A rule-based method to automatically locate lumbar vertebral bodies on MRI images.

Background: Segmentation is a critical process in medical image interpretation. It is also essential for preparing training datasets for machine learning (ML)-based solutions. Despite technological advancements, achieving fully automatic segmentation is still challenging.

Evaluation of the ODISEA APP for improving a STEMI regional network.

Background: The use of technological innovations in ST elevation myocardial infarction (STEMI) care networks has been shown to be effective in improving information flow and coordination, and thus reducing the time to reperfusion. We developed a smartphone application called ODISEA to improve our STEMI care network and evaluated the results of its use.

Method: Quasi-experimental study that compared the outcomes of STEMI suspected patients with an alert and indication for transfer to a cath lab during a previous period and a period in which the ODISEA APP was used.

Limitations and solutions of low cost virtual reality mirror therapy for post-stroke patients.

Mirror therapy is applied to reduce phantom pain and as a rehabilitation technique in post-stroke patients. Using Virtual Reality and head-mounted displays this therapy can be performed in virtual scenarios. However, for its efficient use in clinical settings, some hardware limitations need to be solved.

Design and deployment of ODISEA, an application for the myOcarDial infarction SafEtytrAnsfer of patients.

Background: Rapid primary angioplasty is the most effective reperfusion strategy for acute ST-elevation myocardial infarction (STEMI) patients. Since not all hospitals have a catheterization laboratory to perform this intervention, adequate coordination of all medical professionals involved in the management of STEMI patients from the emergency room to the hospital catheterization laboratory is necessary.

Objective: Present the design and deployment of ODISEA (acronym of myOcarDial Infarction SafEtytrAnsfer), a web-based environment plus an application created to complement and support the transfer and management of STEMI patients from the first medical contact to the catheterization laboratory where the primary angioplasty will be carried out.

Attending pediatric acutely ill patients at home: families' socioeconomic characterization, expectations, and experiences.

Background: SJD a Casa is an acute pediatric hospital-at-home program that was initiated in 2019. For a thorough understanding of acute pediatric homecare programs, an analysis of all related factors, including the medical, social, and economic aspects as well as the family's experience, is essential. However, no previous study has attempted a comprehensive evaluation of this topic in relation to a complex program such as ours (in terms of the diseases and treatments offered).

Asynchronous online learning as a key tool to adapt to new educational needs in radiology during the COVID-19 pandemic.

The risk of contagion and the lockdown caused by the COVID-19 pandemic forced a change in teaching methodologies in radiology. New knowledge about the disease that was being acquired on a daily basis needed to be rapidly spread worldwide, but the restrictions imposed made it difficult to share this information. This paper describes the methodology applied to design and launch a practice-based course on chest X-ray suggestive of COVID-19 right after the pandemic started, and aims to determine whether asynchronous online learning tools for radiology education are useful and acceptable to general practitioners and other medical personnel during a pandemic.

Using a route planner to optimize door-to-door visits for a pediatric home-hospitalization program: Feasibility study.

Background: Hospital Sant Joan de Déu (Barcelona) initiated a pediatric acute home-hospitalization program. Due to high patient turnover and the health staff's lack of planning training, daily scheduling was a time-consuming task. Home-hospitalization planning is a vehicle routing problem that can be solved with a technological solution.

Deep learning for the quality control of thermoforming food packages.

Quality control is a key process designed to ensure that only products satisfying the defined quality requirements reach the end consumer or the next step in a production line. In the food industry, in the packaging step, there are many products that are still evaluated by human operators. To automate the process and improve efficiency and effectiveness, computer vision and artificial intelligence techniques can be applied.

A Method to Compensate for the Errors Caused by Temperature in Structured-Light 3D Cameras.

Although low cost red-green-blue-depth (RGB-D) cameras are factory calibrated, to meet the accuracy requirements needed in many industrial applications proper calibration strategies have to be applied. Generally, these strategies do not consider the effect of temperature on the camera measurements. The aim of this paper is to evaluate this effect considering an Orbbec Astra camera.

A Serious Game on the First-Aid Procedure in Choking Scenarios: Design and Evaluation Study.

Background: Choking is one of the causes of unintentional injury death. Gaining the knowledge of the first-aid procedure that has to be applied in case of choking can increase the chances of survival of persons with choking. Serious games can be a good channel for educating people about choking scenarios and the actions to be taken to save the persons with choking.

Bariatric Surgery-Induced Changes in Intima-Media Thickness and Cardiovascular Risk Factors in Class 3 Obesity: A 3-Year Follow-Up Study.

Objective: The impact of weight loss induced by bariatric surgery (BS) and nonsurgical approaches on cardiovascular risk factors (CVRFs) has not been fully elucidated. We assessed the effects of BS and a nonsurgical approach on carotid intima-media thickness (CIMT) and CVRFs in participants with class 3 obesity.

Methods: A total of 87 participants with obesity (59 women; 46 [37-52] years old; BMI, 43 [40-47]) and 75 controls were recruited; 21 (25%) participants with obesity underwent BS.

Starviewer and its comparison with other open-source DICOM viewers using a novel hierarchical evaluation framework.

Methods: The aim of the paper is twofold. First, we present Starviewer, a DICOM viewer developed in C++ with a core component built on top of open-source libraries. The viewer supports extensions that implement functionalities and front-ends for specific use cases.

Introducing Online Continuing Education in Radiology for General Practitioners.

The aim of this study was to determine whether e-learning as a new teaching methodology was acceptable for general practitioners in continuous education courses of radiology. Generally, these courses are face-to-face with the corresponding time and place limitations. To overcome these limitations, we transformed one of these courses to an online one evaluating its acceptance.

IVET, an Interactive Veterinary Education Tool.

The use of e-learning tools for medical teaching is a common practice, but similar tools do not exist for veterinary teaching. In this article, we present a fully web-based e-learning platform, denoted Interactive Veterinary Education Tool (IVET), which is designed to support teaching and learning in veterinary science. To make content creation easier, it provides theory, exercise, and image editors with functionalities to prepare exercises and theoretical content including 2-dimensional (2D) images, 3-dimensional (3D) models, and Digital Imaging and Communications in Medicine (DICOM) files, which can be manipulated by the users.

Novel Brain Complexity Measures Based on Information Theory.

Brain networks are widely used models to understand the topology and organization of the brain. These networks can be represented by a graph, where nodes correspond to brain regions and edges to structural or functional connections. Several measures have been proposed to describe the topological features of these networks, but unfortunately, it is still unclear which measures give the best representation of the brain.

How the gender of a victim character in a virtual scenario created to learn CPR protocol affects student nurses' performance.

Background And Objective: Virtual simulations recreate scenarios where student nurses can practice procedures in a safe and supervised manner and with no risk to the patient. Virtual scenarios include digital characters that reproduce human actions. Generally, these characters are modeled as males and restricted roles are assigned to females.

Brain parcellation based on information theory.

Background And Objective: In computational neuroimaging, brain parcellation methods subdivide the brain into individual regions that can be used to build a network to study its structure and function. Using anatomical or functional connectivity, hierarchical clustering methods aim to offer a meaningful parcellation of the brain at each level of granularity. However, some of these methods have been only applied to small regions and strongly depend on the similarity measure used to merge regions.

A new e-learning platform for radiology education (RadEd).

One of the key elements of e-learning platforms is the content provided to the students. Content creation is a time demanding task that requires teachers to prepare material taking into account that it will be accessed on-line. Moreover, the teacher is restricted by the functionalities provided by the e-learning platforms.

Using a serious game to complement CPR instruction in a nurse faculty.

Cardiopulmonary resuscitation (CPR) is a first aid key survival technique used to stimulate breathing and keep blood flowing to the heart. Its effective administration can significantly increase the chances of survival for victims of cardiac arrest. LISSA is a serious game designed to complement CPR teaching and also to refresh CPR skills in an enjoyable way.

Information theory-based automatic multimodal transfer function design.

In this paper, we present a new framework for multimodal volume visualization that combines several information-theoretic strategies to define both colors and opacities of the multimodal transfer function. To the best of our knowledge, this is the first fully automatic scheme to visualize multimodal data. To define the fused color, we set an information channel between two registered input datasets, and afterward, we compute the informativeness associated with the respective intensity bins.

Increased corticospinal tract fractional anisotropy can discriminate stroke onset within the first 4.5 hours.

Background And Purpose: The role of diffusion tensor imaging in determining stroke age remains unclear. We tested the ability of diffusion tensor imaging metrics to discriminate ischemic stroke <4.5 hours of onset.

Information-theoretic approach for automated white matter fiber tracts reconstruction.

Fiber tracking is the most popular technique for creating white matter connectivity maps from diffusion tensor imaging (DTI). This approach requires a seeding process which is challenging because it is not clear how and where the seeds have to be placed. On the other hand, to enhance the interpretation of fiber maps, segmentation and clustering techniques are applied to organize fibers into anatomical structures.

Multimodal Data Fusion Based on Mutual Information.

Multimodal visualization aims at fusing different data sets so that the resulting combination provides more information and understanding to the user. To achieve this aim, we propose a new information-theoretic approach that automatically selects the most informative voxels from two volume data sets. Our fusion criteria are based on the information channel created between the two input data sets that permit us to quantify the information associated with each intensity value.