform: A Large-Scale Multicenter Study.

Most lung cancer (LC) patients are diagnosed at advanced stages due to the lack of effective screening tools. This multicenter study analyzes 1043 sal...

An anatomically enhanced and clinically validated framework for lung abnormality classification using deep features and KL divergence.

Detecting lung abnormalities via chest X-rays is challenging due to understated tissue variations often ignored by traditional methods. Augmentation t...

Comparing efficiency of an attention-based deep learning network with contemporary radiological workflow for pulmonary embolism detection on CTPA: A retrospective study.

RATIONAL AND OBJECTIVES: Pulmonary embolism (PE) is the third most fatal cardiovascular disease in the United States. Currently, Computed Tomography P...

Dental practitioners versus artificial intelligence software in assessing alveolar bone loss using intraoral radiographs.

OBJECTIVES: Integrating artificial intelligence (AI) in the dental field can potentially enhance the efficiency of dental care. However, few studies h...

Explainable PET-based intratumoral and peritumoral machine learning model for predicting visceral pleural invasion in clinical-stage IA non-small cell lung cancer: A two-center study.

AIM: The aim of this study was to develop a PET-based machine learning model for predicting visceral pleural invasion (VPI) in patients with clinical ...

Development and External Validation of a Detection Model to Retrospectively Identify Patients With Acute Respiratory Distress Syndrome.

OBJECTIVE: The aim of this study was to develop and externally validate a machine-learning model that retrospectively identifies patients with acute r...

Transforming pulmonary health care: the role of artificial intelligence in diagnosis and treatment.

INTRODUCTION: Respiratory diseases like pneumonia, asthma, and COPD are major global health concerns, significantly impacting morbidity and mortality ...

Pediatric chest X-ray diagnosis using neuromorphic models.

This research presents an innovative neuromorphic method utilizing Spiking Neural Networks (SNNs) to analyze pediatric chest X-rays (PediCXR) to ident...

BigLSTM: Recurrent neural network for the treatment of anomalous temporal signals. Application in the prediction of endotracheal obstruction in COVID-19 patients in the intensive care unit.

Real-world applications, particularly in the medical field, often handle irregular time signals (ITS) with non-uniform intervals between measurements....

A magnetic resonance imaging (MRI)-based deep learning radiomics model predicts recurrence-free survival in lung cancer patients after surgical resection of brain metastases.

AIM: To develop and validate a magnetic resonance imaging (MRI)-based deep learning radiomics model (DLRM) to predict recurrence-free survival (RFS) i...

Whole slide image-level classification of malignant effusion cytology using clustering-constrained attention multiple instance learning.

BACKGROUND: Cytological diagnosis of pleural effusion plays an important role in the early detection and diagnosis of lung cancers. Recently, attempts...

CellOMaps: A compact representation for robust classification of lung adenocarcinoma growth patterns.

Lung adenocarcinoma (LUAD) is a morphologically heterogeneous disease, characterized by five primary histological growth patterns. The classification ...

Beyond accuracy: The need for explainable AI in biomedical voice technology.

Speech and voice have emerged as valuable non-invasive biomarkers for detecting and monitoring a range of medical conditions, from neurodegenerative a...

Machine Learning Accurately Predicts Need for Critical Care Support in Patients Admitted to Hospital for Community-Acquired Pneumonia.

OBJECTIVES: Hospitalized community-acquired pneumonia (CAP) patients are admitted for ventilation, vasopressors, and renal replacement therapy (RRT). ...

Machine Learning Accurately Predicts Need for Critical Care Support in Patients Admitted to Hospital for Community-Acquired Pneumonia.

OBJECTIVES: Hospitalized community-acquired pneumonia (CAP) patients are admitted for ventilation, vasopressors, and renal replacement therapy (RRT). ...

Detection of COVID-19, lung opacity, and viral pneumonia via X-ray using machine learning and deep learning.

The COVID-19 pandemic has significantly strained healthcare systems, highlighting the need for early diagnosis to isolate positive cases and prevent t...