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. METHODS: A bio-inspired approach is proposed on synaptic metaplasticity in convolutional neural networks. This biological phenomenon is known to directly interfere in both learning and memory by reinforcing less common occurrences during the learning process. Synaptic metaplasticity has been included in the backpropagation stage of a convolution operation for every convolutional layer. RESULTS: The proposed method has been evaluated by using a public small diabetic retinopathy dataset from Kaggle with four award-winning convolutional neural network architectures. Results show that convolutional neural network architectures including synaptic metaplasticity improve both learning rate and accuracy. Furthermore, obtained results outperform other methods in current literature, even using smaller datasets for training. Best results have been obtained for the InceptionV3 architecture with synaptic metaplasticity with a 95.56% accuracy, 94.24% F1-score, 98.9% precision and 90% recall, using 3662 images for training. CONCLUSIONS: Convolutional neural networks with synaptic metaplasticity are suitable for early detection of diabetic retinopathy due to their fast convergence rate, training simplicity and high performance.

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. This phenomenon produces a nebulous image that causes interruptions during the surgical intervention. Removing this nebulous effect is a key factor to improve the vision of the surgeon. In this study, we have used a method based on the dark channel prior to remove the haze in video frames of laparoscopic surgeries to provide better quality images. The results have been positively evaluated by specialists using real video frames of laparoscopic surgeries, thus demonstrating that this method can be effective in improving the quality of the images without losing any detail of the original image.

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. For this reason, in this paper we propose a model based on Business Process Management paradigm, together with a group of technologies, techniques and IT principles which increase the benefits of the paradigm. To achieve the proposed model, the clinical process of the hypertension is analyzed with the objective of detecting weaknesses and improving the process. Once the process is analyzed, an architecture that joins health devices and environmental sensors, together with an information system, has been developed. To test the architecture, a web system connected with health monitors and environment sensors, and with a mobile app have been implemented.

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. As a result of the model and to perform a functional validation, an architecture based on services and a prototype of the system has been defined. In this prototype, a set of different devices and technologies to monitor variables from patients and their environment has been integrated. Artificial intelligence algorithms are also included to reduce the workload related to the review and analysis of the information gathered. Due to the continuous and automated monitoring of the Crohn's patient, this proposal can help in the personalization of the Crohn's disease clinical process.