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Development of an artificial intelligence powered software for automated analysis of skeletal muscle ultrasonography
(Springer Science and Business Media LLC, 2025-04-29)
Zoe Calulo Rivera
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GONZALEZ SEGUEL, FELIPE ANDRES
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Arimitsu Horikawa-Strakovsky
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Catherine Granger
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Aarti Sarwal
1Scopus© Citations 11
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Machine learning-based identification of efficient and restrictive physiological subphenotypes in acute respiratory distress syndrome
(Springer Science and Business Media LLC, 2025-03-01)
Gabriela Meza-Fuentes
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DELGADO BECERRA, OROZIMBA IRIS
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Mario Barbé
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Ignacio Sánchez
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RETAMAL LUCERO, MAURICIO ANTONIO
Acute respiratory distress syndrome (ARDS) is a severe condition with high morbidity and mortality, characterized by significant clinical heterogeneity. This heterogeneity complicates treatment selection and patient inclusion in clinical trials. Therefore, the objective of this study is to identify physiological subphenotypes of ARDS using machine learning, and to determine ventilatory variables that can effectively discriminate between these subphenotypes in a bedside setting with high performance, highlighting potential utility for future clinical stratification approaches.</jats:p> Methodology A retrospective cohort study was conducted using data from our ICU, covering admissions from 2017 to 2021. The study included 224 patients over 18 years of age diagnosed with ARDS according to the Berlin criteria and undergoing invasive mechanical ventilation (IMV). Data on physiological and ventilatory variables were collected during the first 24 h IMV. We applied machine learning techniques to categorize subphenotypes in ARDS patients. Initially, we employed the unsupervised Gaussian Mixture Classification Model approach to group patients into subphenotypes. Subsequently, we applied supervised models such as XGBoost to perform root cause analysis, evaluate the classification of patients into these subgroups, and measure their performance.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>Our models identified two ARDS subphenotypes with significant clinical differences and significant outcomes. Subphenotype Efficient (<jats:italic>n</jats:italic> = 172) was characterized by lower mortality, lower clinical severity and presented a less restrictive pattern with better gas exchange compared to Subphenotype Restrictive (<jats:italic>n</jats:italic> = 52), which showed the opposite. The models demonstrated high performance with an area under the ROC curve of 0.94, sensitivity of 94.2% and specificity of 87.5%, in addition to an F1 score of 0.85. The most influential variables in the discrimination of subphenotypes were distension pressure, respiratory frequency and exhaled carbon dioxide volume.Conclusion This study presents an approach to improve subphenotype categorization in ARDS. The generation of clustering and prediction models by machine learning involving clinical, ventilatory mechanics, and gas exchange variables allowed for more accurate stratification of patients. These findings have the potential to optimize individualized treatment selection and improve clinical outcomes in patients with ARDS.</jats:p> Graphical Abstract
Scopus© Citations 2 5
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SPINE20 Recommendations 2024 -Spinal Disability: Social Inclusion as a Key to Prevention and Management
(2024)
Cristiano M. Menezes
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Carlos Tucci
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Koji Tamai
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Harvinder S. Chhabra
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Fahad H. Alhelal
<jats:p> Spine disorders are the leading cause of disability worldwide. To promote social inclusion, it is essential to ensure that people can participate in their societies by improving their ability, opportunities, and dignity, through access to high-quality, evidence-based, and affordable spine services for all. </jats:p><jats:p> To achieve this goal, SPINE20 recommends six actions. </jats:p><jats:p> - SPINE20 recommends that G20 countries deliver evidence-based education to the community health workers and primary care clinicians to promote best practice for spine health, especially in underserved communities. </jats:p><jats:p> - SPINE20 recommends that G20 countries deliver evidence-based, high-quality, cost-effective spine care interventions that are accessible, affordable and beneficial to patients. </jats:p><jats:p> - SPINE20 recommends that G20 countries invest in Health Policy and System Research (HPSR) to generate evidence to develop and implement policies aimed at integrating rehabilitation in primary care to improve spine health. </jats:p><jats:p> - SPINE20 recommends that G20 countries support ongoing research initiatives on digital technologies including artificial intelligence, regulate digital technologies, and promote evidence-based, ethical digital solutions in all aspects of spine care, to enrich patient care with high value and quality. </jats:p><jats:p> - SPINE20 recommends that G20 countries prioritize social inclusion by promoting equitable access to comprehensive spine care through collaborations with healthcare providers, policymakers, and community organizations. </jats:p><jats:p> - SPINE20 recommends that G20 countries prioritize spine health to improve the well-being and productivity of their populations. Government health systems are expected to create a healthier, more productive, and equitable society for all through collaborative efforts and sustained investment in evidence-based care and promotion of spine health. </jats:p>
Scopus© Citations 7 1
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Predicting Response to Intravesical BCG in High-Risk NMIBC Using an Artificial Intelligence–Powered Pathology Assay: Development and Validation in an International 12-Center Cohort
(2024)
Yair Lotan
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Viswesh Krishna
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Waleed M. Abuzeid
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Bryn Launer
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Sam S. Chang
Scopus© Citations 30
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Interpretable multimodal classification for age-related macular degeneration diagnosis
(2024)
Carla Vairetti
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Sebastián Maldonado
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Loreto Cuitino
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URZUA SALINAS, CRISTHIAN
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Xu Yanwu
<jats:p>Explainable Artificial Intelligence (XAI) is an emerging machine learning field that has been successful in medical image analysis. Interpretable approaches are able to “unbox” the black-box decisions made by AI systems, aiding medical doctors to justify their diagnostics better. In this paper, we analyze the performance of three different XAI strategies for medical image analysis in ophthalmology. We consider a multimodal deep learning model that combines optical coherence tomography (OCT) and infrared reflectance (IR) imaging for the diagnosis of age-related macular degeneration (AMD). The classification model is able to achieve an accuracy of 0.94, performing better than other unimodal alternatives. We analyze the XAI methods in terms of their ability to identify retinal damage and ease of interpretation, concluding that grad-CAM and guided grad-CAM can be combined to have both a coarse visual justification and a fine-grained analysis of the retinal layers. We provide important insights and recommendations for practitioners on how to design automated and explainable screening tests based on the combination of two image sources.</jats:p>
Scopus© Citations 1
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The Future Impact and Role of the Journal<i>Radiology</i>in Latin America
(2023)
SILVA FUENTE ALBA, CLAUDIO SERGIO
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Guillermo Elizondo-Riojas
6Scopus© Citations 1
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The Role of MicroRNAs in Breast Cancer and the Challenges of Their Clinical Application
(2023)
Juan P. Muñoz
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Pablo Pérez-Moreno
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Yasmín Pérez
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Gloria M. Calaf
<jats:p>MicroRNAs (miRNAs) constitute a subclass of non-coding RNAs that exert substantial influence on gene-expression regulation. Their tightly controlled expression plays a pivotal role in various cellular processes, while their dysregulation has been implicated in numerous pathological conditions, including cancer. Among cancers affecting women, breast cancer (BC) is the most prevalent malignant tumor. Extensive investigations have demonstrated distinct expression patterns of miRNAs in normal and malignant breast cells. Consequently, these findings have prompted research efforts towards leveraging miRNAs as diagnostic tools and the development of therapeutic strategies. The aim of this review is to describe the role of miRNAs in BC. We discuss the identification of oncogenic, tumor suppressor and metastatic miRNAs among BC cells, and their impact on tumor progression. We describe the potential of miRNAs as diagnostic and prognostic biomarkers for BC, as well as their role as promising therapeutic targets. Finally, we evaluate the current use of artificial intelligence tools for miRNA analysis and the challenges faced by these new biomedical approaches in its clinical application. The insights presented in this review underscore the promising prospects of utilizing miRNAs as innovative diagnostic, prognostic, and therapeutic tools for the management of BC.</jats:p>
Scopus© Citations 52
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ISOM 2023 research Panel 4 - Diagnostics and microbiology of otitis media
(2023)
Sharon Ovnat Tamir
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Seweryn Bialasiewicz
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Christopher G. Brennan-Jones
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DER MUSSA, CAROLINA FATHME
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Liron Kariv
Scopus© Citations 10 6
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Color Dependence Analysis in a CNN-Based Computer-Aided Diagnosis System for Middle and External Ear Diseases
(2022)
Michelle Viscaino
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Matias Talamilla
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Juan Cristóbal Maass
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Pablo Henríquez
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Paul H. Délano
<jats:p>Artificial intelligence-assisted otologic diagnosis has been of growing interest in the scientific community, where middle and external ear disorders are the most frequent diseases in daily ENT practice. There are some efforts focused on reducing medical errors and enhancing physician capabilities using conventional artificial vision systems. However, approaches with multispectral analysis have not yet been addressed. Tissues of the tympanic membrane possess optical properties that define their characteristics in specific light spectra. This work explores color wavelengths dependence in a model that classifies four middle and external ear conditions: normal, chronic otitis media, otitis media with effusion, and earwax plug. The model is constructed under a computer-aided diagnosis system that uses a convolutional neural network architecture. We trained several models using different single-channel images by taking each color wavelength separately. The results showed that a single green channel model achieves the best overall performance in terms of accuracy (92%), sensitivity (85%), specificity (95%), precision (86%), and F1-score (85%). Our findings can be a suitable alternative for artificial intelligence diagnosis systems compared to the 50% of overall misdiagnosis of a non-specialist physician.</jats:p>
Scopus© Citations 9 18
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A Free-Market Environmentalist Enquiry on Spain’s Energy Transition along with Its Recent Increasing Electricity Prices
(2022)
William Hongsong Wang
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ESPINOSA LOYOLA, VICTOR IGNACIO
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Jesús Huerta de Soto
<jats:p>This paper analyzes the Spanish energy transition’s general situation and its increasing electricity prices in recent years from a free-market environmentalist (FME) approach. We hypothesize and argue that high taxes, high government subsidies, and government industrial access restrictions breach private property rights, hindering Spain’s renewable energy (RE) development. Our paper discovers that Spain’s state-interventionist policies have increased the cost of the energy and power industries, leading to electricity prices remaining relatively high before and after the outbreak of the COVID-19 pandemic. After reviewing the literature on the FME approach and Spain’s case, a Box–Jenkins (ARIMA) model is used to clarify the economic performance of the Spanish electricity industry with a proposal for forecasting electricity prices. It is observed that Spain fails the EU and its national goal of providing an affordable energy price as a part of the green energy transition. Finally, free-market environmental solutions and policy reforms are proposed to facilitate Spain’s energy transition.</jats:p>
3Scopus© Citations 4

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