View clinical trials related to Chest--Diseases.
Filter by:- To evaluate the effectiveness of LDCT in detecting early-stage lung cancer in patients with chronic lung conditions compared to standard chest x-rays. - To improve lung cancer outcomes through optimized use of radiological technologies for early detection in high-risk patients with pre-existing lung conditions.
The objective of this study is to determine the pattern and outcome of respiratory diseases in adults patients admitted to the Department of Chest Diseases at Assiut University Hospital.
The goal of this observational study is to develop a decision support system in patients presenting with chest pain in the prehospital setting. The main question it aims to answer is: • Performance of a machine learning based model for decision support of patients in contact with emergency medical services due to chest pain Participants will be asked to: - respond to questions asked by the clinician at the scene regarding previous known risk factors and pain characteristics - consent to the collection of routinely available data from medical records - consent of taking one blood sample capillary or venous (if perifer catheter is placed for standard care reasons) troponin and glucose which is measured at the scene, disposed, and the result is entered in the clinical report form.
Artificial intelligence and machine learning have the potential to transform the practice of radiology, but real-world application of machine learning algorithms in clinical settings has been limited. An area in which machine learning could be applied to radiology is through the prioritization of unread studies in a radiologist's worklist. This project proposes a framework for integration and clinical validation of a machine learning algorithm that can accurately distinguish between normal and abnormal chest radiographs. Machine learning triage will be compared with traditional methods of study triage in a prospective controlled clinical trial. The investigators hypothesize that machine learning classification and prioritization of studies will result in quicker interpretation of abnormal studies. This has the potential to reduce time to initiation of appropriate clinical management in patients with critical findings. This project aims to provide a thoughtful and reproducible framework for bringing machine learning into clinical practice, potentially benefiting other areas of radiology and medicine more broadly.
It is planned to integrate various services based on computer vision technologies for analysis of the certain type of x-ray study into Moscow Unified Radiological Information Service (hereinafter referred to as URIS). As a result of using computer vision-based services, it is expected: 1. Reducing the number of false negative and false positive diagnoses; 2. Reducing the time between conducting a study and obtaining a report by the referring physician; 3. Increasing the average number of radiology reports provided by a radiologist per shift.
The purpose of this study is to assess if there is decrease in cough during flexible bronchoscopy and endobronchial ultrasound when different modes of lidocaine administration are used. The modes of administration being evaluated are topical, nebulized and atomized.
Noninvasive ventilation is increasingly used method of respiratory management in both the emergency room and critical care. Noninvasive ventilation delivers mechanically assisted breaths without the placement of an artificial airway and has become an important mechanism of ventilator support inside and outside the intensive care unit. Noninvasive ventilation is further subdivided into negative pressure ventilation which is the iron lung, first used in 1928 and the Hayek oscillator, is a more recently designed to provide negative pressure during inspiration and positive pressure during expiration. Noninvasive positive pressure ventilation can be used as continuous positive airway pressure or bi-level positive airway pressure.