All clinical trials

Previous prospective randomized controlled study demonstrated higher accuracy rate of diagnosing early gastric cancers by Magnifying image-enhanced endoscopy than conventional white-light endoscopy. Nevertheless, it is difficult to differentiate early gastric cancer from noncancerous lesions for beginner. we developed a new computer-aided system to assist endoscopists in identifying early gastric cancers in magnifying optical enhancement images.

Laparoscopic cholecystectomy is the treatment of choice for gall bladder stone disease. Difficult laparoscopic cholecystectomy (LC) is the most common "difficult" surgical procedure performed today, which possesses the potential to place the patient at significant operative risk.The aim of this study was to put a scoring system to avoid difficult laparoscopic cholecystectomy.

Patients from 60 to 75 years old diagnosed with cerebral small vessel disease with no history of symptomatic stroke, brain tumor, traumatic brain injury, seizures and neurodegenerative or mental disorder will undergo overnight leg actigraphy and cardiorespiratory monitoring. Those of them with apnea/hypopnea index under 5 will be enrolled. Brain MRI and cognitive assessment will be performed at baseline and in 1-year follow-up, sleep quality will be assessed at baseline with self-reported questionnaires. Progression of cerebral small vessel disease markers and cognitive dysfunction will be compared between patients with high periodic limb movement index (the number of periodic limb movement ≥ 15 per hour of sleep) and controls (periodic limb movement index < 15/h).

During patient follow up the investigators noticed the occasional occurence of a lung herniation after uniportal Video assisted thoracoscopic surgery (VATS). Most of These patients were asymptomatic and didn't require any kind of treatment. To analyse the incidence of postoperative lung herniation after uniportal and the presence of symptoms the investigators decided to review every postoperative CT scan from January 2016 until March 2019 to radiologically identify postoperative lung herniations. Afterwards the investigators will contact the respective patient to ask for a follow-up physical examination and symptom evaluation (after consent was signed).

All hospitalised patients with COVID-19 who have positive RT-PCR for SARS-COV-2 will be included in the study. The patients will be divided into two groups, as diabetics and non-diabetics. The COVID-19 patients' medical records will be evaluated and compared in terms of the duration of hospitalization, the presence of lung involvement in Computerised Tomography, the need for intensive care unit and mortality rates in patients with and without diabetes.

Diabetes represents one of the 10 leading causes of death in the world and concerns 5% of the French population (> 3.3 million patients). About 30% of diabetic patients will develop heart failure. The specific and early identification of diabetic cardiomyopathy at a subclinical stage (asymptomatic patients with normal LVEF) will thus make it possible to predict the risk of the onset of heart failure and to strengthen their monitoring and further adapt their treatment.

The aim of this study is to assess the diagnostic accuracy of CMR feature-tracking (FT) at rest & during stress with low dose dobutamine (LDD) in the evaluation of viability in ischemic cardiomyopathy (ICM) patients and compare it with delayed gadolinium enhancement (DGE).

The association between myocardial inflammation (clinically represented by acute myocarditis episodes) and the later development of an arrhythmogenic cardiomyopathy is widely elusive.

The project leaders plan to create a clinical decision support (CDS) system by programming a self-learning software to analyze the cardiotocography (CTG) traces in the - already existing - database from the maternity department of the Inselspital Berne. The project leaders will process and analyze all clinical outcomes of the estimated 10000-15000 eligible patient records. CSEM will design, develop, and validate several AI architectures with the intend to create the CDS system. The AI would learn to assist on this task by training machine learning (ML) algorithms. The main purpose of the AI-CDS will be to determine the best fetal extraction moment during labor, based on a self-learning approach, as a "superhuman" support tool for obstetricians in decision making during labor.

The aim of the study will be to determine the epidemiological and clinical features of COVID-19 cases, immunological and virological courses, interaction with nutritional status, and response to treatment for COVID-19 patients admitted to treatment centers in Ethiopia. Methods: This multi-site cohort enrolls, patients with confirmed COVID-19 infection admitted to treatment centers will be enrolled irrespective of their symptoms and followed up for 12 months. Baseline epidemiological, clinical, laboratory and imaging data will be collected from treatment records, interviews, physical measurements and biological samples. Endline data involves treatment and prognostic outcomes to be measured using different biomarkers and clinical parameters, The patients will be followed up in the selected treatment centers for COVID-19 infection. For all data collected both descriptive and multivariable analyses will be performed to isolated determinants of the treatment outcome and prognosis to generate relevant information for informed prevention and case management.