Multi-View & Multi-Vendor Ventricular Segmentation

Cardiac MRI segmentation is a clinically interesting field that can accelerate and improve diagnostics. Targeting the capability of models towards better generalizing in unseen subsets of data that can better represent minority cohorts, greatly enhancing the lives of multiple people, cheapening the diagnostics, and making current models more resilient to unseen pathologies. In this project, our aim was to study how different architectures behave in a multiview multivendor multipathology scenario with respect to these generalization capacities and explore how postprocessing can improve the results. In addition, we also assess the computational cost that these models need to ensure that they are valid for clinical products and machines that can be reached at any clinical center

Automatic ventricle segmentation using CNNs in cardiac MRI

Cardiac magnetic resonance imaging has been proven to be a great aid tool in clinical diagnosis. Computational models arising from these images have been developed for many years by engineers, radiologists and clinicians. A first task in this process is to segment the different regions of the heart, where machine learning and, more recently, deep learning, have shown good performance. My project aims to improve the current network performance when segmenting the left-ventricular, myocardial and right-ventricular regions through (1) data augmentation, (2) data-set combination and (3) loss-function optimization, with a limited amount of computational resources. Results show improvements for all three methodologies. In addition, investing computational resources on muscular regions provides better performance in cavity regions

Cardiac Segmentation using Transfer Learning under Respiratory Motion Artifacts

Methods that are resilient to artifacts in the cardiac magnetic resonance imaging (MRI) while performing ventricle segmentation, are crucial for ensuring quality in structural and functional analysis of those tissues. While there has been significant efforts on improving the quality of the algorithms, few works have tackled the harm that the artifacts generate in the predictions. In this work, we study fine tuning of pretrained networks to improve the resilience of previous methods to these artifacts. In our proposed method, we adopted the extensive usage of data augmentations that mimic those artifacts. The results significantly improved the baseline segmentations (up to 0.06 Dice score, and 4mm Hausdorff distance improvement).Comment: accepted for the STACOM2022 workshop @ MICCAI202

Cardiac Magnetic Resonance Phase Detection Using Neural Networks

The precision of cardiac magnetic resonance segmentation is an important area to investigate clinically and has received a lot of attention from the research community for its impact on the evaluation of cardiac functions. However, the correct identification of key time frames of cardiac sequences has received significantly less attention, especially in the MR domain, despite its great importance in the correct measurement of the Ejection Fraction, a key metric in diagnostics. In this paper, we present two deep learning regression methods to automate the otherwise time-consuming annotation process, with performance within the 1–2 frame distance error and almost instant calculation over short-axis images from a public dataset. Results are presented using publicly available data

Integrating feature attribution methods into the loss function of deep learning classifiers

Feature attribution methods are typically used post-training to judge if a deep learning classifier is using meaningful concepts in an input image when making classifications. In this study, we propose using feature attribution methods to give a classifier automated feedback throughout the training process via a novel loss function. We call such a loss function, a heatmap loss function. Heatmap loss functions enable us to incentivize a model to rely on relevant sections of the input image when making classifications. Two groups of models were trained, one group with a heatmap loss function and the other using categorical cross entropy (CCE). Models trained with the heatmap loss function were capable of achieving equivalent classification accuracies on a test dataset of synthesised cardiac MRIs. Moreover, HiResCAM heatmaps suggest that these models relied to a greater extent on regions of the input image within the heart. A further experiment demonstrated how heatmap loss functions can be used to prevent deep learning classifiers from using non-causal concepts that disproportionately co-occur with certain classes when making classifications. This suggests that heatmap loss functions could be used to prevent models from learning dataset biases by directing where the model should be looking when making classifications

Semi-supervised learning of cardiac MRI using image registration

In this work, we propose a method to aid the 2-D segmentation of short-axis cardiac MRI. In particular, the deformation fields obtained during the registration are used to propagate the labels to all time frames, resulting in a weakly supervised segmentation approach that benefits from the features in unlabelled volumes along with the annotated data. Experimental results over the M\&Ms datasets show that the addition of the synthetically obtained labels to the original dataset yields promising results in the performance and improves the capability of the network to generalise to scanners from different vendors

Cardiac MRI reconstruction from undersampled k-space using double-stream IFFT and a denoising GNA-UNET pipeline

In this work, we approach the problem of cardiac Magnetic Resonance Imaging (MRI) image reconstruction from undersampled k-space. This is an inherently ill-posed problem leading to a variety of noise and aliasing artifacts if not appropriately addressed. We propose a two-step double-stream processing pipeline that first reconstructs a noisy sample from the undersampled k-space (frequency domain) using the inverse Fourier transform. Second, in the spatial domain we train a denoising GNA-UNET (enhanced by Group Normalization and Attention layers) on the noisy aliased and fully sampled image data using the Mean Square Error loss function. We achieve competitive results on the leaderboard and show that the algorithmic combination proposed is effective in high-quality MRI reconstruction from undersampled cardiac long-axis and short-axis complex k-space data

Connecting Actors With the Introduction of Mobile Technology in Health Care Practice Placements (4D Project):Protocol for a Mixed Methods Study

Background: The learning process in clinical placements for health care students is a multifaceted endeavor that engages numerous actors and stakeholders, including students, clinical tutors, link teachers, and academic assessors. Successfully navigating this complex process requires the implementation of tasks and mentorships that are synchronized with educational and clinical processes, seamlessly embedded within their respective contexts. Given the escalating number of students and the rising demand for health care services from the general population, it becomes imperative to develop additional tools that support the learning process. These tools aim to simplify day-to-day clinical practice, allowing a concentrated focus on value-based activities. This paper introduces a project funded by the European Commission that involves 5 European countries. The project’s objective is to comprehensively outline the entire process of development and ultimately implement mobile technology in practice placements. The project tackles the existing gap by constructing tailored mobile apps designed for students, teachers, tutors, and supervisors within each participating organization. This approach leverages practice-based learning, mobile technology, and technology adoption to enhance the overall educational experience. Objective: This study aims to introduce mobile technology in clinical practice placements with the goal of facilitating and enhancing practice-based learning. The objective is to improve the overall effectiveness of the process for all stakeholders involved. Methods: The “4D in the Digitalization of Learning in Practice Placement” (4D Project) will use a mixed methods research design, encompassing 3 distinct study phases: phase 1 (preliminary research), which incorporates focus groups and a scoping review, to define the problem, identify necessities, and analyze contextual factors; phase 2 (collaborative app development), which involves researchers and prospective users working together to cocreate and co-design tailored apps; and phase 3, which involves feasibility testing of these mobile apps within practice settings. Results: The study’s potential impact will primarily focus on improving communication and interaction processes, fostering connections among stakeholders in practice placements, and enhancing the assessment of training needs. The literature review and focus groups will play a crucial role in identifying barriers, facilitators, and factors supporting the integration of mobile technology in clinical education. The cocreation process of mobile learning apps will reveal the core values and needs of various stakeholders, including students, teachers, and health care professionals. This process also involves adapting and using mobile apps to meet the specific requirements of practice placements. A pilot study aimed at validating the app will test and assess mobile technology in practice placements. The study will determine results related to usability and design, learning outcomes, student engagement, communication among stakeholders, user behavior, potential issues, and compliance with regulations. Conclusions: Health care education, encompassing disciplines such as medicine, nursing, midwifery, and others, confronts evolving challenges in clinical training. Essential to addressing these challenges is bridging the gap between health care institutions and academic settings. The introduction of a new digital tool holds promise for empowering health students and mentors in effectively navigating the intricacies of the learning process.</p

Penetrance of Dilated Cardiomyopathy in Genotype-Positive Relatives

BACKGROUND Disease penetrance in genotype -positive (G+) relatives of families with dilated cardiomyopathy (DCM) and the characteristics associated with DCM onset in these individuals are unknown. OBJECTIVES This study sought to determine the penetrance of new DCM diagnosis in G+ relatives and to identify factors associated with DCM development. METHODS The authors evaluated 779 G+ patients (age 35.8 +/- 17.3 years; 459 [59%] females; 367 [47%] with variants in TTN ) without DCM followed at 25 Spanish centers. RESULTS After a median follow-up of 37.1 months (Q1 -Q3: 16.3-63.8 months), 85 individuals (10.9%) developed DCM (incidence rate of 2.9 per 100 person -years; 95% CI: 2.3-3.5 per 100 person -years). DCM penetrance and age at DCM onset was different according to underlying gene group (log -rank P = 0.015 and P <0.01, respectively). In a multivariable model excluding CMR parameters, independent predictors of DCM development were: older age (HR per 1 -year increase: 1.02; 95% CI: 1.0-1.04), an abnormal electrocardiogram (HR: 2.13; 95% CI: 1.38-3.29); presence of variants in motor sarcomeric genes (HR: 1.92; 95% CI: 1.05-3.50); lower left ventricular ejection fraction (HR per 1% increase: 0.86; 95% CI: 0.82-0.90) and larger left ventricular end -diastolic diameter (HR per 1 -mm increase: 1.10; 95% CI: 1.06-1.13). Multivariable analysis in individuals with cardiac magnetic resonance and late gadolinium enhancement assessment (n = 360, 45%) identi fied late gadolinium enhancement as an additional independent predictor of DCM development (HR: 2.52; 95% CI: 1.43-4.45). CONCLUSIONS Following a first negative screening, approximately 11% of G+ relatives developed DCM during a median follow-up of 3 years. Older age, an abnormal electrocardiogram, lower left ventricular ejection fraction, increased left ventricular end -diastolic diameter, motor sarcomeric genetic variants, and late gadolinium enhancement are associated with a higher risk of developing DCM. (J Am Coll Cardiol 2024;83:1640 -1651) (c) 2024 The Authors. Published by Elsevier on behalf of the American College of Cardiology Foundation. This is an open access article under the CC BY -NC -ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Breastfeeding during the COVID-19 pandemic: analysis of the breastmilk antibodies, neutralization capacity and microbiota profile from infected and vaccinated wome

Resumen del póster presentado a las III Jornadas Científicas PTI+ Salud Global, celebradas en el Centro de Ciencias Humanas y Sociales (CCHS), CSIC (Madrid) del 20 al 22 de noviembre de 2023.[Background] Breastmilk is considered the gold standard in infant nutrition and provides bioactive compounds to the neonate, among them antibodies and microbiota. In the context of the COVID- 19 pandemics, there were great concerns about a possible mother-to-infant transfer of SARS-CoV-2, since limited knowledge about the safety of breastfeeding after natural infection or vaccination, as well as the transfer of protective antibodies and their neutralization capacity, was available. Additionally, there are concerns about potential short- and long-term adverse effects of SARS-CoV-2 infection and vaccine-induced changes to the breastmilk microbiome composition, which contributes in shaping the early-life microbiome.[Methods] This study included 60 mothers which had a confirmed SARS-CoV-2 infection and also, 86 mothers vaccinated with mRNA-based (Comirnaty, mRNA-1273) and adenoviral-vectored vaccines (ChAdOx1 nCoV-19) were recruited and breastmilk samples were collected longitudinally from baseline up to 30 days after the second dose at seven or eight time points (depending on vaccine type). In COVID-19 lactating mothers, the presence of SARS-CoV-2 was assessed by RT-qPCR targeting the N1 region of the nucleocapsid gene and the envelope (E) gene. In both studies, the levels of SARS-CoV-2 RBD-specific IgA, IgM and IgG were determined by ELISA. The neutralization capacity was tested using pseudotyped vesicular stomatitis virus carrying either the Wuhan-Hu-1, Delta, or BA.1 Omicron spike proteins. To assess the microbiome composition, DNA from breastmilk samples was extracted and the V3-V4 region of the 16S rRNA gene was sequenced using the MiSeq system of Illumina.[Results] After SARS-CoV-2 infection, no virus-specific RNA was detected in breastmilk samples. Determination of antibody levels in mothers with confirmed SARS-CoV-2 infection showed that 82.9% (58 of 70) of milk samples were positive for at least one of the three tested antibody isotypes. Vaccination elicited also a strong induction of SARS-CoV-2-specific antibodies, which was higher in IgG when compared to COVID-19 convalescent women and was strongly increased after the 2nd dose. mRNA-based vaccines induced higher IgG and IgA levels when compared to the adenovirus- vectored vaccine, and women with previous virus exposure increased their IgG antibodies levels after the first dose to a similar level observed in vaccinated women after the second dose. When assessing the neutralization capacity, natural infection resulted in higher neutralizing titers that correlated positively with levels of SARS-CoV-2-specific immunoglobulin A in breastmilk. Breastmilk samples from COVID-19 convalescent mothers infected during the first wave (Wuhan-Hu-1 strain) neutralized less effectively Omicron BA.1 than the Wuhan-Hu-1 variant. In addition, significant differences in the capacity to produce neutralizing antibodies were observed between both mRNA- based vaccines and the adenovirus-vectored ChAdOx1 COVID-19 vaccine. First results of the analysis of the breastmilk microbiome found no significant differences in the mean diversity of species (alpha-diversity) after natural SARS-CoV-2 infection, whereas some specific bacterial groups were increased (e.g. Enterobacteriaceae).[Conclusions] Overall, our results indicate that breastmilk from naturally infected women or those vaccinated with mRNA-based vaccines contain SARS-CoV-2 neutralizing antibodies that could potentially provide protection to breastfed infants from infection.Peer reviewe