Review
Peripheral Vascular Disease
Carol Mitchell, Claudia E. Korcarz, James A. Zagzebski, James H. Stein
Summary: Technological advances in ultrasound imaging have significantly improved image quality and resolution, but limitations exist in using carotid IMT measurements in clinical medicine due to historical data and instrument settings. However, standardized instrumentation, presets, image acquisition, and measurements can still make carotid IMT measurements a valuable research tool. Consensus in technical aspects of ultrasound imaging acquisition, processing, and display for blood vessels could lead to more reliable measurements.
Article
Biochemistry & Molecular Biology
Akito Sakanaka, Naoto Katakami, Masahiro Furuno, Hitoshi Nishizawa, Kazuo Omori, Naohiro Taya, Asuka Ishikawa, Shota Mayumi, Moe Inoue, Emiko Tanaka Isomura, Atsuo Amano, Iichiro Shimomura, Eiichiro Fukusaki, Masae Kuboniwa
Summary: This study identified saliva metabolites, including allantoin and 1,5-anhydroglucitol, as important predictors of atherosclerosis in patients with T2D, and demonstrated their diagnostic potential in non-invasive identification of high IMT group.
FRONTIERS IN MOLECULAR BIOSCIENCES
(2022)
Article
Nutrition & Dietetics
Jubo Bhuiya, Yoshitomo Notsu, Hironori Kobayashi, Abu Zaffar Shibly, Abdullah Md. Sheikh, Ryota Okazaki, Kazuto Yamaguchi, Atsushi Nagai, Toru Nabika, Takafumi Abe, Masayuki Yamasaki, Minoru Isomura, Shozo Yano
Summary: Recent evidence suggests that trimethylamine-N-oxide (TMAO), a metabolite of L-carnitine and choline, is linked to atherosclerosis and cardiovascular diseases. However, Japanese people who consume lots of fish, which is high in TMAO, show a low risk of atherosclerosis. This study found that TMAO was not a significant risk factor for atherosclerosis in elderly Japanese people, while a low level of trimethyllysine (TML) might be a potential risk. L-carnitine may be a marker for atherosclerosis in women.
Review
Health Care Sciences & Services
Anna Maria Rychter, Dariusz Naskret, Agnieszka Zawada, Alicja Ewa Ratajczak, Agnieszka Dobrowolska, Iwona Krela-Kazmierczak
Summary: Atherosclerosis is strongly associated with obesity, with carotid intima-media thickness (cIMT) serving as a predictor of cardiovascular events and subclinical atherosclerosis. Behavioral interventions can directly impact cIMT values, influencing cardiovascular disease risk.
JOURNAL OF PERSONALIZED MEDICINE
(2021)
Article
Endocrinology & Metabolism
Vishal Chandra Sharma, Sudha Vidyasagar, Cynthia Amrutha Sukumar, B. Nanda Krishna, Sharanya Shree
Summary: The study evaluated the association between serum OC levels and atherosclerosis in 113 T2DM patients. Results showed a significant negative correlation between serum OC levels and carotid intima-media thickness estimates. CC-IMT was also significantly associated with other biochemical parameters such as fasting blood sugar, glycated hemoglobin, and high-density lipoprotein.
BMC ENDOCRINE DISORDERS
(2023)
Article
Hematology
Matthew J. Feinstein, Margaret F. Doyle, James H. Stein, Colleen M. Sitlani, Alison E. Fohner, Sally A. Huber, Alan L. Landay, Susan R. Heckbert, Kenneth Rice, Richard A. Kronmal, Catherine Hedrick, Ani Manichaikul, Coleen McNamara, Stephen Rich, Russell P. Tracy, Nels C. Olson, Bruce M. Psaty, Joseph A. C. Delaney
Summary: This study investigated the associations of myeloid and lymphoid cell subsets with cardiovascular disease onset and progression, finding that nonclassical monocytes were associated with progression of carotid IMT. Significant sex differences were observed in the associations of monocyte subsets with IMT progression, with different patterns between men and women.
ARTERIOSCLEROSIS THROMBOSIS AND VASCULAR BIOLOGY
(2021)
Article
Nutrition & Dietetics
Esther Gonzalez-Padilla, Suzanne Janzi, Stina Ramne, Camilla Thuneland, Yan Borne, Emily Sonestedt
Summary: This study found no clear association between sugar intake and IMT measurements.
Article
Medicine, Research & Experimental
Mira Merashli, Tommaso Bucci, Alessia Arcaro, Fabrizio Gentile, Paul R. J. Ames
Summary: This study aimed to evaluate the intima media thickness of carotid arteries (IMT) and its correlates in Behcet's disease (BD). A meta-analysis of case control studies found that IMT was greater in BD patients compared to controls. The analysis also showed a higher prevalence of carotid plaques in BD patients. The results suggest that subclinical carotid artery atherosclerosis is a vascular feature of BD. The inverse correlations between IMT, age, and azathioprine use indicate that thicker carotid arteries at disease onset may regress with immune suppressive treatment but further research is needed.
CLINICAL AND EXPERIMENTAL MEDICINE
(2023)
Article
Chemistry, Analytical
Aisha Al-Mohannadi, Somaya Al-Maadeed, Omar Elharrouss, Kishor Kumar Sadasivuni
Summary: This study proposed a method for early diagnosis of cardiovascular diseases using deep learning techniques, by applying semantic segmentation and calculating cIMT measurement. The encoder-decoder model with multi-image inputs overcame the issue of dataset scarcity, and the experimental results demonstrated the effectiveness of the proposed architecture.
Article
Biochemistry & Molecular Biology
Daniela Coggi, Beatrice Frigerio, Alice Bonomi, Massimiliano Ruscica, Nicola Ferri, Daniela Sansaro, Alessio Ravani, Palma Ferrante, Manuela Damigella, Fabrizio Veglia, Nicolo Capra, Maria Giovanna Lupo, Chiara Macchi, Kai Savonen, Angela Silveira, Sudhir Kurl, Philippe Giral, Matteo Pirro, Rona Juliette Strawbridge, Bruna Gigante, Andries Jan Smit, Elena Tremoli, Mauro Amato, Damiano Baldassarre
Summary: In individuals asymptomatic for cardiovascular diseases, PCSK9 plasma levels do not correlate with vascular damage and/or subclinical atherosclerosis of extracranial carotid arteries.
Article
Biochemistry & Molecular Biology
Po-Chih Lin, Chung-Yen Chen, Charlene Wu, Ta-Chen Su
Summary: This study investigated subclinical carotid atherosclerosis in patients with familial hypercholesterolemia (FH) and their family members, and found that systemic inflammation synergistically contributed to atherogenic dyslipidemia on subclinical atherosclerosis.
Article
Cardiac & Cardiovascular Systems
Nestor S. Martins, Joaquim Barreto, Sheila Tatsumi Kimura-Medorima, Sofia Helena Vitte, Thiago Quinaglia, Barbara Assato, Otavio Rizzi Coelho-Filho, Jose Roberto Matos-Souza, Wilson Nadruz, Andrei C. Sposito
Summary: This study found that in individuals with type 2 diabetes, carotid intimal thickness (cIT) is a better predictor of coronary artery calcification (CAC) than carotid intima-media thickness (cIMT), and carotid media thickness (cM) is not associated with CAC.
NUTRITION METABOLISM AND CARDIOVASCULAR DISEASES
(2023)
Article
Computer Science, Artificial Intelligence
Weichuan Zhang, Changming Sun
Summary: This study evaluates the capability of second-order generalized Gaussian directional derivative filters in suppressing Gaussian noise and explores the properties of corners and edges to propose a new image corner detection method. Experimental results demonstrate that the proposed detector outperforms other tested detectors.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2021)
Article
Clinical Neurology
Huaguang Zheng, Hongwei Li, Yilong Wang, Zhanquan Li, Bo Hu, Xiaogang Li, Lu Fu, Hongtao Hu, Zhiyu Nie, Bilian Zhao, Di Wei, Bjorn W. Karlson, Michiel L. Bots, XiangWen Meng, Yundai Chen, Yongjun Wang
Summary: This study demonstrates that rosuvastatin significantly reduces the progression of CIMT in Chinese adults with subclinical atherosclerosis and is well tolerated. The findings contribute to a deeper understanding of atherosclerosis and provide guidance for clinical practice.
Article
Endocrinology & Metabolism
J. J. Drinkwater, F. K. Chen, A. M. Brooks, B. T. Davis, A. W. Turner, T. M. E. Davis, W. A. Davis
Summary: This study found an independent association between macrovascular disease and arterial stiffness with diabetic retinopathy in patients with type 2 diabetes. The occurrence of diabetic retinopathy is closely related to patients' age at diagnosis, blood glucose control level, treatment method, and cardiovascular health.
Article
Computer Science, Interdisciplinary Applications
Maria Matsangidou, Fotos Frangoudes, Eirini Schiza, Kleanthis C. Neokleous, Ersi Papayianni, Katerian Xenari, Marios Avraamides, Constantinos S. Pattichis
Summary: This study confirms the significant role of virtual reality in improving physical training and emotional health of dementia patients when appropriately designed. The study also highlights four key factors that should be incorporated in a virtual reality system.
Article
Acoustics
Ali Abbasian Ardakani, Afshin Mohammadi, Mohammad Mirza-Aghazadeh-Attari, Fariborz Faeghi, Thomas J. Vogl, U. Rajendra Acharya
Summary: In this study, a deep learning model called ClymphNet was developed to differentiate malignant and benign lymph nodes in patients with papillary thyroid cancer. The model outperformed conventional machine learning algorithms in both internal and external validation, providing evidence of its utility in early and accurate differentiation of lymphadenopathy.
JOURNAL OF ULTRASOUND IN MEDICINE
(2023)
Article
Computer Science, Artificial Intelligence
Gulay Tasci, Hui Wen Loh, Prabal Datta Barua, Mehmet Baygin, Burak Tasci, Sengul Dogan, Turker Tuncer, Elizabeth Emma Palmer, Ru-San Tan, U. Rajendra Acharya
Summary: This study presents a computationally lightweight handcrafted classification model for accurate detection of major depressive disorder (MDD) using electroencephalogram (EEG) signals. The model extracts local textural features and statistical features from the raw EEG signal and applies feature selection and classification algorithms to optimize the model. The generated model achieves high accuracies and outperforms other models developed using the same dataset.
KNOWLEDGE-BASED SYSTEMS
(2023)
Editorial Material
Computer Science, Information Systems
Marios S. Pattichis, Scott T. Acton, Constantinos S. Pattichis, Andreas S. Panayides
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2023)
Article
Automation & Control Systems
Amanpreet Singh, Ali Abbasian Ardakani, Hui Wen Loh, P. V. Anamika, U. Rajendra Acharya, Sidharth Kamath, Anil K. Bhat
Summary: The objective of this study was to develop a high-performing deep-learning model using only plain wrist radiographs to detect apparent and non-apparent occult scaphoid fractures. A CNN-based model was developed and achieved good performance in two-class and three-class classification, with high sensitivity, specificity, accuracy, and AUC values. The model also utilized gradient-weighted class activation mapping for fracture localization.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Automation & Control Systems
Prabal Datta Barua, Arif Metehan Yildiz, Nida Canpolat, Tugce Keles, Sengul Dogan, Mehmet Baygin, Ilknur Tuncer, Turker Tuncer, Ru-San Tan, Hamido Fujita, U. Rajendra Acharya
Summary: Speaker counting is an important research area in sound forensics. This work aims to collect a new overlapping speech signal dataset for speaker counting and proposes a novel feature engineering model. A new framework that mimics the deep learning model has been proposed to classify the collected speech classes, achieving a classification accuracy of 86.74% using the symlet4 mother wavelet function.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Engineering, Biomedical
Ela Kaplan, Mehmet Baygin, Prabal D. Barua, Sengul Dogan, Turker Tuncer, Erman Altunisik, Elizabeth Emma Palmer, U. Rajendra Acharya
Summary: The purpose of this study is to classify the neuroradiological features of patients with Alzheimer's disease (AD) using an automatic hand-modeled method with high accuracy. The proposed model, ExHiF, uses feature extraction, feature selection, and multiple classifiers to achieve the classification. The results show that the ExHiF model achieves 100% classification accuracy for AD patients using two datasets.
MEDICAL ENGINEERING & PHYSICS
(2023)
Article
Computer Science, Artificial Intelligence
M. Murugappan, Ali K. K. Bourisly, N. B. Prakash, M. G. Sumithra, U. Rajendra Acharya
Summary: This work aims to develop a computationally efficient and robust deep learning model for lung segmentation using chest CT images. The DeepLabV3+ network with ResNet-18 and a batch size of 8 performs better for two-class segmentation, while the DeepLabV3+ network with ResNet-50 and a batch size of 16 yields better results for four-class segmentation. Additionally, the ResNet model with a fewer number of layers has lower computational complexity and is highly adequate for developing a more robust lung segmentation network compared to the conventional DeepLabV3+ network with Xception.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Biology
U. Raghavendra, Anjan Gudigar, Aritra Paul, T. S. Goutham, Mahesh Anil Inamdar, Ajay Hegde, Aruna Devi, Chui Ping Ooi, Ravinesh C. Deo, Prabal Datta Barua, Filippo Molinari, Edward J. Ciaccio, U. Rajendra Acharya
Summary: A brain tumor is an abnormal mass inside the skull that can lead to significant health problems by putting pressure on the brain. Early detection of these tumors is crucial as malignant brain tumors grow rapidly and can result in higher mortality rates. Computer-aided diagnostic systems, combined with artificial intelligence techniques, play a vital role in the early detection of this disorder. This review highlights the challenges faced by CAD systems based on different modalities, current requirements in this field, and future prospects in research.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Biology
Manish Sharma, Sarv Verma, Divyansh Anand, Vikram M. Gadre, U. Rajendra Acharya
Summary: The Cyclic Alternating Pattern (CAP) is a physiological marker of sleep instability and can examine various sleep-related disorders. The study proposes a novel WSN-based CAPSCNet for automatically detecting specific events (A-phases) during sleep. The model achieves high classification accuracy in healthy subjects and patients with different sleep disorders.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Biology
Oh Shu Lih, V. Jahmunah, Elizabeth Emma Palmer, Prabal D. Barua, Sengul Dogan, Turker Tuncer, Salvador Garcia, Filippo Molinari, U. Rajendra Acharya
Summary: Epilepsy is a common neurological condition that requires a rapid and accurate diagnosis. This study proposes an automated system using deep learning to detect and monitor epilepsy using a large database. The results show high classification accuracy and the potential for transformative impact on neurological diagnostics worldwide.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Suat Kamil Sut, Mustafa Koc, Gokhan Zorlu, Ihsan Serhatlioglu, Prabal Datta Barua, Sengul Dogan, Mehmet Baygin, Turker Tuncer, Ru-San Tan, U. Rajendra Acharya
Summary: A new handcrafted machine learning method has been developed for the automated and accurate classification of adrenal gland CT images. The method analyzed a dataset of 759 CT image slices from 96 subjects, and achieved high accuracy in classification using k-nearest neighbor, support vector machine, and neural network classifiers.
JOURNAL OF DIGITAL IMAGING
(2023)
Article
Computer Science, Information Systems
Aditya Wadichar, Shruti Murarka, Dhruvi Shah, Ankit Bhurane, Manish Sharma, Hasan S. Mir, U. Rajendra Acharya
Summary: Sleep is essential for health, but sleep disorders can degrade sleep quality. This study proposes a hierarchical approach to automatically detect sleep disorders and classify sleep quality using EEG data from the CAP sleep database. The proposed method achieved high accuracy using long short-term memory and convolutional neural networks.
Article
Computer Science, Information Systems
Mahesh Anil Inamdar, U. Raghavendra, Anjan Gudigar, Sarvesh Bhandary, Massimo Salvi, Ravinesh C. Deo, Prabal Datta Barua, Edward J. Ciaccio, Filippo Molinari, U. Rajendra Acharya
Summary: This paper presents an efficient gland segmentation model using digital histopathology and deep learning, which has the potential to revolutionize medicine. The study aims to develop an automated method for segmenting histopathological images of human prostate glands and compare it with other techniques, showing that our method performs better in segmentation task.
Article
Computer Science, Artificial Intelligence
Sinan Tatli, Gulay Macin, Irem Tasci, Burak Tasci, Prabal Datta Barua, Mehmet Baygin, Turker Tuncer, Sengul Dogan, Edward J. Ciaccio, U. Rajendra Acharya
Summary: This study aims to propose a new algorithm for early diagnosis of multiple sclerosis (MS) using machine learning. The algorithm utilizes transfer learning and hybrid feature engineering, and calculates feature vectors through multiple layers of neural networks, resulting in high classification accuracy.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
