Utilizing a Wireless Radar Framework in Combination With Deep Learning Approaches to Evaluate Obstructive Sleep Apnea Severity in Home-Setting Environments.

Objective: Common examinations for diagnosing obstructive sleep apnea (OSA) are polysomnography (PSG) and home sleep apnea testing (HSAT). However, both PSG and HSAT require that sensors be attached to a subject, which may disturb their sleep and affect the results. Hence, in this study, we aimed to verify a wireless radar framework combined with deep learning techniques to screen for the risk of OSA in home-based environments.

Multimodal treatment combining neoadjuvant therapy, laparoscopic subtotal distal pancreatectomy and adjuvant therapy for pancreatic neck-body cancer: Case series.

Background: Pancreatic cancer involving the pancreas neck and body often invades the retroperitoneal vessels, making its radical resection challenging. Multimodal treatment strategies, including neoadjuvant therapy, surgery, and postoperative adjuvant therapy, are contributing to a paradigm shift in the treatment of pancreatic cancer. This strategy is also promising in the treatment of pancreatic neck-body cancer.

Cough recognition in pneumoconiosis patients based on a flexible patch with an embedded ACC sensor for remote monitoring.

Background: Many respiratory diseases such as pneumoconiosis require to close monitor the symptoms such as abnormal respiration and cough. This study introduces an automated, nonintrusive method for detecting cough events in clinical settings using a flexible chest patch with tri-axial acceleration sensors.

Methods: Twenty-five young healthy persons (hereinafter referred to as healthy adults) and twenty-five clinically diagnosed pneumoconiosis patients (hereinafter referred to as patients) participated in the experiment by wearing a flexible chest patch with an embedded ACC sensor.

Deep learning based decision-making and outcome prediction for adolescent idiopathic scoliosis patients with posterior surgery.

With the emergence of numerous classifications, surgical treatment for adolescent idiopathic scoliosis (AIS) can be guided more effectively. However, surgical decision-making and optimal strategies still lack standardization and personalized customization. Our study aims to devise proper deep learning (DL) models that incorporate key factors influencing surgical outcomes on the coronal plane in AIS patients to facilitate surgical decision-making and predict surgical results for AIS patients.