Pulmonology

ToxMPNN: A deep learning model for small molecule toxicity prediction.

Machine learning (ML) has shown a great promise in predicting toxicity of small molecules. However, the availability of data for such predictions is often limited. Because of the unsatisfactory performance of models trained on a single toxicity endpoint, we collected toxic small molecules with multiple toxicity endpoints from previous study. The dataset comprises 27 toxic endpoints categorized int...

Multicentre validation of a machine learning model for predicting respiratory failure after noncardiac surgery.

BACKGROUND: Postoperative respiratory failure is a serious complication that could benefit from early accurate identification of high-risk patients. We developed and validated a machine learning model to predict postoperative respiratory failure, defined as prolonged (>48 h) mechanical ventilation or reintubation after surgery.

Deep match: A zero-shot framework for improved fiducial-free respiratory motion tracking.

BACKGROUND AND PURPOSE: Motion management is essential to reduce normal tissue exposure and maintain adequate tumor dose in lung stereotactic body rad...

Biochemical implications of robotic surgery: a new frontier in the operating room.

Robotic surgery represents a milestone in surgical procedures, offering advantages such as less invasive methods, elimination of tremors, scaled motio...

Improving Computer-Aided Thoracic Disease Diagnosis through Comparative Analysis Using Chest X-ray Images Taken at Different Times.

Medical professionals in thoracic medicine routinely analyze chest X-ray images, often comparing pairs of images taken at different times to detect le...

Clinical utilization of artificial intelligence in predicting therapeutic efficacy in pulmonary tuberculosis.

Traditional methods for monitoring pulmonary tuberculosis (PTB) treatment efficacy lack sensitivity, prompting the exploration of artificial intellige...

Ultra-low dose chest CT with silver filter and deep learning reconstruction significantly reduces radiation dose and retains quantitative information in the investigation and monitoring of lymphangioleiomyomatosis (LAM).

PURPOSE: Frequent CT scans to quantify lung involvement in cystic lung disease increases radiation exposure. Beam shaping energy filters can optimize ...

An Accelerometer-Based Wearable Patch for Robust Respiratory Rate and Wheeze Detection Using Deep Learning.

Wheezing is a critical indicator of various respiratory conditions, including asthma and chronic obstructive pulmonary disease (COPD). Current diagnos...

Prospective evaluation of deep learning image reconstruction for Lung-RADS and automatic nodule volumetry on ultralow-dose chest CT.

PURPOSE: To prospectively evaluate whether Lung-RADS classification and volumetric nodule assessment were feasible with ultralow-dose (ULD) chest CT s...

Robot-assisted thoracic surgery for benign tumors at the cervicothoracic junction: a propensity-matched study.

This study aimed to assess the feasibility and safety of robot-assisted thoracic surgery (RATS) for resecting benign tumors of the cervicothoracic jun...

Elevated Plasma CXCL8 Concentrations in Significant Fibrosis but Not in Subclinical Rejection After Adult Liver Transplantation.

BACKGROUND: The noninvasive detection of subclinical graft injury including subclinical T cell-mediated rejection (subTCMR) is one of the unresolved c...

The importance of data in Pulmonary Arterial Hypertension: from international registries to Machine Learning.

Real-world registries have been critical to building the scientific knowledge of rare diseases, including Pulmonary Arterial Hypertension (PAH). In th...

Machine learning assisted methods for the identification of low toxicity inhibitors of Enoyl-Acyl Carrier Protein Reductase (InhA).

Tuberculosis (TB) is one of the life-threatening infectious diseases with prehistoric origins and occurs in almost all habitable parts of the world. T...

Fully automated deep learning model for detecting proximity of mandibular third molar root to inferior alveolar canal using panoramic radiographs.

OBJECTIVE: This study endeavored to develop a novel, fully automated deep-learning model to determine the topographic relationship between mandibular ...

Intelligently Quantifying the Entire Irregular Dental Structure.

Quantitative analysis of irregular anatomical structures is crucial in oral medicine, but clinicians often typically measure only several representati...

Application of artificial intelligence in diagnosis of pulmonary tuberculosis.

A deep learning-based framework (Co-ReTr) for auto-segmentation of non-small cell-lung cancer in computed tomography images.

PURPOSE: Deep learning-based auto-segmentation algorithms can improve clinical workflow by defining accurate regions of interest while reducing manual...

Artificial intelligence-enabled optimization of Fe/Zn@biochar photocatalyst for 2,6-dichlorophenol removal from petrochemical wastewater: A techno-economic perspective.

While numerous studies have addressed the photocatalytic degradation of 2,6-dichlorophenol (2,6-DCP) in wastewater, an existing research gap pertains ...

COPD stage detection: leveraging the auto-metric graph neural network with inspiratory and expiratory chest CT images.

Chronic obstructive pulmonary disease (COPD) is a common lung disease that can lead to restricted airflow and respiratory problems, causing a signific...

Automated Real-Time Detection of Lung Sliding Using Artificial Intelligence: A Prospective Diagnostic Accuracy Study.

BACKGROUND: Rapid evaluation for pneumothorax is a common clinical priority. Although lung ultrasound (LUS) often is used to assess for pneumothorax, ...