Pulmonology

A computed tomography-based deep learning radiomics model for predicting the gender-age-physiology stage of patients with connective tissue disease-associated interstitial lung disease.

OBJECTIVES: To explore the feasibility of using a diagnostic model constructed with deep learning-radiomics (DLR) features extracted from chest computed tomography (CT) images to predict the gender-age-physiology (GAP) stage of patients with connective tissue disease-associated interstitial lung disease (CTD-ILD).

Non-invasive diagnosis of lung diseases via multimodal feature extraction from breathing audio and chest dynamics.

Early and accurate diagnosis of lung diseases is crucial for effective treatment. While traditional methods have limitations, audio analysis offers a promising non-invasive approach. However, existing studies often rely solely on acoustic features, neglecting valuable information contained in visual cues like chest wall dynamics. This research proposes a novel multimodal approach that integrates b...

Uncovering hepatic transcriptomic and circulating proteomic signatures in MASH: A meta-analysis and machine learning-based biomarker discovery.

BACKGROUND: Metabolic-associated steatohepatitis (MASH), the progressive form of metabolic-associated steatotic liver disease (MASLD), poses significa...

Automatic cough detection via a multi-sensor smart garment using machine learning.

Coughing behavior is associated with conditions such as sleep apnea, asthma, and chronic obstructive pulmonary disorder and can severely affect qualit...

Automated engineered-stone silicosis screening and staging using Deep Learning with X-rays.

Silicosis, a debilitating occupational lung disease caused by inhaling crystalline silica, continues to be a significant global health issue, especial...

Refining source-specific lung cancer risk assessment from PM-bound PAHs: Integrating component-based potency factors and machine learning in Ningbo, China.

The component-based potency factor approach, combined with benzo[a]pyrene (BaP) unit risk values from the World Health Organization (WHO), is commonly...

Accuracy of a deep neural network for automated pulmonary embolism detection on dedicated CT pulmonary angiograms.

PURPOSE: To assess the performance of a Deep Neural Network (DNN)-based prototype algorithm for automated PE detection on CTPA scans.

Broadening the Net: Overcoming Challenges and Embracing Novel Technologies in Lung Cancer Screening.

Lung cancer is one of the leading causes of cancer-related mortality worldwide, with most cases diagnosed at advanced stages where curative treatment ...

Artificial intelligence in predicting EGFR mutations from whole slide images in lung Cancer: A systematic review and Meta-Analysis.

BACKGROUND: Epidermal growth factor receptor (EGFR) mutations play a pivotal role in guiding targeted therapy for lung cancer, making their accurate d...

The present and future of lung cancer screening: latest evidence.

Lung cancer is the leading cause of cancer-related mortality worldwide. Early lung cancer detection improves lung cancer-related mortality and surviva...

A hybrid AI method for lung cancer classification using explainable AI techniques.

PURPOSE: The use of Artificial Intelligence (AI) methods for the analysis of CT (computed tomography) images has greatly contributed to the developmen...

Getting More from Less: Transfer Learning Improves Sleep Stage Decoding Accuracy in Peripheral Wearable Devices

Transfer learning, a technique commonly used in generative artificial intelligence, allows neural network models to bring prior knowledge to bear wh...

DLiPath: A Benchmark for the Comprehensive Assessment of Donor Liver Based on Histopathological Image Dataset

Pathologists comprehensive evaluation of donor liver biopsies provides crucial information for accepting or discarding potential grafts. However, ra...

PathGene: Benchmarking Driver Gene Mutations and Exon Prediction Using Multicenter Lung Cancer Histopathology Image Dataset

Accurately predicting gene mutations, mutation subtypes and their exons in lung cancer is critical for personalized treatment planning and prognosti...

Harnessing Foundation Models for Robust and Generalizable 6-DOF Bronchoscopy Localization

Vision-based 6-DOF bronchoscopy localization offers a promising solution for accurate and cost-effective interventional guidance. However, existing ...

Utilizing spectral detector computed tomography quantitative parameters via multimodality tumor tracking to predict occult lymph node metastasis in clinical stage 1 pure solid lung adenocarcinoma.

BACKGROUND: The presence of occult lymph node metastasis (OLNM) has significant implications for the staging, treatment and prognosis of patients with...

Opportunities and challenges in lung cancer care in the era of large language models and vision language models.

Lung cancer remains the leading cause of cancer-related deaths globally. Over the past decade, the development of artificial intelligence (AI) has sig...

Deep learning in histopathology images for prediction of oncogenic driver molecular alterations in lung cancer: a systematic review and meta-analysis.

BACKGROUND: Lung cancer (LC) is the second most diagnosed cancer and the leading cause of cancer mortality worldwide. Non-small cell lung cancer (NSCL...

Deep learning reconstruction improves computer-aided pulmonary nodule detection and measurement accuracy for ultra-low-dose chest CT.

PURPOSE: To compare the image quality and pulmonary nodule detectability and measurement accuracy between deep learning reconstruction (DLR) and hybri...

Automated diagnosis for extraction difficulty of maxillary and mandibular third molars and post-extraction complications using deep learning.

Optimal surgical methods require accurate prediction of extraction difficulty and complications. Although various automated methods related to third m...