Diabetic retinopathy detection through convolutional neural networks with synaptic metaplasticity.

Background And Objectives: Diabetic retinopathy is a type of diabetes that causes vascular changes that can lead to blindness. The ravages of this disease cannot be reversed, so early detection is essential. This work presents an automated method for early detection of this disease using fundus colored images.

A DCP-based Method for Improving Laparoscopic Images.

Laparoscopy is an invasive surgical technique performed in abdominal surgery that provides faster recovery than conventional open surgeries. It requires to introduce a camera to observe the surgical maneuvers. However, during this intervention, the quality of the image may be reduced due to the creation of water vapor and carbon dioxide inside the pelvic-abdominal cavity.

Empowerment of Patients with Hypertension through BPM, IoT and Remote Sensing.

Hypertension affects one in five adults worldwide. Healthcare processes require interdisciplinary cooperation and coordination between medical teams, clinical processes, and patients. The lack of patients' empowerment and adherence to treatment makes necessary to integrate patients, data collecting devices and clinical processes.

Monitoring-Based Model for Personalizing the Clinical Process of Crohn's Disease.

Crohn's disease is a chronic pathology belonging to the group of inflammatory bowel diseases. Patients suffering from Crohn's disease must be supervised by a medical specialist for the rest of their lives; furthermore, each patient has its own characteristics and is affected by the disease in a different way, so health recommendations and treatments cannot be generalized and should be individualized for a specific patient. To achieve this personalization in a cost-effective way using technology, we propose a model based on different information flows: control, personalization, and monitoring.