Article
Biochemistry & Molecular Biology
Pang-Shuo Huang, Yu-Heng Tseng, Chin-Feng Tsai, Jien-Jiun Chen, Shao-Chi Yang, Fu-Chun Chiu, Zheng-Wei Chen, Juey-Jen Hwang, Eric Y. Chuang, Yi-Chih Wang, Chia-Ti Tsai
Summary: The use of artificial intelligence (AI) with electrocardiograms (ECGs) can identify significant coronary artery disease (CAD) and determine the site of the coronary obstruction. This technology can be easily implemented in health check-ups to identify high-risk patients for future coronary events.
Review
Computer Science, Artificial Intelligence
Jia-Chi Wang, Yi-Chung Shu, Che-Yu Lin, Wei-Ting Wu, Lan-Rong Chen, Yu-Cheng Lo, Hsiao-Chi Chiu, Levent Ozcakar, Ke-Vin Chang
Summary: This study aimed to explore and summarize the performance of deep learning algorithms in the automatic sonographic assessment of the median nerve at the carpal tunnel level. The results showed that the deep learning algorithm enables automated localization and segmentation of the median nerve in ultrasound imaging with acceptable accuracy and precision. Future research should validate the performance of deep learning algorithms in detecting and segmenting the median nerve along its entire length and across datasets obtained from various ultrasound manufacturers.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2023)
Article
Cardiac & Cardiovascular Systems
Changho Han, Ki-Woon Kang, Tae Young Kim, Jae-Sun Uhm, Je-Wook Park, In Hyun Jung, Minkwan Kim, SungA Bae, Hong-Seok Lim, Dukyong Yoon
Summary: This study aimed to predict coronary artery calcium (CAC) using electrocardiograms (ECGs), addressing the limitations of CAC measurement. Deep convolutional neural network models were constructed using raw ECG waveforms as input. The models performed well in both internal and external validation datasets, with better performance in predicting higher CAC scores, suggesting a correlation between higher CAC scores and more prominent structural changes of the heart.
FRONTIERS IN CARDIOVASCULAR MEDICINE
(2022)
Article
Medicine, General & Internal
Jordan Chamberlin, Madison R. Kocher, Jeffrey Waltz, Madalyn Snoddy, Natalie F. C. Stringer, Joseph Stephenson, Pooyan Sahbaee, Puneet Sharma, Saikiran Rapaka, U. Joseph Schoepf, Andres F. Abadia, Jonathan Sperl, Phillip Hoelzer, Megan Mercer, Nayana Somayaji, Gilberto Aquino, Jeremy R. Burt
Summary: The study demonstrates that an AI prototype can rapidly and accurately identify significant risk factors for cardiopulmonary disease on low-dose chest CT scans, improving diagnostic ability and prognosis prediction. This has the potential to facilitate intervention, improve patient outcomes, and reduce healthcare costs.
Article
Multidisciplinary Sciences
Takeshi Shimizu, Yoshihiro Sasaki, Kei Ito, Masashi Matsuzaka, Hirotake Sakuraba, Shinsaku Fukuda
Summary: This study developed a deep learning-based classification model for colorectal neoplastic lesions and achieved high accuracy in diagnosing low-grade dysplasia, high-grade dysplasia or mucosal carcinoma, superficially invasive submucosal carcinoma, and deeply invasive submucosal carcinomas.
SCIENTIFIC REPORTS
(2023)
Article
Cardiac & Cardiovascular Systems
Michal Cohen-Shelly, Zachi Attia, Paul A. Friedman, Saki Ito, Benjamin A. Essayagh, Wei-Yin Ko, Dennis H. Murphree, Hector Michelena, Maurice Enriquez-Sarano, Rickey E. Carter, Patrick W. Johnson, Peter A. Noseworthy, Francisco Lopez-Jimenez, Jae K. Oh
Summary: The study showed that AI-ECG has a certain degree of accuracy and reliability in identifying patients with moderate to severe AS, especially showing differences among patients of different ages and genders. For patients with false-positive AI-ECGs, the risk of developing moderate or severe AS within the next 15 years is twice that of true negative AI-ECGs.
EUROPEAN HEART JOURNAL
(2021)
Article
Cardiac & Cardiovascular Systems
Michal Cohen-Shelly, Zachi Attia, Paul A. Friedman, Saki Ito, Benjamin A. Essayagh, Wei-Yin Ko, Dennis H. Murphree, Hector Michelena, Maurice Enriquez-Sarano, Rickey E. Carter, Patrick W. Johnson, Peter A. Noseworthy, Francisco Lopez-Jimenez, Jae K. Oh
Summary: The study aimed to develop an artificial intelligence-enabled electrocardiogram (AI-ECG) to identify patients with moderate to severe aortic stenosis (AS). The AI-ECG showed promising results in detecting AS, especially in a community screening setting.
EUROPEAN HEART JOURNAL
(2021)
Article
Chemistry, Multidisciplinary
Mustapha Mukhtar, Ariyo Oluwasanmi, Nasser Yimen, Zhang Qinxiu, Chiagoziem C. Ukwuoma, Benjamin Ezurike, Olusola Bamisile
Summary: This study develops two novel hybrid neural network models for accurate prediction of global solar radiation. Compared with traditional artificial neural network models, the hybrid models show better performance in different countries across Africa. The results of this study are of great significance for finding more accurate methods of solar radiation estimation.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
Mosleh Hmoud Al-Adhaileh
Summary: This research applied deep learning techniques, specifically AlexNet and Restnet50, for the classification and recognition of Alzheimer's disease (AD) using brain MRI images. The proposed method outperformed existing systems in terms of detection accuracy, with the AlexNet model achieving outstanding performance based on five evaluation metrics.
Review
Dermatology
Lloyd Steele, Xiang Li Tan, Bayanne Olabi, Jing Mia Gao, Reiko J. J. Tanaka, Hywel C. C. Williams
Summary: Machine learning models for skin cancer recognition have varying performance across different skin phototypes and skin cancer types. Our study aims to assess whether studies have reported results separately for different skin phototypes and rarer skin cancers, and to graphically represent the skin cancer training datasets used by current ML models.
JOURNAL OF THE EUROPEAN ACADEMY OF DERMATOLOGY AND VENEREOLOGY
(2023)
Article
Computer Science, Information Systems
N. Sasikaladevi
Summary: Deep learning, as an automatic and accurate model for image classification, shows potential in plant pathology diagnosis. This paper proposes a deep convolutional neural network model based on Hypergraph modeling for the accurate and rapid identification of plant disease, and experimental results prove its superiority.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Xisheng Ding, Junqi Yu
Summary: This study designed and built a convolutional neural network (CNN) on an embedded building lighting control system to investigate its impact on image recognition accuracy and energy consumption reduction. The experiment results showed that the CNN-based control system achieved high image recognition accuracy and achieved approximately 40% energy savings compared to the system without intelligent control.
Article
Biology
V. Jahmunah, E. Y. K. Ng, Tan Ru San, U. Rajendra Acharya
Summary: In this study, an automated system was developed using convolutional neural network (CNN) and unique GaborCNN models for categorizing ECG signals into normal, CAD, MI, and CHF classes. High classification accuracies exceeding 98.5% were achieved by the CNN and GaborCNN models, with GaborCNN being preferred for its performance and reduced complexity. This study is the first to propose using GaborCNN for automated categorization of ECG signals into normal, CAD, MI, and CHF classes.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Agriculture, Multidisciplinary
Jaemyung Shin, Young K. Chang, Brandon Heung, Tri Nguyen-Quang, Gordon W. Price, Ahmad Al-Mallahi
Summary: This study utilized Deep Learning models to detect powdery mildew in strawberries, optimizing well-established learners and performing data augmentation to prevent overfitting. The six DL algorithms used showed an average classification accuracy of over 92%, with ResNet-50 performing the best in classification.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2021)
Article
Computer Science, Artificial Intelligence
Chenxi Huang, Jian Wang, Shui-Hua Wang, Yu-Dong Zhang
Summary: Brain diseases are a serious threat worldwide. Artificial intelligence, especially deep learning technologies, has been employed to assist in brain disease studies, including preprocessing and clinical applications. This survey reviews over one hundred representative papers and introduces the methods and future trends of applying AI to brain disease studies.
Editorial Material
Cardiac & Cardiovascular Systems
Marly van Assen, Alexander C. Razavi, Seamus P. Whelton, Carlo N. De Cecco
EUROPEAN HEART JOURNAL
(2023)
Editorial Material
Radiology, Nuclear Medicine & Medical Imaging
Carlo N. De Cecco, Marly van Assen
Article
Clinical Neurology
Frans Kauw, Birgitta K. Velthuis, Richard A. P. Takx, Marco Guglielmo, Maarten J. Cramer, Fasco van Ommen, Anneloes Bos, Edwin Bennink, L. Jaap Kappelle, Hugo W. A. M. de Jong, Jan W. Dankbaar
Summary: Identifying cardioembolic sources in patients with acute ischemic stroke is important for the choice of secondary prevention strategies. This study aimed to investigate the yield of admission cardiac computed tomography angiography (CTA) in detecting cardiac thrombi and determining cardioembolic sources in stroke patients. The results showed that the presence of cardiac thrombus on admission CTA was associated with more severe strokes, higher clot burden, and a higher likelihood of undergoing endovascular treatment. The use of spectral iodine maps on CTA increased the diagnostic certainty for left atrial appendage thrombus.
Article
Cardiac & Cardiovascular Systems
Andrea Baggiano, Edoardo Conte, Luigi Spiritigliozzi, Saima Mushtaq, Andrea Annoni, Maria Ludovica Carerj, Francesco Cilia, Fabio Fazzari, Alberto Formenti, Antonio Frappampina, Laura Fusini, Margherita Gaudenzi Asinelli, Daniele Junod, Maria Elisabetta Mancini, Valentina Mantegazza, Riccardo Maragna, Francesca Marchetti, Marco Penso, Luigi Tassetti, Alessandra Volpe, Francesca Baessato, Marco Guglielmo, Alexia Rossi, Chiara Rovera, Daniele Andreini, Mark G. Rabbat, Andrea Igoren Guaricci, Mauro Pepi, Gianluca Pontone
Summary: The aim of this study was to test the diagnostic accuracy of extracellular volume (ECV) estimated through cardiac computed tomography (CCT) in patients with a recent diagnosis of dilated cardiomyopathy. The results showed that ECV estimation using a whole-heart single source, single energy CT scanner is feasible and accurate. This method can be integrated into the comprehensive evaluation of patients with newly diagnosed dilated cardiomyopathy with a small increase in overall radiation exposure.
JOURNAL OF CARDIOVASCULAR COMPUTED TOMOGRAPHY
(2023)
Editorial Material
Cardiac & Cardiovascular Systems
Gianluca Pontone, Saima Mushtaq, Subhi J. Al'Aref, Daniele Andreini, Andrea Baggiano, Arzu Canan, Joao L. Cavalcante, Anjali Chelliah, Marcus Chen, Andrew Choi, Dey Damini, Carlo Nicola De Cecco, Kanwal M. Farooqi, Maros Ferencik, Gudrun Feuchtner, Harvey Hecht, Heidi Gransar, Marton Kolossv, Jonathon Leipsic, Michael T. Lu, Mohamed Marwan, Ming-Yen Ng, Pal Maurovich-Horvat, Prashant Nagpal, Ed Nicol, Jonathan Weir-McCall, Seamus P. Whelton, Michelle C. Williams, Anna Reid, Timothy A. Fairbairn, Todd Villines, Rosemarie Vliegenthart, Armin Arbab-Zadeh
Summary: This review provides a summary of key articles published in the Journal of Cardiovascular Computed Tomography (JCCT) in 2022, focusing on those with significant scientific and educational impact. The JCCT has experienced growth in terms of submissions, published manuscripts, citations, downloads, social media presence, and impact factor. The selected articles in this review highlight the role of cardiovascular computed tomography (CCT) in detecting subclinical atherosclerosis, assessing stenosis relevance, and planning invasive procedures. It also includes sections on CCT in infants, patients with congenital heart disease, women, and the importance of CT training. Additionally, important consensus documents and guidelines published in JCCT last year are highlighted.
JOURNAL OF CARDIOVASCULAR COMPUTED TOMOGRAPHY
(2023)
Article
Cardiac & Cardiovascular Systems
Saima Mushtaq, Gianluca Pontone, Edoardo Conte, Daniela Trabattoni, Stefano Galli, Sebastiano Gili, Sarah Troiano, Giovanni Teruzzi, Andrea Baggiano, Alice Bonomi, Vincenzo Mallia, Davide Marchetti, Matteo Schillaci, Eleonora Melotti, Marta Belmonte, Andrea Igoren Guaricci, Carlo Gigante, Mauro Pepi, Antonio L. Bartorelli, Daniele Andreini
Summary: This study demonstrates that detection of a subendocardial perfusion defect is significantly more accurate than detection of a transmural defect in identifying coronary territories with ISR or CAD progression.
JOURNAL OF CARDIOVASCULAR COMPUTED TOMOGRAPHY
(2023)
Article
Medicine, General & Internal
Paolo Basile, Andrea Igoren Guaricci, Giuseppina Piazzolla, Sara Volpe, Alfredo Vozza, Marina Benedetto, Maria Cristina Carella, Daniela Santoro, Francesco Monitillo, Andrea Baggiano, Saima Mushtaq, Laura Fusini, Fabio Fazzari, Cinzia Forleo, Nunziata Ribecco, Gianluca Pontone, Carlo Sabba, Marco Matteo Ciccone
Summary: GLP-1 receptor agonists (GLP-1 RAs) are effective medications for treating type 2 diabetes and atherosclerotic cardiovascular disease. An observational study showed that treatment with GLP-1 RAs for 6 months improved left ventricular global longitudinal strain in diabetic patients. Further research is needed to confirm these preliminary findings.
JOURNAL OF CLINICAL MEDICINE
(2023)
Review
Medicine, General & Internal
Francesco Perone, Marco Guglielmo, Michele Coceani, Lucia La Mura, Ilaria Dentamaro, Jolanda Sabatino, Alessia Gimelli
Summary: Acute aortic syndromes are life-threatening conditions characterized by acute wall damage and possible progression to aortic rupture. Accurate and timely diagnosis is crucial to avoid catastrophic consequences, as misdiagnosis can result in premature death. Cardiovascular imaging, including echocardiography, computed tomography, magnetic resonance imaging, and aortography, plays a vital role in the diagnosis, immediate treatment, and detection of complications related to acute aortic syndromes. Multimodality imaging is essential in the diagnostic work-up to confirm or rule out these syndromes.
Review
Medicine, General & Internal
Luca Bergamaschi, Anna Giulia Pavon, Francesco Angeli, Domenico Tuttolomondo, Marta Belmonte, Matteo Armillotta, Angelo Sansonetti, Alberto Foa, Pasquale Paolisso, Andrea Baggiano, Saima Mushtaq, Giulia De Zan, Serena Carriero, Maarten-Jan Cramer, Arco J. Teske, Lysette Broekhuizen, Ivo van der Bilt, Giuseppe Muscogiuri, Sandro Sironi, Laura Anna Leo, Nicola Gaibazzi, Luigi Lovato, Gianluca Pontone, Carmine Pizzi, Marco Guglielmo
Summary: Coronary artery disease (CAD) is a major cause of mortality and morbidity globally. Guidelines recommend a multimodal imaging approach for evaluating patients with suspected CAD. Non-invasive imaging methods, such as coronary computed tomography angiography (CCTA) and stress testing, can assess coronary anatomy and inducible myocardial ischemia. Recent trials have challenged previous management concepts, highlighting the importance of understanding the limitations and strengths of each imaging method and integrating anatomical and functional information for personalized treatment. This review provides an overview of non-invasive imaging modalities for comprehensive management of chronic coronary syndromes (CCS) patients.
Review
Cardiac & Cardiovascular Systems
Francesca Baessato, Laura Fusini, Manuela Muratori, Gloria Tamborini, Sarah Ghulam Ali, Valentina Mantegazza, Andrea Baggiano, Saima Mushtaq, Mauro Pepi, Giuseppe Patti, Gianluca Pontone
Summary: Quantification of chronic mitral regurgitation is crucial for patient management and determining the need for mitral valve surgery. Echocardiography is the preferred imaging modality, but cardiac magnetic resonance (CMR) provides higher accuracy. Echocardiography remains essential for pre-operative anatomical evaluation.
JOURNAL OF CARDIOVASCULAR DEVELOPMENT AND DISEASE
(2023)
Review
Medicine, General & Internal
Fabio Fazzari, Andrea Baggiano, Laura Fusini, Sarah Ghulam Ali, Paola Gripari, Daniele Junod, Maria Elisabetta Mancini, Riccardo Maragna, Saima Mushtaq, Gianluca Pontone, Mauro Pepi, Manuela Muratori
Summary: Biological valve failure (BVF) is a condition that compromises the durability of biological heart valves, resulting in valve dysfunction. Diagnostic modalities such as echocardiography, cardiac computed tomography, cardiac magnetic resonance, and nuclear imaging play vital roles in diagnosing BVF. Early detection and appropriate management of BVF are crucial for improving patient prognosis and overall quality of life. Timely intervention and tailored treatments can alleviate the burden of BVF on patients and enhance medical professionals' ability to manage the condition effectively.
JOURNAL OF CLINICAL MEDICINE
(2023)
Article
Biochemistry & Molecular Biology
Marta Zara, Andrea Baggiano, Patrizia Amadio, Jeness Campodonico, Sebastiano Gili, Andrea Annoni, Gianluca De Dona, Maria Ludovica Carerj, Francesco Cilia, Alberto Formenti, Laura Fusini, Cristina Banfi, Paola Gripari, Calogero Claudio Tedesco, Maria Elisabetta Mancini, Mattia Chiesa, Riccardo Maragna, Francesca Marchetti, Marco Penso, Luigi Tassetti, Alessandra Volpe, Alice Bonomi, Giancarlo Marenzi, Gianluca Pontone, Silvia Stella Barbieri
Summary: This study found that the expression of CD41-CD61 in sEVs can reflect the CMR-assessed ischemic damage after STEMI, providing a new strategy for the timely identification of high-risk patients and their treatment optimization.
Article
Pharmacology & Pharmacy
N. M. Bogari, R. M. Allam, A. Dannoun, M. Athar, A. Bouazzaoui, O. Elkhateeb, M. Poraueddu, S. A. Amer, A. Elsayed, G. I. Colombo
Summary: This study investigates the relationship between genetic variation and biological function on a genomic scale, focusing on the rs2383206 gene and its association with the development of coronary artery disease (CAD) in a Saudi population. The study found a higher prevalence of the GG genotype in rs2383206 among CAD patients compared to controls, suggesting an increased risk for CAD. The findings highlight the potential of this genetic variant as a target for future functional studies.
EUROPEAN REVIEW FOR MEDICAL AND PHARMACOLOGICAL SCIENCES
(2023)
Review
Medicine, General & Internal
Willem Gerrits, Ibrahim Danad, Birgitta Velthuis, Saima Mushtaq, Maarten J. Cramer, Pim van der Harst, Frebus J. van Slochteren, Mathias Meine, Dominika Sucha, Marco Guglielmo
Summary: Research has shown that using cardiac computed tomography (CT) for patient selection and guided placement in cardiac resynchronization therapy (CRT) can improve the treatment response rate.
JOURNAL OF CLINICAL MEDICINE
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Gianluca Pontone, Alexia Rossi, Andrea Baggiano, Daniele Andreini, Edoardo Conte, Laura Fusini, Chaterine Gebhard, Mark G. Rabbat, Andrea Guaricci, Marco Guglielmo, Giuseppe Muscogiuri, Saima Mushtaq, Mouaz H. Al-Mallah, Daniel S. Berman, Matthew J. Budoff, Filippo Cademartiri, Kavitha Chinnaiyan, Jung Hyun Choi, Eun Ju Chun, Pedro de Araujo Goncalves, Ilan Gottlieb, Martin Hadamitzky, Yong Jin Kim, Byoung Kwon Lee, Sang-Eun Lee, Erica Maffei, Hugo Marques, Habib Samady, Sanghoon Shin, Ji Min Sung, Alexander van Rosendael, Renu Virmani, Jeroen J. Bax, Jonathon A. Leipsic, Fay Y. Lin, James K. Min, Jagat Narula, Leslee J. Shaw, Hyuk-Jae Chang
Summary: This study aimed to develop and validate a practical CCTA risk score to predict medium-term disease progression in patients at a low-to-intermediate probability of CAD.
EUROPEAN RADIOLOGY
(2023)