DIEGO ARMANDO CARDONA CARDENAS

About DIEGO ARMANDO CARDONA CARDENAS

DIEGO ARMANDO CARDONA CARDENAS, With an exceptional h-index of 6 and a recent h-index of 6 (since 2020), a distinguished researcher at Universidade de São Paulo, specializes in the field of medical image processing, machine learning, pattern recognition, ultrasound tomography, biological signal processing.

His recent articles reflect a diverse array of research interests and contributions to the field:

Exploring the limitations of blood pressure estimation using the photoplethysmography signal

Explainable AI in Deep Learning-based Detection of Aortic Elongation on Chest X-ray Images

A machine-learning sleep-wake classification model using a reduced number of features derived from photoplethysmography and activity signals

Machine learning-based diabetes detection using photoplethysmography signal features

Quality Assessment of Photoplethysmography Signals For Cardiovascular Biomarkers Monitoring Using Wearable Devices

Interpretable deep learning model for cardiomegaly detection with chest X-ray images

Blood pressure estimation from photoplethysmography by considering intra-and inter-subject variabilities: guidelines for a fair assessment

A machine learning approach to predict arterial blood pressure from photoplethysmography signal

DIEGO ARMANDO CARDONA CARDENAS Information

University

Position

Researcher at the Laboratory of Biomedical Informatics of the Heart Institute

Citations(all)

190

Citations(since 2020)

146

Cited By

64

hIndex(all)

6

hIndex(since 2020)

6

i10Index(all)

6

i10Index(since 2020)

5

Email

University Profile Page

Google Scholar

DIEGO ARMANDO CARDONA CARDENAS Skills & Research Interests

medical image processing

machine learning

pattern recognition

ultrasound tomography

biological signal processing

Top articles of DIEGO ARMANDO CARDONA CARDENAS

Exploring the limitations of blood pressure estimation using the photoplethysmography signal

arXiv preprint arXiv:2404.16049

2024/4/9

Explainable AI in Deep Learning-based Detection of Aortic Elongation on Chest X-ray Images

medRxiv

2023

A machine-learning sleep-wake classification model using a reduced number of features derived from photoplethysmography and activity signals

arXiv preprint arXiv:2308.05759

2023/8/7

Machine learning-based diabetes detection using photoplethysmography signal features

arXiv preprint arXiv:2308.01930

2023/8/2

Quality Assessment of Photoplethysmography Signals For Cardiovascular Biomarkers Monitoring Using Wearable Devices

arXiv preprint arXiv:2307.08766

2023/7/17

Interpretable deep learning model for cardiomegaly detection with chest X-ray images

2023/6/27

Blood pressure estimation from photoplethysmography by considering intra-and inter-subject variabilities: guidelines for a fair assessment

Ieee Access

2023/6/9

A machine learning approach to predict arterial blood pressure from photoplethysmography signal

2022/9/4

Chronic lung lesions in COVID-19 survivors: predictive clinical model

BMJ open

2022/6/1

A deep learning approach for COVID-19 screening and localization on chest x-ray images

2022/4/4

Diego Armando Cardona Cardenas
Diego Armando Cardona Cardenas

H-Index: 3

Marco Antonio Gutierrez
Marco Antonio Gutierrez

H-Index: 14

Aplicação da inteligência artificial em imagem cardiovascular: classificação automática de imagens de radiografia de tórax

Revista da Sociedade de Cardiologia do Estado de São Paulo

2022

Long-Term Pulmonary Consequences of Moderate and Severe COVID-19: A Functional Imaging Protocol for Clinical Screening

2021/9/24

Interpretable multi-stream ensemble learning for radiographic pattern recognition

medRxiv

2021/8/10

Diego Armando Cardona Cardenas
Diego Armando Cardona Cardenas

H-Index: 3

The potential role of radiogenomics in precision medicine for COVID-19

Journal of Thoracic Imaging

2021/5/1

Novel chest radiographic biomarkers for COVID-19 using radiomic features associated with diagnostics and outcomes

Journal of Digital Imaging

2021/4

A general fully automated deep-learning method to detect cardiomegaly in chest x-rays

2021/2/15

Automated radiographic bone suppression with deep convolutional neural networks

2021/2/15

Complementary use of priors for pulmonary imaging with electrical impedance and ultrasound computed tomography

Journal of computational and applied mathematics

2021/10/15

Multicenter validation of convolutional neural networks for automated detection of cardiomegaly on chest radiographs

2020/9/15

Multi-view ensemble convolutional neural network to improve classification of pneumonia in low contrast chest x-ray images

2020/7/20

See List of Professors in DIEGO ARMANDO CARDONA CARDENAS University(Universidade de São Paulo)

Co-Authors

academic-engine