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Entropy (Basel, Switzerland) May 2021The automated classification of heart sounds plays a significant role in the diagnosis of cardiovascular diseases (CVDs). With the recent introduction of medical big... (Review)
Review
The automated classification of heart sounds plays a significant role in the diagnosis of cardiovascular diseases (CVDs). With the recent introduction of medical big data and artificial intelligence technology, there has been an increased focus on the development of deep learning approaches for heart sound classification. However, despite significant achievements in this field, there are still limitations due to insufficient data, inefficient training, and the unavailability of effective models. With the aim of improving the accuracy of heart sounds classification, an in-depth systematic review and an analysis of existing deep learning methods were performed in the present study, with an emphasis on the convolutional neural network (CNN) and recurrent neural network (RNN) methods developed over the last five years. This paper also discusses the challenges and expected future trends in the application of deep learning to heart sounds classification with the objective of providing an essential reference for further study.
PubMed: 34073201
DOI: 10.3390/e23060667 -
Annals of Cardiothoracic Surgery Mar 2020The aim of this study was to compare the outcomes of patients undergoing SynCardia total artificial heart (TAH) and biventricular HeartWare ventricular assist device...
Comparison of SynCardia total artificial heart and HeartWare HVAD biventricular support for management of biventricular heart failure: a systematic review and meta-analysis.
BACKGROUND
The aim of this study was to compare the outcomes of patients undergoing SynCardia total artificial heart (TAH) and biventricular HeartWare ventricular assist device (Bi-HVAD) support for biventricular heart failure (HF).
METHODS
Electronic search was performed to identify all relevant studies detailing patients who underwent biventricular assist device implantation using Bi-HVAD devices and those who underwent TAH placement for biventricular HF. Twelve studies including 512 patients in the TAH group versus 38 patients in the Bi-HVAD group were pooled for meta-analysis.
RESULTS
Ischemic cardiac etiology was present in 32% (95% CI, 24-47) of TAH 15% (95% CI, 4-44) of Bi-HVAD patients (P=0.21). There was a comparable incidence of stroke [TAH 11% (95% CI, 7-16) Bi-HVAD 13% (95% CI, 2-51), P=0.86] and acute kidney injury [TAH 28% (95% CI, 2-89) Bi-HVAD 27% (95% CI, 9-59), P=0.98]. Overall infection rate was 67% (95% CI, 47-82) in TAH and 36% (95% CI, 10-74) in Bi-HVAD (P=0.16). Driveline infections were comparable between the two groups [TAH 11% (95% CI, 6-19) Bi-HVAD 8% (95% CI, 1-39), P=0.73] and although a higher incidence of mediastinitis was found in the Bi-HVAD group [TAH 4% (95% CI, 2-7) Bi-HVAD 15% (95% CI, 4-45), P=0.07] there was no statistically significant difference between the groups. Postoperative bleeding was present in 42% (95% CI, 28-58) of TAH 23% (95% CI, 8-52) of Bi-HVAD (P=0.22). Patients in the TAH group had shorter duration of support [TAH 71 days (95% CI, 15-127) Bi-HVAD 167 days (95% CI, 116-217), P=0.01]. At the mean follow-up time of 120 days, (95% CI, 83-157) patients in both groups had similar overall mortality [TAH 36% (95% CI, 22-49) Bi-HVAD 26% (95% CI, 6-46), P=0.44] including mortality on device support [TAH 26% (95% CI, 17-36) Bi-HVAD 21% (95% CI, 4-37), P=0.55]. Discharge home on support was achieved in 6% (95% CI, 4-17%) of TAH patients 73% (95% CI, 48-89%) of Bi-HVAD (P<0.01), and 68% (95% CI, 52-84) of TAH patients were transplanted 61% (95% CI, 47-75) in the Bi-HVAD group (P=0.14).
CONCLUSIONS
Patients on Bi-HVAD support were more likely to be able to be discharged home on support and had similar overall mortality to TAH, albeit with much longer duration of support.
PubMed: 32309154
DOI: 10.21037/acs.2020.03.07 -
JMIR Diabetes Jul 2022Diabetes is a chronic condition that necessitates regular monitoring and self-management of the patient's blood glucose levels. People with type 1 diabetes (T1D) can... (Review)
Review
BACKGROUND
Diabetes is a chronic condition that necessitates regular monitoring and self-management of the patient's blood glucose levels. People with type 1 diabetes (T1D) can live a productive life if they receive proper diabetes care. Nonetheless, a loose glycemic control might increase the risk of developing hypoglycemia. This incident can occur because of a variety of causes, such as taking additional doses of insulin, skipping meals, or overexercising. Mainly, the symptoms of hypoglycemia range from mild dysphoria to more severe conditions, if not detected in a timely manner.
OBJECTIVE
In this review, we aimed to report on innovative detection techniques and tactics for identifying and preventing hypoglycemic episodes, focusing on T1D.
METHODS
A systematic literature search following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines was performed focusing on the PubMed, GoogleScholar, IEEEXplore, and ACM Digital Library to find articles on technologies related to hypoglycemia detection in patients with T1D.
RESULTS
The presented approaches have been used or devised to enhance blood glucose monitoring and boost its efficacy in forecasting future glucose levels, which could aid the prediction of future episodes of hypoglycemia. We detected 19 predictive models for hypoglycemia, specifically on T1D, using a wide range of algorithmic methodologies, spanning from statistics (1.9/19, 10%) to machine learning (9.88/19, 52%) and deep learning (7.22/19, 38%). The algorithms used most were the Kalman filtering and classification models (support vector machine, k-nearest neighbors, and random forests). The performance of the predictive models was found to be satisfactory overall, reaching accuracies between 70% and 99%, which proves that such technologies are capable of facilitating the prediction of T1D hypoglycemia.
CONCLUSIONS
It is evident that continuous glucose monitoring can improve glucose control in diabetes; however, predictive models for hypo- and hyperglycemia using only mainstream noninvasive sensors such as wristbands and smartwatches are foreseen to be the next step for mobile health in T1D. Prospective studies are required to demonstrate the value of such models in real-life mobile health interventions.
PubMed: 35862181
DOI: 10.2196/34699 -
Brain Informatics Sep 2020Neuromarketing has become an academic and commercial area of interest, as the advancements in neural recording techniques and interpreting algorithms have made it an... (Review)
Review
Neuromarketing has become an academic and commercial area of interest, as the advancements in neural recording techniques and interpreting algorithms have made it an effective tool for recognizing the unspoken response of consumers to the marketing stimuli. This article presents the very first systematic review of the technological advancements in Neuromarketing field over the last 5 years. For this purpose, authors have selected and reviewed a total of 57 relevant literatures from valid databases which directly contribute to the Neuromarketing field with basic or empirical research findings. This review finds consumer goods as the prevalent marketing stimuli used in both product and promotion forms in these selected literatures. A trend of analyzing frontal and prefrontal alpha band signals is observed among the consumer emotion recognition-based experiments, which corresponds to frontal alpha asymmetry theory. The use of electroencephalogram (EEG) is found favorable by many researchers over functional magnetic resonance imaging (fMRI) in video advertisement-based Neuromarketing experiments, apparently due to its low cost and high time resolution advantages. Physiological response measuring techniques such as eye tracking, skin conductance recording, heart rate monitoring, and facial mapping have also been found in these empirical studies exclusively or in parallel with brain recordings. Alongside traditional filtering methods, independent component analysis (ICA) was found most commonly in artifact removal from neural signal. In consumer response prediction and classification, Artificial Neural Network (ANN), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) have performed with the highest average accuracy among other machine learning algorithms used in these literatures. The authors hope, this review will assist the future researchers with vital information in the field of Neuromarketing for making novel contributions.
PubMed: 32955675
DOI: 10.1186/s40708-020-00109-x -
International Journal of Cardiology Oct 2017Heart failure is the leading cause of morbidity and mortality for adults with congenital heart disease (ACHD). Many patients are ineligible for transplantation, and... (Review)
Review
BACKGROUND
Heart failure is the leading cause of morbidity and mortality for adults with congenital heart disease (ACHD). Many patients are ineligible for transplantation, and those who are eligible often face long wait times with high wait-list morbidity. Durable mechanical circulatory support (MCS) may be an option for many patients. This systematic review evaluates the published literature on the use of durable MCS in teenagers and adults with congenital heart disease.
METHODS
A comprehensive search of MEDLINE (PubMed), EMBASE, and the Cochrane Library was performed electronically in July 2015 and updated in March 2016, guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines.
RESULTS
Individual case reports and several case series identified 66 patients with ACHD treated with durable MCS. More than half were INTERMACS 1 or 2 at the time of implantation. Patients with Fontan repairs were more frequently classified as INTERMACS 1 or 2 (89% compared to 59% or less among other groups). Cases published after 2010 showed a trend toward less severe INTERMACS status, and patients were less likely to have received transplants by the time of reporting (31% compared to 61% prior). Durable MCS was implanted as bridge-to-transplant in 77%. Patients with Fontan repair accounted for 14% of cases.
CONCLUSION
Reports of durable MCS utilization in patients with ACHD are becoming more frequent and devices are being implanted in more stable patients. Reports are mostly case reports or small case series so reporting bias is likely and prospective protocoled reporting is needed.
Topics: Adolescent; Adult; Heart Defects, Congenital; Heart Failure; Heart-Assist Devices; Humans; Registries
PubMed: 28781147
DOI: 10.1016/j.ijcard.2017.07.107 -
Clinical Cardiology Jul 2023Cardiac resynchronization therapy (CRT) strategy for heart failure with mildly reduced ejection fraction (HFmrEF) is controversial. Left bundle branch area pacing... (Meta-Analysis)
Meta-Analysis Review
Cardiac resynchronization therapy (CRT) strategy for heart failure with mildly reduced ejection fraction (HFmrEF) is controversial. Left bundle branch area pacing (LBBAP) is an emerging pacing modality and an alternative option to CRT. This analysis aimed to perform a systematic review of the literature and meta-analysis on the impact of the LBBAP strategy in HFmrEF, with left ventricular ejection fraction (LVEF) between 35% and 50%. PubMed, Embase, and Cochrane Library were searched for full-text articles on LBBAP from inception to July 17, 2022. The outcomes of interest were QRS duration and LVEF at baseline and follow-up in mid-range heart failure. Data were extracted and summarized. A random-effect model incorporating the potential heterogeneity was used to synthesize the results. Out of 1065 articles, 8 met the inclusion criteria for 211 mid-range heart failure patients with an implant LBBAP across the 16 centers. The average implant success rate with lumenless pacing lead use was 91.3%, and 19 complications were reported among all 211 enrolled patients. During the average follow-up of 9.1 months, the average LVEF was 39.8% at baseline and 50.5% at follow-up (MD: 10.90%, 95% CI: 6.56-15.23, p < .01). Average QRS duration was 152.6 ms at baseline and 119.3 ms at follow-up (MD: -34.51 ms, 95% CI: -60.00 to -9.02, p < .01). LBBAP could significantly reduce QRS duration and improve systolic function in a patient with LVEF between 35% and 50%. Application of LBBAP as a CRT strategy for HFmrEF may be a viable option.
Topics: Humans; Stroke Volume; Cardiac Pacing, Artificial; Ventricular Function, Left; Heart Conduction System; Cardiac Resynchronization Therapy; Heart Failure; Electrocardiography; Treatment Outcome
PubMed: 37144691
DOI: 10.1002/clc.24028 -
Metabolites Mar 2022Parkinson’s disease (PD) is a severe, incurable, and costly condition leading to heart failure. The link between PD and cardiovascular disease (CVD) is not available,... (Review)
Review
Parkinson’s disease (PD) is a severe, incurable, and costly condition leading to heart failure. The link between PD and cardiovascular disease (CVD) is not available, leading to controversies and poor prognosis. Artificial Intelligence (AI) has already shown promise for CVD/stroke risk stratification. However, due to a lack of sample size, comorbidity, insufficient validation, clinical examination, and a lack of big data configuration, there have been no well-explained bias-free AI investigations to establish the CVD/Stroke risk stratification in the PD framework. The study has two objectives: (i) to establish a solid link between PD and CVD/stroke; and (ii) to use the AI paradigm to examine a well-defined CVD/stroke risk stratification in the PD framework. The PRISMA search strategy selected 223 studies for CVD/stroke risk, of which 54 and 44 studies were related to the link between PD-CVD, and PD-stroke, respectively, 59 studies for joint PD-CVD-Stroke framework, and 66 studies were only for the early PD diagnosis without CVD/stroke link. Sequential biological links were used for establishing the hypothesis. For AI design, PD risk factors as covariates along with CVD/stroke as the gold standard were used for predicting the CVD/stroke risk. The most fundamental cause of CVD/stroke damage due to PD is cardiac autonomic dysfunction due to neurodegeneration that leads to heart failure and its edema, and this validated our hypothesis. Finally, we present the novel AI solutions for CVD/stroke risk prediction in the PD framework. The study also recommends strategies for removing the bias in AI for CVD/stroke risk prediction using the PD framework.
PubMed: 35448500
DOI: 10.3390/metabo12040312 -
Diagnostics (Basel, Switzerland) Feb 2023Monkeypox or Mpox is an infectious virus predominantly found in Africa. It has spread to many countries since its latest outbreak. Symptoms such as headaches, chills,... (Review)
Review
Monkeypox or Mpox is an infectious virus predominantly found in Africa. It has spread to many countries since its latest outbreak. Symptoms such as headaches, chills, and fever are observed in humans. Lumps and rashes also appear on the skin (similar to smallpox, measles, and chickenpox). Many artificial intelligence (AI) models have been developed for accurate and early diagnosis. In this work, we systematically reviewed recent studies that used AI for mpox-related research. After a literature search, 34 studies fulfilling prespecified criteria were selected with the following subject categories: diagnostic testing of mpox, epidemiological modeling of mpox infection spread, drug and vaccine discovery, and media risk management. In the beginning, mpox detection using AI and various modalities was described. Other applications of ML and DL in mitigating mpox were categorized later. The various machine and deep learning algorithms used in the studies and their performance were discussed. We believe that a state-of-the-art review will be a valuable resource for researchers and data scientists in developing measures to counter the mpox virus and its spread.
PubMed: 36899968
DOI: 10.3390/diagnostics13050824 -
Healthcare (Basel, Switzerland) Jun 2023IntraUterine Growth Restriction (IUGR) is a global public health concern and has major implications for neonatal health. The early diagnosis of this condition is crucial... (Review)
Review
BACKGROUND
IntraUterine Growth Restriction (IUGR) is a global public health concern and has major implications for neonatal health. The early diagnosis of this condition is crucial for obtaining positive outcomes for the newborn. In recent years Artificial intelligence (AI) and machine learning (ML) techniques are being used to identify risk factors and provide early prediction of IUGR. We performed a systematic review (SR) and meta-analysis (MA) aimed to evaluate the use and performance of AI/ML models in detecting fetuses at risk of IUGR.
METHODS
We conducted a systematic review according to the PRISMA checklist. We searched for studies in all the principal medical databases (MEDLINE, EMBASE, CINAHL, Scopus, Web of Science, and Cochrane). To assess the quality of the studies we used the JBI and CASP tools. We performed a meta-analysis of the diagnostic test accuracy, along with the calculation of the pooled principal measures.
RESULTS
We included 20 studies reporting the use of AI/ML models for the prediction of IUGR. Out of these, 10 studies were used for the quantitative meta-analysis. The most common input variable to predict IUGR was the fetal heart rate variability ( = 8, 40%), followed by the biochemical or biological markers ( = 5, 25%), DNA profiling data ( = 2, 10%), Doppler indices ( = 3, 15%), MRI data ( = 1, 5%), and physiological, clinical, or socioeconomic data ( = 1, 5%). Overall, we found that AI/ML techniques could be effective in predicting and identifying fetuses at risk for IUGR during pregnancy with the following pooled overall diagnostic performance: sensitivity = 0.84 (95% CI 0.80-0.88), specificity = 0.87 (95% CI 0.83-0.90), positive predictive value = 0.78 (95% CI 0.68-0.86), negative predictive value = 0.91 (95% CI 0.86-0.94) and diagnostic odds ratio = 30.97 (95% CI 19.34-49.59). In detail, the RF-SVM (Random Forest-Support Vector Machine) model (with 97% accuracy) showed the best results in predicting IUGR from FHR parameters derived from CTG.
CONCLUSIONS
our findings showed that AI/ML could be part of a more accurate and cost-effective screening method for IUGR and be of help in optimizing pregnancy outcomes. However, before the introduction into clinical daily practice, an appropriate algorithmic improvement and refinement is needed, and the importance of quality assessment and uniform diagnostic criteria should be further emphasized.
PubMed: 37297757
DOI: 10.3390/healthcare11111617 -
Heart Failure Reviews Mar 2023Screening for left ventricular systolic dysfunction (LVSD), defined as reduced left ventricular ejection fraction (LVEF), deserves renewed interest as the medical... (Review)
Review
Screening for left ventricular systolic dysfunction (LVSD), defined as reduced left ventricular ejection fraction (LVEF), deserves renewed interest as the medical treatment for the prevention and progression of heart failure improves. We aimed to review the updated literature to outline the potential and caveats of using artificial intelligence-enabled electrocardiography (AIeECG) as an opportunistic screening tool for LVSD.We searched PubMed and Cochrane for variations of the terms "ECG," "Heart Failure," "systolic dysfunction," and "Artificial Intelligence" from January 2010 to April 2022 and selected studies that reported the diagnostic accuracy and confounders of using AIeECG to detect LVSD.Out of 40 articles, we identified 15 relevant studies; eleven retrospective cohorts, three prospective cohorts, and one case series. Although various LVEF thresholds were used, AIeECG detected LVSD with a median AUC of 0.90 (IQR from 0.85 to 0.95), a sensitivity of 83.3% (IQR from 73 to 86.9%) and a specificity of 87% (IQR from 84.5 to 90.9%). AIeECG algorithms succeeded across a wide range of sex, age, and comorbidity and seemed especially useful in non-cardiology settings and when combined with natriuretic peptide testing. Furthermore, a false-positive AIeECG indicated a future development of LVSD. No studies investigated the effect on treatment or patient outcomes.This systematic review corroborates the arrival of a new generic biomarker, AIeECG, to improve the detection of LVSD. AIeECG, in addition to natriuretic peptides and echocardiograms, will improve screening for LVSD, but prospective randomized implementation trials with added therapy are needed to show cost-effectiveness and clinical significance.
Topics: Humans; Ventricular Function, Left; Stroke Volume; Prospective Studies; Retrospective Studies; Electrocardiography; Ventricular Dysfunction, Left; Heart Failure; Intelligence
PubMed: 36344908
DOI: 10.1007/s10741-022-10283-1