Proposal of Design and Innovation in the Creation of the Internet of Medical Things Based on the CDIO Model through the Methodology of Problem-Based Learning.

The educational framework-Conceive, Design, Implement, and Operate-is part of an international proposal to improve education in the field of engineering, emphasizing how to teach engineering comprehensively, which allows the standardization of skills in professionals as a model for teaching engineering. Moreover, problem-based learning allows students to experiment with challenging situations through cases that simulate natural contexts with their profession. The integration of these two education strategies applied to the Internet of Things (IoT) Education for Industry 4.0 has promoted the generation of teaching challenges. Our education strategy proposes the synergy between laboratory guides and the classroom with the following actions: the content of the topic is presented, followed by the presentation of an issue focused into a realistic context, with practical exercises integrating software and hardware for the deployment of the solution to be reported as a final project. Moreover, undergraduate students in the biomedical engineering area acquired new knowledge about IoT, but at the same time, they may develop skills in the field of programming and structuring different architectures to solve real-world problems. Finally, traditional models of education require new teaching initiatives in the field of biomedical engineering concerning the current challenges and needs of the labor market.

Etiology of Macular Edema Defined by Deep Learning in Optical Coherence Tomography Scans.

Purpose: To develop an automated method based on deep learning (DL) to classify macular edema (ME) from the evaluation of optical coherence tomography (OCT) scans. Methods: A total of 4230 images were obtained from data repositories of patients attended in an ophthalmology clinic in Colombia and two free open-access databases. They were annotated with four biomarkers (BMs) as intraretinal fluid, subretinal fluid, hyperreflective foci/tissue, and drusen. Then the scans were labeled as control or ocular disease among diabetic macular edema (DME), neovascular age-related macular degeneration (nAMD), and retinal vein occlusion (RVO) by two expert ophthalmologists. Our method was developed by following four consecutive phases: segmentation of BMs, the combination of BMs, feature extraction with convolutional neural networks to achieve binary classification for each disease, and, finally, multiclass classification of diseases and control images. Results: The accuracy of our model for nAMD was 97%, and for DME, RVO, and control were 94%, 93%, and 93%, respectively. Area under curve values were 0.99, 0.98, 0.96, and 0.97, respectively. The mean Cohen's kappa coefficient for the multiclass classification task was 0.84. Conclusions: The proposed DL model may identify OCT scans as normal and ME. In addition, it may classify its cause among three major exudative retinal diseases with high accuracy and reliability. Translational Relevance: Our DL approach can optimize the efficiency and timeliness of appropriate etiological diagnosis of ME, thus improving patient access and clinical decision making. It could be useful in places with a shortage of specialists and for readers that evaluate OCT scans remotely.

Multi-task deep learning for glaucoma detection from color fundus images.

Glaucoma is an eye condition that leads to loss of vision and blindness if not diagnosed in time. Diagnosis requires human experts to estimate in a limited time subtle changes in the shape of the optic disc from retinal fundus images. Deep learning methods have been satisfactory in classifying and segmenting diseases in retinal fundus images, assisting in analyzing the increasing amount of images. Model training requires extensive annotations to achieve successful generalization, which can be highly problematic given the costly expert annotations. This work aims at designing and training a novel multi-task deep learning model that leverages the similarities of related eye-fundus tasks and measurements used in glaucoma diagnosis. The model simultaneously learns different segmentation and classification tasks, thus benefiting from their similarity. The evaluation of the method in a retinal fundus glaucoma challenge dataset, including 1200 retinal fundus images from different cameras and medical centers, obtained a [Formula: see text] AUC performance compared to an [Formula: see text] obtained by the same backbone network trained to detect glaucoma. Our approach outperforms other multi-task learning models, and its performance pairs with trained experts using [Formula: see text] times fewer parameters than training each task separately. The data and the code for reproducing our results are publicly available.

Morphological analysis of T-wave in vectorcardiographic leads system by a bi-Gaussian approach in patients under effect of salbutamol.

There are several models of decomposition of the electrocardiogram (ECG). Some of these models are intended to describe the ECG signal, and others are more specific to extract the relevant information relating to individual waveform which contributes to explain the P-QRS complex. The latter approach may be particularly suitable for a portion where a morphological analysis of the ECG is of particular interest, as the cardiac repolarization segment or T-wave. This study aims: to model and detect useful patterns in the evaluation of T wave morphology, which explains the different changes in ventricular repolarization during inhalation of Salbutamol.