María J. Ledesma‐Carbayo
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View article: Multi-view deep learning framework for the detection of chest X-rays compatible with pediatric pulmonary tuberculosis
Multi-view deep learning framework for the detection of chest X-rays compatible with pediatric pulmonary tuberculosis Open
Tuberculosis (TB) remains a major global health burden, particularly in low-resource, high-prevalence regions. Pediatric TB diagnosis poses challenges with non-specific symptoms and less distinct radiological manifestations than adult TB. …
View article: Post-Processing Methods for Improving Accuracy in MRI Inpainting
Post-Processing Methods for Improving Accuracy in MRI Inpainting Open
Magnetic Resonance Imaging (MRI) is the primary imaging modality used in the diagnosis, assessment, and treatment planning for brain pathologies. However, most automated MRI analysis tools, such as segmentation and registration pipelines, …
View article: Spatio-temporal deep learning with temporal attention for indeterminate lung nodule classification
Spatio-temporal deep learning with temporal attention for indeterminate lung nodule classification Open
Lung cancer is the leading cause of cancer-related death worldwide. Deep learning-based computer-aided diagnosis (CAD) systems in screening programs enhance malignancy prediction, assist radiologists in decision-making, and reduce inter-re…
View article: Patient‐specific visco‐hyperelastic mechanical model for breast tumor localization in surgical planning
Patient‐specific visco‐hyperelastic mechanical model for breast tumor localization in surgical planning Open
Breast‐conserving surgery is typically performed with the patient in a supine position, whereas preoperative diagnostic MRI breast images are obtained with the patient in a prone position. The change in patient positioning causes significa…
View article: Correction: Monitoring peripheral blood data supports the prediction of immunotherapy response in advanced non‑small cell lung cancer based on real‑world data
Correction: Monitoring peripheral blood data supports the prediction of immunotherapy response in advanced non‑small cell lung cancer based on real‑world data Open
View article: Artificial Intelligence algorithm for real-time detection and counting of<i>Trypanosoma cruzi</i>parasites using smartphone microscopy
Artificial Intelligence algorithm for real-time detection and counting of<i>Trypanosoma cruzi</i>parasites using smartphone microscopy Open
Chagas disease affects 6–7 million people worldwide and causes approximately 12,000 deaths annually. Diagnostic methods vary by disease stage, with serological tests commonly used in the chronic phase, while microscopy and molecular techni…
View article: Monitoring peripheral blood data supports the prediction of immunotherapy response in advanced non-small cell lung cancer based on real-world data
Monitoring peripheral blood data supports the prediction of immunotherapy response in advanced non-small cell lung cancer based on real-world data Open
The identification of non-small cell lung cancer (NSCLC) patients who will benefit from immunotherapy remains a clinical challenge. Monitoring real-world data (RWD) in the first cycles of therapy may provide a more accurate representation …
View article: Unsupervised spatiotemporal classification of deformation patterns of embryonic tissues matches their fate map
Unsupervised spatiotemporal classification of deformation patterns of embryonic tissues matches their fate map Open
View article: Magnetic Resonance Imaging Feature-Based Subtyping and Model Ensemble for Enhanced Brain Tumor Segmentation
Magnetic Resonance Imaging Feature-Based Subtyping and Model Ensemble for Enhanced Brain Tumor Segmentation Open
Accurate and automatic segmentation of brain tumors in multi-parametric magnetic resonance imaging (mpMRI) is essential for quantitative measurements, which play an increasingly important role in clinical diagnosis and prognosis. The Inter…
View article: Adult Glioma Segmentation in Sub-Saharan Africa using Transfer Learning on Stratified Finetuning Data
Adult Glioma Segmentation in Sub-Saharan Africa using Transfer Learning on Stratified Finetuning Data Open
Gliomas, a kind of brain tumor characterized by high mortality, present substantial diagnostic challenges in low- and middle-income countries, particularly in Sub-Saharan Africa. This paper introduces a novel approach to glioma segmentatio…
View article: Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance Imaging
Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance Imaging Open
Segmenting brain tumors in multi-parametric magnetic resonance imaging enables performing quantitative analysis in support of clinical trials and personalized patient care. This analysis provides the potential to impact clinical decision-m…
View article: Data Alchemy: Mitigating Cross-Site Model Variability Through Test Time Data Calibration
Data Alchemy: Mitigating Cross-Site Model Variability Through Test Time Data Calibration Open
Deploying deep learning-based imaging tools across various clinical sites poses significant challenges due to inherent domain shifts and regulatory hurdles associated with site-specific fine-tuning. For histopathology, stain normalization …
View article: IMG-28. AUTOMATIC BRAIN TUMOR VOLUMETRIC ANALYSIS IN MAGNETIC RESONANCE IMAGING GENERALIZABLE TO PEDIATRIC NEURO-ONCOLOGY
IMG-28. AUTOMATIC BRAIN TUMOR VOLUMETRIC ANALYSIS IN MAGNETIC RESONANCE IMAGING GENERALIZABLE TO PEDIATRIC NEURO-ONCOLOGY Open
BACKGROUND The prognosis of brain tumors is variable in clinical practice if it only relies on human interpretation of magnetic resonance imaging (MRI). The automatic segmentation of brain tumors in MRI enables quantitative analysis in sup…
View article: Zero-Shot Pediatric Tuberculosis Detection in Chest X-Rays Using Self-Supervised Learning
Zero-Shot Pediatric Tuberculosis Detection in Chest X-Rays Using Self-Supervised Learning Open
Tuberculosis (TB) remains a significant global health challenge, with\npediatric cases posing a major concern. The World Health Organization (WHO)\nadvocates for chest X-rays (CXRs) for TB screening. However, visual\ninterpretation by radi…
View article: Chest X-Ray–Based Telemedicine Platform for Pediatric Tuberculosis Diagnosis in Low-Resource Settings: Development and Validation Study
Chest X-Ray–Based Telemedicine Platform for Pediatric Tuberculosis Diagnosis in Low-Resource Settings: Development and Validation Study Open
Background Tuberculosis (TB) remains a major cause of morbidity and death worldwide, with a significant impact on children, especially those under the age of 5 years. The complex diagnosis of pediatric TB, compounded by limited access to m…
View article: Edge Artificial Intelligence (AI) for real-time automatic quantification of filariasis in mobile microscopy
Edge Artificial Intelligence (AI) for real-time automatic quantification of filariasis in mobile microscopy Open
Filariasis, a neglected tropical disease caused by roundworms, is a significant public health concern in many tropical countries. Microscopic examination of blood samples can detect and differentiate parasite species, but it is time consum…
View article: DiCoM -- Diverse Concept Modeling towards Enhancing Generalizability in Chest X-Ray Studies
DiCoM -- Diverse Concept Modeling towards Enhancing Generalizability in Chest X-Ray Studies Open
Chest X-Ray (CXR) is a widely used clinical imaging modality and has a pivotal role in the diagnosis and prognosis of various lung and heart related conditions. Conventional automated clinical diagnostic tool design strategies relying on r…
View article: Digital Microscopy Augmented by Artificial Intelligence to Interpret Bone Marrow Samples for Hematological Diseases
Digital Microscopy Augmented by Artificial Intelligence to Interpret Bone Marrow Samples for Hematological Diseases Open
Analysis of bone marrow aspirates (BMAs) is an essential step in the diagnosis of hematological disorders. This analysis is usually performed based on a visual examination of samples under a conventional optical microscope, which involves …
View article: Evaluation of the Performance of a 3D-Printed Smartphone-Based Retinal Imaging Device as a Screening Tool for Retinal Pathology in Mozambique
Evaluation of the Performance of a 3D-Printed Smartphone-Based Retinal Imaging Device as a Screening Tool for Retinal Pathology in Mozambique Open
Low-income countries carry approximately 90% of the global burden of visual impairment, and up to 80% of this could be prevented or cured. However, there are only a few studies on the prevalence of retinal disease in these countries. Easie…
View article: Chest X-Ray–Based Telemedicine Platform for Pediatric Tuberculosis Diagnosis in Low-Resource Settings: Development and Validation Study (Preprint)
Chest X-Ray–Based Telemedicine Platform for Pediatric Tuberculosis Diagnosis in Low-Resource Settings: Development and Validation Study (Preprint) Open
BACKGROUND Tuberculosis (TB) remains a major cause of morbidity and death worldwide, with a significant impact on children, especially those under the age of 5 years. The complex diagnosis of pediatric TB, compounded by limited access to …
View article: Edge Artificial Intelligence for real-time automatic quantification of filariasis in mobile microscopy
Edge Artificial Intelligence for real-time automatic quantification of filariasis in mobile microscopy Open
Filariasis, a neglected tropical disease caused by roundworms, is a significant public health concern in many tropical countries. Microscopic examination of blood samples can detect and differentiate parasite species, but it is time consum…
View article: Enhancing physicians’ radiology diagnostics of COVID-19’s effects on lung health by leveraging artificial intelligence
Enhancing physicians’ radiology diagnostics of COVID-19’s effects on lung health by leveraging artificial intelligence Open
Introduction: This study aimed to develop an individualized artificial intelligence model to help radiologists assess the severity of COVID-19’s effects on patients’ lung health. Methods: Data was collected from medical records of 1103 pat…
View article: A Lightweight, Rapid and Efficient Deep Convolutional Network for Chest X-Ray Tuberculosis Detection
A Lightweight, Rapid and Efficient Deep Convolutional Network for Chest X-Ray Tuberculosis Detection Open
Tuberculosis (TB) is still recognized as one of the leading causes of death worldwide. Recent advances in deep learning (DL) have shown to enhance radiologists' ability to interpret chest X-ray (CXR) images accurately and with fewer errors…
View article: Integration of longitudinal deep-radiomics and clinical data improves the prediction of durable benefits to anti-PD-1/PD-L1 immunotherapy in advanced NSCLC patients
Integration of longitudinal deep-radiomics and clinical data improves the prediction of durable benefits to anti-PD-1/PD-L1 immunotherapy in advanced NSCLC patients Open
Background Identifying predictive non-invasive biomarkers of immunotherapy response is crucial to avoid premature treatment interruptions or ineffective prolongation. Our aim was to develop a non-invasive biomarker for predicting immunothe…
View article: Use of semi-synthetic data for catheter segmentation improvement
Use of semi-synthetic data for catheter segmentation improvement Open
In the era of data-driven machine learning algorithms, data is the new oil. For the most optimal results, datasets should be large, heterogeneous and, crucially, correctly labeled. However, data collection and labeling are time-consuming a…
View article: Deep learning-based lung segmentation and automatic regional template in chest X-ray images for pediatric tuberculosis
Deep learning-based lung segmentation and automatic regional template in chest X-ray images for pediatric tuberculosis Open
Tuberculosis (TB) is still considered a leading cause of death and a substantial threat to global child health. Both TB infection and disease are curable using antibiotics. However, most children who die of TB are never diagnosed or treate…
View article: Effects of Cardiac Stem Cell on Postinfarction Arrhythmogenic Substrate
Effects of Cardiac Stem Cell on Postinfarction Arrhythmogenic Substrate Open
Clinical data suggest that cardiosphere-derived cells (CDCs) could modify post-infarction scar and ventricular remodeling and reduce the incidence of ventricular tachycardia (VT). This paper assesses the effect of CDCs on VT substrate in a…
View article: Intrapericardial cardiosphere-derived cells hinder epicardial dense scar expansion and promote electrical homogeneity in a porcine post-infarction model
Intrapericardial cardiosphere-derived cells hinder epicardial dense scar expansion and promote electrical homogeneity in a porcine post-infarction model Open
The arrhythmic substrate of ventricular tachycardias in many structural heart diseases is located in the epicardium, often resulting in poor outcomes with currently available therapies. Cardiosphere-derived cells (CDCs) have been shown to …
View article: Fetal MRI by Robust Deep Generative Prior Reconstruction and Diffeomorphic Registration
Fetal MRI by Robust Deep Generative Prior Reconstruction and Diffeomorphic Registration Open
Magnetic resonance imaging of whole fetal body and placenta is limited by different sources of motion affecting the womb. Usual scanning techniques employ single-shot multi-slice sequences where anatomical information in different slices m…
View article: Digital system augmented by artificial intelligence to interpret bone marrow samples for hematological disease diagnosis
Digital system augmented by artificial intelligence to interpret bone marrow samples for hematological disease diagnosis Open
Analysis of bone marrow aspirates (BMA) is an essential step in the diagnosis of hematological disorders. This analysis is usually performed based on visual examination of the samples under a conventional optical microscope, which involves…