María J. Ledesma-Carbayo

Full Professor

Universidad Politécnica de Madrid

Spain

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María J. Ledesma-Carbayo is a Full Professor at Universidad Politécnica de Madrid, Spain. Her research areas include deep learning applications in medical imaging, particularly focusing on lung nodule classification and the development of AI-based tools for chest radiography. Additionally, her work explores the real-world implications of monitoring peripheral blood data in cancer immunotherapy and the use of digital microscopy augmented by artificial intelligence for hematological diseases.

María J. Ledesma-Carbayo

Research Interests

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Articles (25)

Spatio-temporal deep learning with temporal attention for indeterminate lung nodule classification

AI-Based Chest Radiography Tools and the Lateral Projection: Challenges and Opportunities.

Monitoring peripheral blood data supports the prediction of immunotherapy response in advanced non-small cell lung cancer based on real-world data.

Unsupervised spatiotemporal classification of deformation patterns of embryonic tissues matches their fate map.

Chest X-Ray–Based Telemedicine Platform for Pediatric Tuberculosis Diagnosis in Low-Resource Settings: Development and Validation Study

A Smartphone App for Real-Time Assessment of Malaria Prophylaxis Adverse Events

A Smartphone App for Real-Time Assessment of Malaria Prophylaxis Adverse Events

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

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

Concordance in the estimation of tumor percentage in non-small cell lung cancer using digital pathology.

Fetal MRI by Robust Deep Generative Prior Reconstruction and Diffeomorphic Registration

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

Evaluation of the Performance of a 3D-Printed Smartphone-Based Retinal Imaging Device as a Screening Tool for Retinal Pathology in Mozambique

Prediction of Cardiac Resynchronization Therapy Response Using a Lead Placement Score Derived From 4-Dimensional Computed Tomography

Telemedicine for international travelers through a Smartphone-based monitoring platform (Trip Doctor®)

Analysis of Potential Biases on Mammography Datasets for Deep Learning Model Development

Breast Tumor Localization by Prone to Supine Landmark Driven Registration for Surgical Planning

Deep learning-based lesion subtyping and prediction of clinical outcomes in COVID-19 pneumonia using chest CT

Digital system augmented by artificial intelligence to interpret bone marrow samples for hematological disease diagnosis

Effects of Cardiac Stem Cell on Postinfarction Arrhythmogenic Substrate

Intrapericardial cardiosphere-derived cells hinder epicardial dense scar expansion and promote electrical homogeneity in a porcine post-infarction model

Regional left ventricular endocardial strains estimated from low-dose 4DCT: Comparison with cardiac magnetic resonance feature tracking

Collaborators (12)

ANA DELIA RAMOS GUERRA

Juan José Gómez-Valverde

Felicia Alfano

Joseph Hajnal

Jens Cosedis Nielsen

Maria-Jesus Pinazo

Josep Castellvi

Maria Linares

Javier Pascau

Jose Muñoz Gutierrez

Andres Santos

Maria Deprez

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