Deep Learning and Cardiovascular Diseases: An Updated Narrative Review.

Future Trends in CT for Coronary Artery Disease: From Diagnosis to Prevention.

Background:Brain tumors represent a diverse group of central nervous system neoplasms that require precise diagnosis and characterization for effective treatment. Advances in neuroimaging have improved the detection of these tumors; however, conventional interpretation methods remain time-intensive and subject to variability. Artificial intelligence (AI), particularly through machine learning and deep learning, has emerged as a powerful tool in neuro-oncological imaging, offering automation, consistency, and enhanced diagnostic performance. Objective:This review aims to provide a comprehensive overview of AI-assisted imaging tools used in the detection and characterization of brain tumors. It highlights key AI technologies, their clinical applications, performance compared to human experts, and the emerging trends shaping the future of AI in neuro-oncology. Methods:A narrative review was conducted of recent peer-reviewed studies focused on the application of AI in brain tumor imaging. Emphasis was placed on machine learning and deep learning models used for tumor segmentation, histopathologic and molecular subtype prediction, prognostic modeling, and treatment response monitoring. Technical challenges, ethical concerns, and regulatory considerations were also examined. Results:AI models have demonstrated high accuracy in tasks such as tumor segmentation, classification of tumor types and grades, non-invasive prediction of molecular markers (e.g., IDH mutation, MGMT methylation), and survival prediction. Emerging techniques such as federated learning, multimodal data integration, and explainable AI are addressing key limitations, including data privacy, generalizability, and clinical trust. Conclusion:AI-assisted imaging holds considerable promise in improving the accuracy, speed, and personalization of brain tumor diagnosis and management. For widespread clinical adoption, future efforts should focus on multi-institutional collaboration, prospective validation, regulatory alignment, and clinician education. With continued advancement, AI can become a valuable adjunct in the neuro-oncology diagnostic arsenal, ultimately contributing to better patient outcomes.

In today’s digital environment, the pattern is everywhere. It is present in many aspects of our daily life. Algorithms can be used to detect or mathematically observe a pattern physically. Vector feature values represent a pattern in the digital world. With the advent of artificial intelligence (AI) techniques in the recent era, there are so many machine learning (ML) and deep learning (DL) models that have been developed. ML is the branch of AI, which can perform tasks like data analysis, analytical model building, and classification without being explicitly programmed. DL is the subset of ML in AI. In DL, mostly artificial neural networks (ANNs) are utilized. It has the capability of work based upon unsupervised learning from the data that is unlabeled and unstructured. Using these DL and ML models, extract the meaningful features from the given image or video is known as pattern recognition (PR). PR is used in many engineering applications such as computer vision, natural language processing, character recognition, robotics, speech recognition, and so on. It is also used in many medical image processing applications and telemedicine. The present work discussed the pattern recognition problem and its various stages in detail. In addition to that, the application of deep learning and machine learning in pattern recognition is also explained briefly.

Artificial Intelligence: The Future of Maxillofacial Prognosis and Diagnosis?

Worldwide, the prevalence of cardiovascular diseases has doubled, demanding new diagnostic tools. Artificial intelligence, especially machine learning and deep learning, offers innovative possibilities for medical research. Despite historical challenges, such as a lack of data, these techniques have potential for cardiovascular research. This thesis explores the application of machine learning and deep learning in cardiology, focusing on automation and decision support in cardiovascular imaging.Part I of this thesis focuses on automating cardiovascular MRI analysis. A deep learning model was developed to analyze the ascending aorta in cardiovascular MRI images. The model's results were used to investigate connections between genetic material and aortic properties, and between aortic properties and cardiovascular diseases and mortality. A second model was developed to select MRI images suitable for analyzing the pulmonary artery.Part II focuses on decision support in nuclear cardiovascular imaging. A first machine learning model was developed to predict myocardial ischemia based on CTA variables. In addition, a deep neural network was used to identify reduced oxygen supply through the arteries supplying oxygen-rich blood to the heart and cardiovascular risk features using PET images.This thesis successfully explores the possibilities of machine learning and deep learning in cardiovascular research, with a focus on automated analysis and decision support.

AI-Derived Left Ventricular Mass From Noncontrast Cardiac CT: Correlation With Contrast CT Angiography and CMR

Artificial intelligence (AI) and deep learning (DL) have shown tremendous potential in driving sustainability across various sectors. This paper reviews recent advancements in AI and DL and explores their applications in achieving sustainable development goals (SDGs), renewable energy, environmental health, and smart building energy management. AI has the potential to contribute to 134 of the 169 targets across all SDGs, but the rapid development of these technologies necessitates comprehensive regulatory oversight to ensure transparency, safety, and ethical standards. In the renewable energy sector, AI and DL have been effectively utilized in optimizing energy management, fault detection, and power grid stability. They have also demonstrated promise in enhancing waste management and predictive analysis in photovoltaic power plants. In the field of environmental health, the integration of AI and DL has facilitated the analysis of complex spatial data, improving exposure modeling and disease prediction. However, challenges such as the explainability and transparency of AI and DL models, the scalability and high dimensionality of data, the integration with next-generation wireless networks, and ethics and privacy concerns need to be addressed. Future research should focus on enhancing the explainability and transparency of AI and DL models, developing scalable algorithms for processing large datasets, exploring the integration of AI with next-generation wireless networks, and addressing ethical and privacy considerations. Additionally, improving the energy efficiency of AI and DL models is crucial to ensure the sustainable use of these technologies. By addressing these challenges and fostering responsible and innovative use, AI and DL can significantly contribute to a more sustainable future.

Artificial intelligence (AI) is transforming cardiovascular imaging (CVI), enhancing accuracy, efficiency, and diagnostic capability across echocardiography (Echo), cardiac computed tomography (CCT), and cardiac magnetic resonance (CMR). In Echo, AI improves image acquisition, segmentation, quantification of chamber function, and detection of wall motion abnormalities, supporting diagnosis and prognosis in various diseases. Automated two-dimensional and three-dimensional (3D) analysis allows rapid, reproducible assessments of ventricular volumes and EF. In valvular heart disease, AI assists in measurement, procedural planning, and integration with 3D printing. CCT benefits from AI at every workflow stage, from image acquisition to disease assessment. AI optimizes scanning protocols, reduces radiation exposure, and enhances coronary artery calcium scoring, plaque analysis, and ischemia evaluation. Algorithms enable rapid segmentation and functional assessment, while ongoing studies support its utility in risk prediction and plaque characterization. In CMR, AI accelerates acquisition, reduces artifacts, and automates segmentation and tissue characterization. Deep learning (DL) models accurately detect fibrosis, scar, and functional parameters, positively influencing prognosis prediction in every cardiac disease. AI-driven tools also streamline report generation, enhance Telemedicine workflow, and guide less experienced users in image acquisition. Despite these advances, challenges remain. Robust and diverse datasets, explainable AI models, regulatory approvals, and ethical considerations are critical for safe and widespread adoption. AI’s “black box” nature hinders clinician trust, making interpretability essential. As these barriers are addressed, AI is expected to become an essential tool in every aspect of CVI, enabling personalized medicine, improving patient care, and optimizing clinical workflows in the coming decades.

Purpose To develop and validate a deep learning (DL) model for automated assessment of coronary stenosis in vessels with heavily calcified plaques at coronary CT angiography (CCTA), using quantitative coronary angiography as the reference standard. Materials and Methods A total of 10 101 CCTA examinations (June 2017-December 2020) from three tertiary hospitals in China were retrospectively collected for DL model development. External testing dataset 1 included 442 CCTA examinations (Agatston score > 300) from two independent hospitals (January 2021-May 2022) for performance evaluation. The separate external testing dataset 2 of 120 CCTA examinations was used for a reader study assessing whether DL assistance improved diagnostic accuracy among junior, attending, and senior radiologists. External testing dataset 3 included 150 prospectively collected CCTA examinations (June-July 2023) that were analyzed to compare model performance against clinical reports, simulating real-world deployment. Model diagnostic performance was assessed using receiver operating characteristic analysis, with quantitative coronary angiography as the reference. Results In external testing dataset 1, specificities for detecting 50% or more stenosis were 78%, 72%, and 48% and the areas under the receiver operating characteristic curve (AUC) were 0.89, 0.90, and 0.87 at the segment, vessel, and patient levels, respectively. In external testing dataset 2, DL assistance improved radiologist specificity by 7%-11% (P < .001) with improving AUC and increased interreader agreement (Δκ = 0.155-0.228; P < .05). In external testing dataset 3, the model demonstrated 53% specificity and a higher AUC versus clinical reports (0.91 vs 0.76; P < .001). Conclusion The proposed DL model accurately detected coronary stenosis of heavily calcified plaques at CCTA and improved diagnostic performance of radiologists. Keywords: CT Angiography, Cardiac, Heart, Arteriosclerosis, Calcifications, Calculi, Quantification, Diagnosis Supplemental material is available for this article. © The Author(s) 2025. Published by the Radiological Society of North America under a CC BY 4.0 license. See also commentary by Maiter and Alabed in this issue.

Objective: The objective of this narrative review was to summarize and critically appraise current clinical applications of artificial intelligence (AI) in cardiology, with particular emphasis on arrhythmia detection and cardiovascular imaging modalities. Methods: A selective narrative review of peer-reviewed literature published between 2015 and 2025 was performed using PubMed, Scopus, and Web of Science. Studies were selected based on clinical relevance, validation methodology, and reported diagnostic or prognostic performance in human populations. Results: AI-based algorithms demonstrate diagnostic performance comparable to expert interpretation in atrial fibrillation (AF) detection as well as across echocardiography, coronary computed tomography angiography (CCTA), cardiac magnetic resonance imaging (CMR), and intravascular optical coherence tomography (OCT). Beyond diagnostic accuracy, AI has been shown to reduce analysis time and interobserver variability in controlled and retrospective study settings. However, the majority of available studies are retrospective, rely on curated datasets, and lack prospective validation demonstrating a direct impact on clinical outcomes. Conclusions: AI constitutes a valuable decision-support tool in contemporary cardiology, enhancing diagnostic efficiency and risk stratification across multiple imaging and electrophysiological modalities. Nevertheless, broader clinical adoption will require rigorous external validation, improved model interpretability, and prospective outcome-driven studies. This review uniquely integrates evidence across electrophysiology and multiple imaging modalities to identify shared translational challenges and near-term clinical opportunities for AI in cardiology.

Artificial intelligence (AI), which includes machine learning and deep learning has been introduced to nursing care in recent years. The present study reviews the following topics: the concepts of AI, machine learning, and deep learning; examples of AI-based nursing research; the necessity of education on AI in nursing schools; and the areas of nursing care where AI is useful. AI refers to an intelligent system consisting not of a human, but a machine. Machine learning refers to computers’ ability to learn without being explicitly programmed. Deep learning is a subset of machine learning that uses artificial neural networks consisting of multiple hidden layers. It is suggested that the educational curriculum should include big data, the concept of AI, algorithms and models of machine learning, the model of deep learning, and coding practice. The standard curriculum should be organized by the nursing society. An example of an area of nursing care where AI is useful is prenatal nursing interventions based on pregnant women’s nursing records and AI-based prediction of the risk of delivery according to pregnant women’s age. Nurses should be able to cope with the rapidly developing environment of nursing care influenced by AI and should understand how to apply AI in their field. It is time for Korean nurses to take steps to become familiar with AI in their research, education, and practice.

Artificial intelligence (AI)-based approaches are crucial in computer-aided diagnosis (CAD) for various medical applications. Their ability to quickly and accurately learn from complex data is remarkable. Deep learning (DL) models have shown promising results in accurately classifying Alzheimer's disease (AD) and its related cognitive states, Early Mild Cognitive Impairment (EMCI) and Late Mild Cognitive Impairment (LMCI), along with the healthy conditions known as Cognitively Normal (CN). This offers valuable insights into disease progression and diagnosis. However, certain traditional machine learning (ML) classifiers perform equally well or even better than DL models, requiring less training data. This is particularly valuable in CAD in situations with limited labeled datasets. In this paper, we propose an ensemble classifier based on ML models for magnetic resonance imaging (MRI) data, which achieved an impressive accuracy of 96.52%. This represents a 3-5% improvement over the best individual classifier. We evaluated popular ML classifiers for AD classification under both data-scarce and data-rich conditions using the Alzheimer's Disease Neuroimaging Initiative and Open Access Series of Imaging Studies datasets. By comparing the results to state-of-the-art CNN-centric DL algorithms, we gain insights into the strengths and weaknesses of each approach. This work will help users to select the most suitable algorithm for AD classification based on data availability.

What's New in Spine Surgery.

PurposeThe first transmission of coronavirus to humans started in Wuhan city of China, took the shape of a pandemic called Corona Virus Disease 2019 (COVID-19), and posed a principal threat to the entire world. The researchers are trying to inculcate artificial intelligence (Machine learning or deep learning models) for the efficient detection of COVID-19. This research explores all the existing machine learning (ML) or deep learning (DL) models, used for COVID-19 detection which may help the researcher to explore in different directions. The main purpose of this review article is to present a compact overview of the application of artificial intelligence to the research experts, helping them to explore the future scopes of improvement.MethodsThe researchers have used various machine learning, deep learning, and a combination of machine and deep learning models for extracting significant features and classifying various health conditions in COVID-19 patients. For this purpose, the researchers have utilized different image modalities such as CT-Scan, X-Ray, etc. This study has collected over 200 research papers from various repositories like Google Scholar, PubMed, Web of Science, etc. These research papers were passed through various levels of scrutiny and finally, 50 research articles were selected.ResultsIn those listed articles, the ML / DL models showed an accuracy of 99% and above while performing the classification of COVID-19. This study has also presented various clinical applications of various research. This study specifies the importance of various machine and deep learning models in the field of medical diagnosis and research.ConclusionIn conclusion, it is evident that ML/DL models have made significant progress in recent years, but there are still limitations that need to be addressed. Overfitting is one such limitation that can lead to incorrect predictions and overburdening of the models. The research community must continue to work towards finding ways to overcome these limitations and make machine and deep learning models even more effective and efficient. Through this ongoing research and development, we can expect even greater advances in the future.

Deep Learning (DL) is an area of artificial intelligence (AI), wherein machine learning (ML) using convolutional neural networks to detect patterns in data and forecast its worth. It excels in tough settings like climate change and can handle sophisticated jobs such as image and speech recognition, natural language processing, and others. Deep learning models have found applications in a variety of fields due to their ability to reflect complicated patterns in data. Deep learning applications have the capacity to analyze and automatically classify large volumes of Internet traffic. Deep learning-based solutions that automate the detection of attacks and address complicated cybersecurity issues are gaining popularity. This study extensively describes the promising uses of deep learning, which is based on many layers of artificial neural networks, in a wide range of security challenges. Before critically and comparatively reviewing state-of-the-art solutions from the literature, we will highlight the main properties of typical deep learning architectures used in cybersecurity applications. Deep learning highlights developing concepts and provides an overview of required resources, such as a general framework and appropriate datasets. The limits of the evaluated works are highlighted, as well as a vision of the present challenges in the field, providing useful insights and best practices for researchers and developers working on related problems.