Academic publications

Published: August 12, 2025

Use of Radiomics to Predict Adverse Outcomes in Patients with Pulmonary Embolism: A Scoping Review of an Unresolved Clinical Challenge.

Miguel Ángel Casado-Suela, Juan Torres-Macho, Jesús Prada-Alonso, Rodrigo Pastorín-Salis, Ana Martínez de la Casa-Muñoz, Eva Ruiz-Navío, Ana Bustamante-Fermosel, Anabel Franco-Moreno

Inherent to the challenge of acute pulmonary embolism (APE), the breadth of presentation ranges from asymptomatic pulmonary embolism to sudden death. Risk stratification of patients with PSA is mandatory for determining the appropriate therapeutic management approach. However, the optimal clinically most relevant combination of predictors of death remains to be determined. Radiomics is an emerging discipline in medicine that extracts and analyzes quantitative data from medical images using mathematical algorithms. In APE, these data can reveal thrombus characteristics that are not visible to the naked eye, which may help to more accurately identify patients at higher risk of early clinical deterioration or mortality. We conducted a scoping review to explore the current evidence on the prognostic performance of radiomic models in patients with PSA.

Read complete article

Published: May 2025

Detection of atherosclerosis in Primary Care with Artificial Intelligence applied to Retinography

Prada Alonso, Jesús; Bringas Tejero, Santos; Espejo Paeres, Ángela Carolina; Crespo Carballes, María Jose; TorresMacho, Juan

Subclinical atherosclerosis has an alarming prevalence of 60% in people aged 40-65 years without previous cardiovascular events, and remains asymptomatic until it causes serious events. Traditional risk scores such as SCORE or Framingham have proven to be insufficient, since approximately 50% of infarctions occur in patients previously classified as low cardiovascular risk. There is also a documented problem of underdiagnosis in women, contributing to inequities in health care. Technologies based on artificial intelligence represent an opportunity to transform this scenario by making it possible to diagnose subclinical atherosclerosis in primary care settings in an early, efficient and equitable manner.

Read complete article

Published: March 13, 2024

Automatic calculation of LVEF from echocardiograms using Artificial Intelligence models.

C. Espejo Paeres, M. Ortas Nadal, J. Prada Alonso

Left ventricular (LV) systolic function has crucial implications for the diagnosis and prognosis of patients with cardiac disease and is the limiting factor for the use of many antineoplastic agents in oncologic patients Estimation of left ventricular systolic function is the most common indication for echocardiography. Echocardiography provides real-time images of the beating heart and is the most widely used cardiac imaging modality. Left ventricular ejection fraction (LVEF), calculated from echocardiographic images, is the most widely used and validated parameter for assessing LV systolic function. Although LVEF assessment plays a key role in prognostic and therapeutic decisions, accurate measurement of LVEF remains a challenge.

Read complete article

Published: December 27, 2024

Development of a Predictive Model of Occult Cancer After a Venous Thromboembolism Event Using Machine Learning: The CLOVER Stud

by Anabel Franco-Moreno,Elena Madroñal-Cerezo, Cristina Lucía de Ancos-Aracili, Ana Isabel Farfán-Sedano, Ana Isabel Farfán-Sedano, Nuria Muñoz-Rivas, José Bascuñana Morejón-Girón, José Manuel Ruiz-Giardín, Federico Álvarez-Rodríguez, Jesús Prada Alonso, Yvonne Gala García, Miguel Ángel Casado-Suela, Ana Bustamante-Fermosel, Nuria Alfaro-Fernández and Juan Torres-Macho on behalf of the CLOVER Research Group

Background and Objectives: Venous thromboembolism (VTE) can be the first manifestation of an underlying cancer. This study aimed to develop a predictive model to assess the risk of occult cancer between 30 days and 24 months after a venous thrombotic event using machine learning (ML). Materials and Methods: We designed a case-control study nested in a cohort of patients with VTE included in a prospective registry from two Spanish hospitals between 2005 and 2021. Both clinically and ML-driven feature selection were performed to identify predictors for occult cancer. XGBoost, LightGBM, and CatBoost algorithms were used to train different prediction models, which were subsequently validated in a hold-out dataset....

Read complete article

Published: June 2021

COVID-19 Mortality Risk Prediction Using X-Ray Images

Prada, J.; Gala, Y.; Sierra, A. L.

The pandemic caused by coronavirus COVID-19 has already had a massive impact in our societies in terms of health, economy, and social distress. One of the most common symptoms caused by COVID-19 are lung problems like pneumonia, which can be detected using X-ray images. On the other hand, the popularity of Machine Learning models has grown exponentially in recent years and Deep Learning techniques have become the state-of-the-art for image classification tasks and is widely used in the healthcare sector nowadays as support for clinical decisions. This research aims to build a prediction model based on Machine Learning, including Deep Learning, techniques to predict the mortality risk of a particular patient given an X-ray and some basic demographic data....

Read complete article

Academic publications

Use of Radiomics to Predict Adverse Outcomes in Patients with Pulmonary Embolism: A Scoping Review of an Unresolved Clinical Challenge.

Detection of atherosclerosis in Primary Care with Artificial Intelligence applied to Retinography

Automatic calculation of LVEF from echocardiograms using Artificial Intelligence models.

Development of a Predictive Model of Occult Cancer After a Venous Thromboembolism Event Using Machine Learning: The CLOVER Stud

COVID-19 Mortality Risk Prediction Using X-Ray Images

Horus ML

Solutions

Publications