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Radiology. Cardiothoracic Imaging Jun 2024Purpose To investigate the ability of kilovolt-independent (hereafter, kV-independent) and tin filter spectral shaping to accurately quantify the coronary artery calcium... (Comparative Study)
Comparative Study
Purpose To investigate the ability of kilovolt-independent (hereafter, kV-independent) and tin filter spectral shaping to accurately quantify the coronary artery calcium score (CACS) and radiation dose reductions compared with the standard 120-kV CT protocol. Materials and Methods This prospective, blinded reader study included 201 participants (mean age, 60 years ± 9.8 [SD]; 119 female, 82 male) who underwent standard 120-kV CT and additional kV-independent and tin filter research CT scans from October 2020 to July 2021. Scans were reconstructed using a Qr36f kernel for standard scans and an Sa36f kernel for research scans simulating artificial 120-kV images. CACS, risk categorization, and radiation doses were compared by analyzing data with analysis of variance, Kruskal-Wallis test, Mann-Whitney test, Bland-Altman analysis, Pearson correlations, and κ analysis for agreement. Results There was no evidence of differences in CACS across standard 120-kV, kV-independent, and tin filter scans, with median CACS values of 1 (IQR, 0-48), 0.6 (IQR, 0-58), and 0 (IQR, 0-51), respectively ( = .85). Compared with standard 120-kV scans, kV-independent and tin filter scans showed excellent correlation in CACS values ( = 0.993 and = 0.999, respectively), with high agreement in CACS risk categorization (κ = 0.95 and κ = 0.93, respectively). Standard 120-kV scans had a mean radiation dose of 2.09 mSv ± 0.84, while kV-independent and tin filter scans reduced it to 1.21 mSv ± 0.85 and 0.26 mSv ± 0.11, cutting doses by 42% and 87%, respectively ( < .001). Conclusion The kV-independent and tin filter research CT acquisition techniques showed excellent agreement and high accuracy in CACS estimation compared with standard 120-kV scans, with large reductions in radiation dose. CT, Cardiac, Coronary Arteries, Radiation Safety, Coronary Artery Calcium Score, Radiation Dose Reduction, Low-Dose CT Scan, Tin Filter, kV-Independent © RSNA, 2024.
Topics: Humans; Middle Aged; Female; Male; Radiation Dosage; Prospective Studies; Coronary Artery Disease; Coronary Vessels; Tomography, X-Ray Computed; Vascular Calcification; Tin; Aged; Coronary Angiography; Reproducibility of Results
PubMed: 38934769
DOI: 10.1148/ryct.230246 -
Heliyon Jun 2024As a paradigm shift in tandem with the expansion of ICT, smart electronic health systems hold great promise for enhancing healthcare delivery and illness prevention...
As a paradigm shift in tandem with the expansion of ICT, smart electronic health systems hold great promise for enhancing healthcare delivery and illness prevention efforts. These systems acquire an in-depth understanding of patient health states through the real-time collection and analysis of medical data enabled by the Internet of Things (IoT) and machine learning. With the widespread use of cutting-edge artificial intelligence and machine learning techniques, predictive analytics in medicine can assist in making the shift from a reactive to a proactive healthcare strategy. With the ability to rapidly and precisely evaluate massive amounts of data, draw intelligent conclusions, and solve difficult issues, artificial neural networks could revolutionize several industries. Two cardiac illnesses were assessed in this study using a multilayer perceptron artificial neural network that incorporated a genetic algorithm and an error-back propagation mechanism. The ability of artificial neural networks to handle consecutive time series data is crucial for optimizing resources in smart electronic health systems, especially with the increasing volume of patient information and the broad use of electronic clinical records. This requires the creation of more accurate predictive models. Through the use of Internet of Things (IoT) sensors, the proposed system gathers data, which is then used to do predictive analytics on patient history-related electronic clinical data saved in the cloud. A smart healthcare system that uses Mu-LTM (multidirectional long-term memory) to accurately monitor and predict the risk of heart disease has a coverage error of 97.94 %, an accuracy of 97.89 %, a sensitivity of 97.96 %, and a specificity of 97.99 %. In comparison to other smart heart disease prediction systems, the F1-score of 97.95 % and precision of 97.71 % is very good.
PubMed: 38933933
DOI: 10.1016/j.heliyon.2024.e32090 -
Cureus May 2024Trisomy 21 often leads to cardiac complications, usually associated with congenital heart disease, such as atrial septal defects, ventricular septal defects, and patent...
Trisomy 21 often leads to cardiac complications, usually associated with congenital heart disease, such as atrial septal defects, ventricular septal defects, and patent ductus arteriosus. This case describes an unexpected instance of infective endocarditis (IE) in a middle-aged patient with an incidentally discovered patent foramen ovale (PFO). The common risk factors for IE include previous valve surgery, artificial heart valves, pacemakers, prior IE, congenital defects like bicuspid aortic valve, IV drug use, and the congenital defects mentioned earlier.
PubMed: 38933636
DOI: 10.7759/cureus.61106 -
Frontiers in Physiology 2024Fetal heart rate monitoring during labor can aid healthcare professionals in identifying alterations in the heart rate pattern. However, discrepancies in guidelines and...
INTRODUCTION
Fetal heart rate monitoring during labor can aid healthcare professionals in identifying alterations in the heart rate pattern. However, discrepancies in guidelines and obstetrician expertise present challenges in interpreting fetal heart rate, including failure to acknowledge findings or misinterpretation. Artificial intelligence has the potential to support obstetricians in diagnosing abnormal fetal heart rates.
METHODS
Employ preprocessing techniques to mitigate the effects of missing signals and artifacts on the model, utilize data augmentation methods to address data imbalance. Introduce a multi-scale long short-term memory neural network trained with a variety of time-scale data for automatically classifying fetal heart rate. Carried out experimental on both single and multi-scale models.
RESULTS
The results indicate that multi-scale LSTM models outperform regular LSTM models in various performance metrics. Specifically, in the single models tested, the model with a sampling rate of 10 exhibited the highest classification accuracy. The model achieves an accuracy of 85.73%, a specificity of 85.32%, and a precision of 85.53% on CTU-UHB dataset. Furthermore, the area under the receiver operating curve of 0.918 suggests that our model demonstrates a high level of credibility.
DISCUSSION
Compared to previous research, our methodology exhibits superior performance across various evaluation metrics. By incorporating alternative sampling rates into the model, we observed improvements in all performance indicators, including ACC (85.73% vs. 83.28%), SP (85.32% vs. 82.47%), PR (85.53% vs. 82.84%), recall (86.13% vs. 84.09%), F1-score (85.79% vs. 83.42%), and AUC(0.9180 vs. 0.8667). The limitations of this research include the limited consideration of pregnant women's clinical characteristics and disregard the potential impact of varying gestational weeks.
PubMed: 38933361
DOI: 10.3389/fphys.2024.1398735 -
Frontiers in Neurology 2024Acute Ischemic Stroke (AIS) remains a leading cause of mortality and disability worldwide. Rapid and precise prognostication of AIS is crucial for optimizing treatment...
BACKGROUND
Acute Ischemic Stroke (AIS) remains a leading cause of mortality and disability worldwide. Rapid and precise prognostication of AIS is crucial for optimizing treatment strategies and improving patient outcomes. This study explores the integration of machine learning-derived radiomics signatures from multi-parametric MRI with clinical factors to forecast AIS prognosis.
OBJECTIVE
To develop and validate a nomogram that combines a multi-MRI radiomics signature with clinical factors for predicting the prognosis of AIS.
METHODS
This retrospective study involved 506 AIS patients from two centers, divided into training (n = 277) and validation ( = 229) cohorts. 4,682 radiomic features were extracted from T1-weighted, T2-weighted, and diffusion-weighted imaging. Logistic regression analysis identified significant clinical risk factors, which, alongside radiomics features, were used to construct a predictive clinical-radiomics nomogram. The model's predictive accuracy was evaluated using calibration and ROC curves, focusing on distinguishing between favorable (mRS ≤ 2) and unfavorable (mRS > 2) outcomes.
RESULTS
Key findings highlight coronary heart disease, platelet-to-lymphocyte ratio, uric acid, glucose levels, homocysteine, and radiomics features as independent predictors of AIS outcomes. The clinical-radiomics model achieved a ROC-AUC of 0.940 (95% CI: 0.912-0.969) in the training set and 0.854 (95% CI: 0.781-0.926) in the validation set, underscoring its predictive reliability and clinical utility.
CONCLUSION
The study underscores the efficacy of the clinical-radiomics model in forecasting AIS prognosis, showcasing the pivotal role of artificial intelligence in fostering personalized treatment plans and enhancing patient care. This innovative approach promises to revolutionize AIS management, offering a significant leap toward more individualized and effective healthcare solutions.
PubMed: 38933326
DOI: 10.3389/fneur.2024.1379031 -
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi =... Jun 2024Red blood cells are destroyed when the shear stress in the blood pump exceeds a threshold, which in turn triggers hemolysis in the patient. The impeller design of...
Red blood cells are destroyed when the shear stress in the blood pump exceeds a threshold, which in turn triggers hemolysis in the patient. The impeller design of centrifugal blood pumps significantly influences the hydraulic characteristics and hemolytic properties of these devices. Based on this premise, the present study employs a multiphase flow approach to numerically simulate centrifugal blood pumps, investigating the performance of pumps with varying numbers of blades and blade deflection angles. This analysis encompassed the examination of flow field characteristics, hydraulic performance, and hemolytic potential. Numerical results indicated that the concentration of red blood cells and elevated shear stresses primarily occurred at the impeller and volute tongue, which drastically increased the risk of hemolysis in these areas. It was found that increasing the number of blades within a certain range enhanced the hydraulic performance of the pump but also raised the potential for hemolysis. Moreover, augmenting the blade deflection angle could improve the hemolytic performance, particularly in pumps with a higher number of blades. The findings from this study can provide valuable insights for the structural improvement and performance enhancement of centrifugal blood pumps.
Topics: Hemolysis; Humans; Heart-Assist Devices; Stress, Mechanical; Equipment Design; Erythrocytes; Centrifugation; Computer Simulation
PubMed: 38932545
DOI: 10.7507/1001-5515.202311015 -
Sensors (Basel, Switzerland) Jun 2024Respiratory rate (RR) is a vital indicator for assessing the bodily functions and health status of patients. RR is a prominent parameter in the field of biomedical...
Respiratory rate (RR) is a vital indicator for assessing the bodily functions and health status of patients. RR is a prominent parameter in the field of biomedical signal processing and is strongly associated with other vital signs such as blood pressure, heart rate, and heart rate variability. Various physiological signals, such as photoplethysmogram (PPG) signals, are used to extract respiratory information. RR is also estimated by detecting peak patterns and cycles in the signals through signal processing and deep-learning approaches. In this study, we propose an end-to-end RR estimation approach based on a third-generation artificial neural network model-spiking neural network. The proposed model employs PPG segments as inputs, and directly converts them into sequential spike events. This design aims to reduce information loss during the conversion of the input data into spike events. In addition, we use feedback-based integrate-and-fire neurons as the activation functions, which effectively transmit temporal information. The network is evaluated using the BIDMC respiratory dataset with three different window sizes (16, 32, and 64 s). The proposed model achieves mean absolute errors of 1.37 ± 0.04, 1.23 ± 0.03, and 1.15 ± 0.07 for the 16, 32, and 64 s window sizes, respectively. Furthermore, it demonstrates superior energy efficiency compared with other deep learning models. This study demonstrates the potential of the spiking neural networks for RR monitoring, offering a novel approach for RR estimation from the PPG signal.
Topics: Humans; Respiratory Rate; Neural Networks, Computer; Photoplethysmography; Signal Processing, Computer-Assisted; Heart Rate; Algorithms; Deep Learning
PubMed: 38931763
DOI: 10.3390/s24123980 -
Journal of Clinical Medicine Jun 2024The goal of this study was to evaluate the effect of extracorporeal membrane oxygenation (ECMO) on mortality in patients with cardiogenic shock excluding Impella and...
The goal of this study was to evaluate the effect of extracorporeal membrane oxygenation (ECMO) on mortality in patients with cardiogenic shock excluding Impella and IABP use. The large Nationwide Inpatient Sample (NIS) database was utilized to study any association between the use of ECMO in adults over the age of 18 and mortality and complications with a diagnosis of cardiogenic shocks. ICD-10 codes for ECMO and cardiogenic shock for the available years 2016-2020 were utilized. A total of 796,585 (age 66.5 ± 14.4) patients had a diagnosis of cardiogenic shock excluding Impella. Of these patients, 13,160 (age 53.7 ± 15.4) were treated with ECMO without IABP use. Total inpatient mortality without any device was 32.7%. It was 47.9% with ECMO. In a multivariate analysis adjusting for 47 variables such as age, gender, race, lactic acidosis, three-vessel intervention, left main myocardial infarction, cardiomyopathy, systolic heart failure, acute ST-elevation myocardial infarction, peripheral vascular disease, chronic renal disease, etc., ECMO utilization remained highly associated with mortality (OR: 1.78, CI: 1.6-1.9, < 0.001). Evaluating teaching hospitals only revealed similar findings. Major complications were also high in the ECMO cohort. In patients with cardiogenic shock, the use of ECMO was associated with the high in-hospital mortality regardless of comorbid condition, high-risk futures, or type of hospital.
PubMed: 38930138
DOI: 10.3390/jcm13123607 -
Journal of Clinical Medicine Jun 2024Cardiovascular disease (CVD), particularly coronary heart disease (CHD), is the leading cause of death in the US, with a high economic impact. Coronary artery calcium... (Review)
Review
Cardiovascular disease (CVD), particularly coronary heart disease (CHD), is the leading cause of death in the US, with a high economic impact. Coronary artery calcium (CAC) is a known marker for CHD and a useful tool for estimating the risk of atherosclerotic cardiovascular disease (ASCVD). Although CACS is recommended for informing the decision to initiate statin therapy, the current standard requires a dedicated CT protocol, which is time-intensive and contributes to radiation exposure. Non-dedicated CT protocols can be taken advantage of to visualize calcium and reduce overall cost and radiation exposure; however, they mainly provide visual estimates of coronary calcium and have disadvantages such as motion artifacts. Artificial intelligence is a growing field involving software that independently performs human-level tasks, and is well suited for improving CACS efficiency and repurposing non-dedicated CT for calcium scoring. We present a review of the current studies on automated CACS across various CT protocols and discuss consideration points in clinical application and some barriers to implementation.
PubMed: 38929986
DOI: 10.3390/jcm13123453 -
Journal of Personalized Medicine Jun 2024Chronic kidney disease (CKD) is strongly associated with dementia. However, its independent association with Alzheimer's or Parkinson's disease remains unclear. This...
Chronic kidney disease (CKD) is strongly associated with dementia. However, its independent association with Alzheimer's or Parkinson's disease remains unclear. This study investigated the prospective association of patients with CKD aged ≥55 years with an increased risk of Alzheimer's or Parkinson's disease. We conducted a retrospective cohort analysis using a national cohort sample of approximately one million patients. Primary outcome indicators measured included incidence of all-cause dementia, Alzheimer's disease, and Parkinson's disease events using person-years at risk. The hazard ratio was adjusted using the Cox proportional hazards model. We included 952 patients without CKD and 476 with CKD over 55 years using propensity score matching. The CKD group exhibited higher incidences of all-cause dementia, Parkinson's disease, and Alzheimer's disease than the non-CKD group. Furthermore, the CKD group had an elevated risk of all-cause dementia and a significantly increased risk of Parkinson's disease, especially among older women. Notably, the risk of Parkinson's disease was higher within the first 3 years of CKD diagnosis. These findings emphasize the link between CKD in mid- and late-life individuals and a higher incidence of all-cause dementia and Parkinson's disease rather than Alzheimer's disease.
PubMed: 38929818
DOI: 10.3390/jpm14060597