Browsing by Author "Chabert, Steren"

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    AI Applied to Cardiac Magnetic Resonance for Precision Medicine in Coronary Artery Disease: A Systematic Review
    (2025) Jiménez Jara, Cristina; Salas, Rodrigo; Díaz Navarro, Rienzi; Chabert, Steren; Andía Kohnenkampf, Marcelo Edgardo; Vega, Julián; Urbina, Jesús; Uribe, Sergio; Sekine, Tetsuro; Raimondi, Francesca; Sotelo, Julio
    Cardiac magnetic resonance (CMR) imaging has become a key tool in evaluating myocardial injury secondary to coronary artery disease (CAD), providing detailed assessments of cardiac morphology, function, and tissue composition. The integration of artificial intelligence (AI), including machine learning and deep learning techniques, has enhanced the diagnostic capabilities of CMR by automating segmentation, improving image interpretation, and accelerating clinical workflows. Radiomics, through the extraction of quantitative imaging features, complements AI by revealing sub-visual patterns relevant to disease characterization. This systematic review analyzed AI applications in CMR for CAD. A structured search was conducted in MEDLINE, Web of Science, and Scopus up to 17 March 2025, following PRISMA guidelines and quality-assessed with the CLAIM checklist. A total of 106 studies were included: 46 on classification, 19 using radiomics, and 41 on segmentation. AI models were used to classify CAD vs. controls, predict major adverse cardiovascular events (MACE), arrhythmias, and post-infarction remodeling. Radiomics enabled differentiation of acute vs. chronic infarction and prediction of microvascular obstruction, sometimes from non-contrast CMR. Segmentation achieved high performance for myocardium (DSC up to 0.95), but scar and edema delineation were more challenging. Reported performance was moderate-to-high across tasks (classification AUC = 0.66–1.00; segmentation DSC = 0.43–0.97; radiomics AUC = 0.57–0.99). Despite promising results, limitations included small or overlapping datasets. In conclusion, AI and radiomics offer substantial potential to support diagnosis and prognosis of CAD through advanced CMR image analysis.
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    Benchmarking YOLO Models for Intracranial Hemorrhage Detection Using Varied CT Data Sources
    (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2024) Tapia, Gonzalo; Allende-Cid, Hector; Chabert, Steren; Mery Quiroz Domingo Arturo; Salas, Rodrigo
    Intracranial hemorrhages (ICH) are a significant challenge in emergency medicine due to the critical nature of a timely and accurate diagnosis. This study evaluates the performance of six versions of the You Only Look Once (YOLO) object detection model, from YOLOv5 to YOLOv10, in detecting ICH using computed tomography (CT) scans. The primary focus is understanding the advancements in YOLO architectures over time and their impact on detection accuracy and inference speed. The study used the Brain Hemorrhage Extended Dataset (BHX), comprising 491 CT scans with annotations for six types of hemorrhages: epidural, subdural, subarachnoid, intraparenchymal, intraventricular, and chronic hemorrhage, and introduces a new data set obtained from a major hospital in Chile. The models were trained using a combination of single-class and multi-class approaches to address class imbalance and were evaluated based on precision, recall, F1 score, and mean average precision (mAP). The models were evaluated in three distinct contexts: 1) a biased scenario where images of the same individual could appear in both training and testing sets, 2) a cross-validation setup ensuring the independence of images by separating the sets based on subjects, and 3) an external validation using one dataset for training and the Chilean dataset for testing, maintaining full independence between training and evaluation. The findings indicate that YOLOv8 and YOLOv10 demonstrate superior detection accuracy and inference efficiency performance, respectively, compared to previous versions. In particular, with image independence, YOLOv8 reached the highest average mAP for all classes, with a score of 0.4. This comparative analysis provides information on the effectiveness of architectural advances in YOLO models for medical applications and suggests directions for future improvements in ICH detection.
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    Development of mechanical ventilators in Chile. Chronicle of the initiative "Un Respiro para Chile
    (2022) Bugedo, Guillermo; Tobar, Eduardo; Alegria, Leyla; Oviedo, Vanessa; Arellano, Daniel; Basoalto, Roque; Enberg, Luis; Suarez, Pablo; Bitran, Eduardo; Chabert, Steren; Bruhn, Alejandro
    At the beginning of the COVID-19 pandemic in Chile, in March 2020, a projection indicated that a significant group of patients with pneumonia would require admission to an Intensive Care Unit and connection to a mechanical ventilator. Therefore, a paucity of these devices and other supplies was predicted. The initiative "Un respiro para Chile" brought together many people and institutions, public and private. In the course of three months, it allowed the design and building of several ventilatory assistance devices, which could be used in critically ill patients.
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    Hybrid Framework for Automated Generation of Mammography Radiology Reports
    (2025) Godoy, Eduardo; Mellado, Diego; de Ferrari, Joaquín; Querales, Marvin; Saez, Alex; Chabert, Steren; Parra Santander, Denis Alejandro; Salas, Rodrigo
    Breast cancer remains a significant health concern for women at various stages of life, impacting both productivity and reproductive health. Recent advancements in deep learning (DL) have enabled substantial progress in the automation of radiological reports, offering potential support to radiologists and streamlining examination processes. This study introduces a framework for automated clinical text generation aimed at assisting radiologists in mammography examinations. Rather than replacing medical expertise, the system provides pre-processed evidence and automatic diagnostic suggestions for radiologist validation. The framework leverages an encoder–decoder architecture for natural language generation (NLG) models, trained and fine-tuned on a corpus of Spanish radiological text. Additionally, we incorporate an image intensity enhancement technique to address the issue of image quality variability and assess its impact on report generation outcomes. A comparative analysis using NLG metrics is conducted to identify the optimal feature extraction method. Furthermore, named entity recognition (NER) techniques are employed to extract key clinical concepts and automate precision evaluations. Our results demonstrate that the proposed framework could be a solid starting point for systematizing and implementing automated clinical report generation based on medical images.
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    Quantitative description of the morphology and ossification center in the axial skeleton of 20-week gestation formalin-fixed human fetuses using magnetic resonance images
    (WILEY, 2012) Chabert, Steren; Villalobos, Manuel; Ulloa, Patricia; Salas, Rodrigo; Tejos, Cristian; San Martin, Sebastian; Pereda, Jaime
    Objectives Human tissues are usually studied using a series of two-dimensional visualizations of in vivo or cutout specimens. However, there is no precise anatomical description of some of the processes of human fetal development. The purpose of our study is to develop a quantitative description of the normal axial skeleton by means of high-resolution three-dimensional magnetic resonance (MR) images, collected from six normal 20-week-old human fetuses fixed in formaldehyde.