Browsing by Autor "Miguel Vera"

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3D ultrasound in cardiology
(2004) Antonio Bravo; Miguel Vera; Delia Madriz; Julio Contreras-Velásquez; José Chacón; Sandra Wilches-Durán; Modesto Graterol-Rivas; Daniela Riaño-Wilches; Joselyn Rojas; Valmore Bermúdez
Cardiovascular imaging analysis is a useful tool for the diagnosis, treatment and monitoring of cardiovascular diseases. Imaging techniques allow non-invasive quantitative assessment of cardiac function, providing morphological, functional and dynamic information. Recent technological advances in ultrasound have made it possible to improve the quality of patient treatment, thanks to the use of modern image processing and analysis techniques. However, the acquisition of these dynamic three-dimensional (3D) images leads to the production of large volumes of data to process, from which cardiac structures must be extracted and analyzed during the cardiac cycle. Extraction, three-dimensional visualization, and qualiﬁcation tools are currently used within the clinical routine, but unfortunately require significant interaction with the physician. These elements justify the development of new efficient and robust algorithms for structure extraction and cardiac motion estimation from three-dimensional images. As a result, making available to clinicians new means to accurately assess cardiac anatomy and function from three-dimensional images represents a definite advance in the investigation of a complete description of the heart from a single examination. The aim of this article is to show what advances have been made in 3D cardiac imaging by ultrasound and additionally to observe which areas have been studied under this imaging modality.
Panlar consensus on hip osteoarthritis
(Elsevier BV, 2017) Oscar Rillo; R. Espinosa; Carlota Acosta; Maritza Quintero; Ligia Monterola; Edgar Nieto; L. Franco; R. Arapé; Sílvia Papasidero; Miguel Vera
Panlar Recommendations for the Management of Knee Osteoarthritis
(Elsevier BV, 2017) M. Quintero; R. Espinosa; H. Sánchez Riera; Raul Souto; Juan Diego Zamora Salas; Francisco Radrigán; Miguel Vera; J Bolaños; Lorena Urioste; Isa Iraheta
Systematic Review of Artificial Intelligence and Electrocardiography for Cardiovascular Disease Diagnosis
(Multidisciplinary Digital Publishing Institute, 2025) Hernando Velandia; Aldo Pardo García; María Isabel Vera-Muñoz; Miguel Vera; Miguel Vera; Miguel Vera
Cardiovascular diseases (CVDs) are the leading cause of death globally. Electrocardiograms (ECGs) are crucial diagnostic tools; however, their traditional interpretations exhibit limited sensitivity and reproducibility. This systematic review discusses the recent advances in artificial intelligence (AI), including deep learning and machine learning, applied to ECG analysis for CVD detection. It examines over 100 studies from 2019 to 2025, classifying AI applications by disease type (heart failure, myocardial infarction, and atrial fibrillation), model architecture (convolutional neural networks, long short-term memory, and hybrid models), and methodological innovation (signal denoising, synthetic data generation, and explainable AI). Comparative tables and conceptual figures highlight performance metrics, dataset characteristics, and implementation challenges. Our findings indicated that AI models outperform traditional methods, especially in terms of detecting subclinical conditions and enabling real-time monitoring via wearable technologies. Nonetheless, issues such as demographic bias, lack of dataset diversity, and regulatory hurdles persist. The review concludes by offering actionable recommendations to enhance clinical translation, equity, and transparency in AI-ECG applications. These insights aim to guide interdisciplinary efforts toward the safe and effective adoption of AI in cardiovascular diagnostics.