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A deep learning-based model to estimate pulmonary function from chest x-rays: multi-institutional model development and validation study in Japan.
Lancet Digit Health
August 2024
Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan.
Background: Chest x-ray is a basic, cost-effective, and widely available imaging method that is used for static assessments of organic diseases and anatomical abnormalities, but its ability to estimate dynamic measurements such as pulmonary function is unknown. We aimed to estimate two major pulmonary functions from chest x-rays.
Methods: In this retrospective model development and validation study, we trained, validated, and externally tested a deep learning-based artificial intelligence (AI) model to estimate forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV) from chest x-rays.
Development and Evaluation of a Quantitative Systems Pharmacology Model for Mechanism Interpretation and Efficacy Prediction of Atezolizumab in Combination with Carboplatin and Nab-Paclitaxel in Patients with Non-Small-Cell Lung Cancer.
Pharmaceuticals (Basel)
February 2024
Department of Pharmacy, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China.
Article Synopsis
- Immunotherapy helps treat people with a type of lung cancer called non-small-cell lung cancer (NSCLC), but doctors are exploring combining it with chemotherapy to improve results.
- Researchers created a special computer model to study how a mix of three drugs works together for treating this cancer, which helps predict how well patients will respond to treatment.
- The model also found that certain types of immune cells in tumors can show how likely patients are to benefit from this combination therapy, making it easier for doctors to create personalized treatment plans.
Enhancing squat movement classification performance with a gated long-short term memory with transformer network model.
Sports Biomech
February 2024
Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China.
Bodyweight squat is one of the basic sports training exercises. Automatic classification of aberrant squat movements can guide safe and effective bodyweight squat exercise in sports training. This study presents a novel gated long-short term memory with transformer network (GLTN) model for the classification of bodyweight squat movements.
View Article and Find Full Text PDFExercise as an Airway Clearance Technique in people with Cystic Fibrosis (ExACT-CF): rationale and study protocol for a randomised pilot trial.
NIHR Open Res
December 2022
School of Sport, Health and Exercise Science, University of Portsmouth, Portsmouth, UK.
Background: Chest physiotherapy is an established cornerstone of care for people with cystic fibrosis (pwCF), but is often burdensome. Guidelines recommend at least one chest physiotherapy session daily, using various airway clearance techniques (ACTs). Exercise (with huffs and coughs) may offer an alternative ACT, however the willingness of pwCF to be randomised into a trial needs testing.
View Article and Find Full Text PDFA computationally-inexpensive strategy in CT image data augmentation for robust deep learning classification in the early stages of an outbreak.
Biomed Phys Eng Express
July 2023
Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Birmingham B15 2TT, United Kingdom.
Coronavirus disease 2019 (COVID-19) has spread globally for over three years, and chest computed tomography (CT) has been used to diagnose COVID-19 and identify lung damage in COVID-19 patients. Given its widespread, CT will remain a common diagnostic tool in future pandemics, but its effectiveness at the beginning of any pandemic will depend strongly on the ability to classify CT scans quickly and correctly when only limited resources are available, as it will happen inevitably again in future pandemics. Here, we resort into the transfer learning procedure and limited hyperparameters to use as few computing resources as possible for COVID-19 CT images classification.
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