Showing papers by "Gemma Piella published in 2021"

Nonlinear interaction between APOE ε4 allele load and age in the hippocampal surface of cognitively intact individuals

Abstract: The e4 allele of the gene Apolipoprotein E is the major genetic risk factor for Alzheimer's Disease. APOE e4 has been associated with changes in brain structure in cognitively impaired and unimpaired subjects, including atrophy of the hippocampus, which is one of the brain structures that is early affected by AD. In this work we analyzed the impact of APOE e4 gene dose and its association with age, on hippocampal shape assessed with multivariate surface analysis, in a e4-enriched cohort of n = 479 cognitively healthy individuals. Furthermore, we sought to replicate our findings on an independent dataset of n = 969 individuals covering the entire AD spectrum. We segmented the hippocampus of the subjects with a multi-atlas-based approach, obtaining high-dimensional meshes that can be analyzed in a multivariate way. We analyzed the effects of different factors including APOE, sex, and age (in both cohorts) as well as clinical diagnosis on the local 3D hippocampal surface changes. We found specific regions on the hippocampal surface where the effect is modulated by significant APOE e4 linear and quadratic interactions with age. We compared between APOE and diagnosis effects from both cohorts, finding similarities between APOE e4 and AD effects on specific regions, and suggesting that age may modulate the effect of APOE e4 and AD in a similar way.

La integración de la inteligencia artificial en el abordaje clínico del paciente: enfoque en la imagen cardiaca

Abstract: Resumen La imagen cardiaca es un componente crucial en el abordaje de los pacientes cardiacos, y como tal influye en multiples partes interrelacionadas del flujo de trabajo clinico: el contacto medico-paciente, la adquisicion de imagen, el preprocesamiento y posprocesamiento de imagenes, los informes de estudios, el diagnostico y el pronostico, las intervenciones medicas y, por ultimo, el desarrollo del conocimiento a traves de la investigacion clinica. La incesante infiltracion de la inteligencia artificial en cardiologia pone de manifiesto que, usada apropiadamente, influira y puede mejorar —a traves de la automatizacion, la estandarizacion y la integracion de datos— todos los componentes del flujo de trabajo clinico. El objetivo de esta revision es presentar una vision holistica de como se integra la inteligencia artificial en el abordaje clinico del paciente, con especial foco en la imagen cardiaca, pero aplicable a toda la gestion de informacion, y discutir las barreras actuales que aun deben superarse para su implementacion generalizada.

Identifying causative mechanisms linking early-life stress to psycho-cardio-metabolic multi-morbidity: The EarlyCause project

Abstract: Introduction Depression, cardiovascular diseases and diabetes are among the major non-communicable diseases, leading to significant disability and mortality worldwide. These diseases may share environmental and genetic determinants associated with multimorbid patterns. Stressful early-life events are among the primary factors associated with the development of mental and physical diseases. However, possible causative mechanisms linking early life stress (ELS) with psycho-cardio-metabolic (PCM) multi-morbidity are not well understood. This prevents a full understanding of causal pathways towards the shared risk of these diseases and the development of coordinated preventive and therapeutic interventions. Methods and analysis This paper describes the study protocol for EarlyCause, a large-scale and inter-disciplinary research project funded by the European Union’s Horizon 2020 research and innovation programme. The project takes advantage of human longitudinal birth cohort data, animal studies and cellular models to test the hypothesis of shared mechanisms and molecular pathways by which ELS shapes an individual’s physical and mental health in adulthood. The study will research in detail how ELS converts into biological signals embedded simultaneously or sequentially in the brain, the cardiovascular and metabolic systems. The research will mainly focus on four biological processes including possible alterations of the epigenome, neuroendocrine system, inflammatome, and the gut microbiome. Life-course models will integrate the role of modifying factors as sex, socioeconomics, and lifestyle with the goal to better identify groups at risk as well as inform promising strategies to reverse the possible mechanisms and/or reduce the impact of ELS on multi-morbidity development in high-risk individuals. These strategies will help better manage the impact of multi-morbidity on human health and the associated risk.

Integration of artificial intelligence into clinical patient management: focus on cardiac imaging

Abstract: Cardiac imaging is a crucial component in the management of patients with heart disease, and as such it influences multiple, inter-related parts of the clinical workflow: physician-patient contact, image acquisition, image pre- and postprocessing, study reporting, diagnostics and outcome predictions, medical interventions, and, finally, knowledge-building through clinical research. With the gradual and ubiquitous infiltration of artificial intelligence into cardiology, it has become clear that, when used appropriately, it will influence and potentially improve-through automation, standardization and data integration-all components of the clinical workflow. This review aims to present a comprehensive view of full integration of artificial intelligence into the standard clinical patient management-with a focus on cardiac imaging, but applicable to all information handling-and to discuss current barriers that remain to be overcome before its widespread implementation and integration.

Memory-aware curriculum federated learning for breast cancer classification.

Abstract: For early breast cancer detection, regular screening with mammography imaging is recommended. Routinary examinations result in datasets with a predominant amount of negative samples. A potential solution to such class-imbalance is joining forces across multiple institutions. Developing a collaborative computer-aided diagnosis system is challenging in different ways. Patient privacy and regulations need to be carefully respected. Data across institutions may be acquired from different devices or imaging protocols, leading to heterogeneous non-IID data. Also, for learning-based methods, new optimization strategies working on distributed data are required. Recently, federated learning has emerged as an effective tool for collaborative learning. In this setting, local models perform computation on their private data to update the global model. The order and the frequency of local updates influence the final global model. Hence, the order in which samples are locally presented to the optimizers plays an important role. In this work, we define a memory-aware curriculum learning method for the federated setting. Our curriculum controls the order of the training samples paying special attention to those that are forgotten after the deployment of the global model. Our approach is combined with unsupervised domain adaptation to deal with domain shift while preserving data privacy. We evaluate our method with three clinical datasets from different vendors. Our results verify the effectiveness of federated adversarial learning for the multi-site breast cancer classification. Moreover, we show that our proposed memory-aware curriculum method is beneficial to further improve classification performance. Our code is publicly available at: this https URL.

A Riemannian approach to predicting brain function from the structural connectome

Abstract: Ongoing brain function is largely determined by the underlying wiring of the brain, but the specific rules governing this relationship remain unknown. Emerging literature has suggested that functional interactions between brain regions emerge from the structural connections through mono- as well as polysynaptic mechanisms. Here, we propose a novel approach based on diffusion maps and Riemannian optimization to emulate this dynamic mechanism in the form of random walks on the structural connectome and predict functional interactions as a weighted combination of these random walks. Our proposed approach was evaluated in two different cohorts of healthy adults (Human Connectome Project, HCP; Microstructure-Informed Connectomics, MICs). Our approach outperformed existing approaches and showed that performance plateaus approximately around the third random walk. At macroscale, we found that the largest number of walks was required in nodes of the default mode and frontoparietal networks, underscoring an increasing relevance of polysynaptic communication mechanisms in transmodal cortical networks compared to primary and unimodal systems.

3D fetal face reconstruction from ultrasound imaging

Abstract: Comunicacio presentada al VISIGRAPP 2021: The 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, celebrat del 8 al 10 de febrer de 2021 de manera virtual.

Riemannian geometry of functional connectivity matrices for multi-site attention-deficit/hyperactivity disorder data harmonization

Abstract: AO_SCPLOWBSTRACTC_SCPLOWThe use of multi-site datasets in neuroimaging provides neuroscientists with more statistical power to perform their analyses. However, it has been shown that imaging-site introduces a variability in the data that cannot be attributed to biological sources. In this work, we show that functional connectivity matrices derived from resting-state multi-site data contain a significant imaging-site bias. To this aim, we exploited the fact that functional connectivity matrices belong to the manifold of symmetric positive-definite matrices, making possible to operate on them with Riemannian geometry. We hereby propose a geometry-aware harmonization approach, Rigid Log-Euclidean Translation, that accounts for this site bias. Moreover, we adapted other Riemannian-geometric methods designed for other domain adaptation tasks, and compared them to our proposal. Based on our results, Rigid Log-Euclidean Translation of multi-site functional connectivity matrices seems the most suitable method in a clinical setting. This represents an advance with respect to previous functional connectivity data harmonization approaches, which do not respect the geometric constraints imposed by the underlying structure of the manifold. In particular, when applying our proposed method on data from the ADHD-200 dataset, a multi-site dataset built for the study of attention-deficit/hyperactivity disorder, we obtained results that display a remarkable correlation with established pathophysiological findings and, therefore, represent a substantial improvement when compared to the non-harmonization analysis. Thus, we present evidence supporting that harmonization should be extended to other functional neuroimaging datasets, and provide a simple geometric method to address it.

Detection, growth quantification and malignancy prediction of pulmonary nodules using deep convolutional networks in follow-up CT scans.

Abstract: We address the problem of supporting radiologists in the longitudinal management of lung cancer. Therefore, we proposed a deep learning pipeline, composed of four stages that completely automatized from the detection of nodules to the classification of cancer, through the detection of growth in the nodules. In addition, the pipeline integrated a novel approach for nodule growth detection, which relied on a recent hierarchical probabilistic U-Net adapted to report uncertainty estimates. Also, a second novel method was introduced for lung cancer nodule classification, integrating into a two stream 3D-CNN network the estimated nodule malignancy probabilities derived from a pretrained nodule malignancy network. The pipeline was evaluated in a longitudinal cohort and reported comparable performances to the state of art.

An Uncertainty-aware Hierarchical Probabilistic Network for Early Prediction, Quantification and Segmentation of Pulmonary Tumour Growth.

Abstract: Early detection and quantification of tumour growth would help clinicians to prescribe more accurate treatments and provide better surgical planning. However, the multifactorial and heterogeneous nature of lung tumour progression hampers identification of growth patterns. In this study, we present a novel method based on a deep hierarchical generative and probabilistic framework that, according to radiological guidelines, predicts tumour growth, quantifies its size and provides a semantic appearance of the future nodule. Unlike previous deterministic solutions, the generative characteristic of our approach also allows us to estimate the uncertainty in the predictions, especially important for complex and doubtful cases. Results of evaluating this method on an independent test set reported a tumour growth balanced accuracy of 74%, a tumour growth size MAE of 1.77 mm and a tumour segmentation Dice score of 78%. These surpassed the performances of equivalent deterministic and alternative generative solutions (i.e. probabilistic U-Net, Bayesian test dropout and Pix2Pix GAN) confirming the suitability of our approach.