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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 -
Journal of Evidence-based Medicine Sep 2023Technology including artificial intelligence (AI) may play a key role to strengthen primary health care services in resource-poor settings. This systematic review aims...
AIM
Technology including artificial intelligence (AI) may play a key role to strengthen primary health care services in resource-poor settings. This systematic review aims to explore the evidence on the use of AI and digital health in improving primary health care service delivery.
METHODS
Three electronic databases were searched using a comprehensive search strategy without providing any restriction in June 2023. Retrieved articles were screened independently using the "Rayyan" software. Data extraction and quality assessment were conducted independently by two review authors. A narrative synthesis of the included interventions was conducted.
RESULTS
A total of 4596 articles were screened, and finally, 48 articles were included from 21 different countries published between 2013 and 2021. The main focus of the included studies was noncommunicable diseases (n = 15), maternal and child health care (n = 11), primary care (n = 8), infectious diseases including tuberculosis, leprosy, and HIV (n = 7), and mental health (n = 6). Included studies considered interventions using AI, and digital health of which mobile-phone-based interventions were prominent. m-health interventions were well adopted and easy to use and improved the record-keeping, service deliver, and patient satisfaction.
CONCLUSION
AI and the application of digital technologies improve primary health care service delivery in resource-poor settings in various ways. However, in most of the cases, the application of AI and digital health is implemented through m-health. There is a great scope to conduct further research exploring the interventions on a large scale.
PubMed: 37691394
DOI: 10.1111/jebm.12547 -
Heart Failure Reviews May 2022Heart failure is a significant cause of mortality in children with cardiovascular diseases. Treatment of heart failure depends on patients' symptoms, age, and severity... (Meta-Analysis)
Meta-Analysis Review
Heart failure is a significant cause of mortality in children with cardiovascular diseases. Treatment of heart failure depends on patients' symptoms, age, and severity of their condition, with heart transplantation required when other treatments are unsuccessful. However, due to lack of fitting donor organs, many patients are left untreated, or their transplant is delayed. In these patients, ventricular assist devices (VADs) are used to bridge to heart transplant. However, VAD support presents various complications in patients. The aim of this study was to compile, review, and analyse the studies reporting risk factors and aetiologies of complications of VAD support in children. Random effect risk ratios (RR) with 95% confidence intervals were calculated to analyse relative risk of thrombosis (RR = 3.53 [1.04, 12.06] I = 0% P = 0.04), neurological problems (RR = 0.95 [0.29, 3.15] I = 53% P = 0.93), infection (RR = 0.31 [0.05, 2.03] I = 86% P = 0.22), bleeding (RR = 2.57 [0.76, 8.66] I = 0% P = 0.13), and mortality (RR = 2.20 [1.36, 3.55] I = 0% P = 0.001) under pulsatile-flow and continuous-flow VAD support, relative risk of mortality (RR = 0.45 [0.15, 1.37] I = 36% P = 0.16) under left VAD and biVAD support, relative risk of thrombosis (RR = 1.72 [0.46, 6.44] I = 0% P = 0.42), infection (RR = 1.77 [0.10, 32.24] I = 46% P = 0.70) and mortality (RR = 0.92 [0.14, 6.28] I = 45% P = 0.93) in children with body surface area < 1.2 m and > 1.2 m under VAD support, relative risk of mortality in children supported with VAD and diagnosed with cardiomyopathy and congenital heart diseases (RR = 1.31 [0.10, 16.61] I = 73% P = 0.84), and cardiomyopathy and myocarditis (RR = 0.91 [0.13, 6.24] I = 58% P = 0.92). Meta-analyses results show that further research is necessary to reduce complications under VAD support.
Topics: Cardiomyopathies; Child; Heart Failure; Heart Transplantation; Heart-Assist Devices; Humans; Retrospective Studies; Thrombosis; Treatment Outcome
PubMed: 33661404
DOI: 10.1007/s10741-021-10093-x -
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 -
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 -
American Journal of Obstetrics and... May 2023The past 20 years witnessed an invigoration of research on labor progression and a change of thinking regarding normal labor. New evidence is emerging, and more advanced...
The past 20 years witnessed an invigoration of research on labor progression and a change of thinking regarding normal labor. New evidence is emerging, and more advanced statistical methods are applied to labor progression analyses. Given the wide variations in the onset of active labor and the pattern of labor progression, there is an emerging consensus that the definition of abnormal labor may not be related to an idealized or average labor curve. Alternative approaches to guide labor management have been proposed; for example, using an upper limit of a distribution of labor duration to define abnormally slow labor. Nonetheless, the methods of labor assessment are still primitive and subject to error; more objective measures and more advanced instruments are needed to identify the onset of active labor, monitor labor progression, and define when labor duration is associated with maternal/child risk. Cervical dilation alone may be insufficient to define active labor, and incorporating more physical and biochemical measures may improve accuracy of diagnosing active labor onset and progression. Because the association between duration of labor and perinatal outcomes is rather complex and influenced by various underlying and iatrogenic conditions, future research must carefully explore how to integrate statistical cut-points with clinical outcomes to reach a practical definition of labor abnormalities. Finally, research regarding the complex labor process may benefit from new approaches, such as machine learning technologies and artificial intelligence to improve the predictability of successful vaginal delivery with normal perinatal outcomes.
Topics: Child; Female; Humans; Pregnancy; Artificial Intelligence; Delivery, Obstetric; Dystocia; Labor Stage, First; Labor, Obstetric
PubMed: 37164489
DOI: 10.1016/j.ajog.2022.11.1299 -
American Journal of Surgery Sep 2023The role of metabolic and bariatric surgery (MBS), in synergy with left ventricular assist device (LVAD) implantation, in the scope of end-stage heart failure management... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
The role of metabolic and bariatric surgery (MBS), in synergy with left ventricular assist device (LVAD) implantation, in the scope of end-stage heart failure management for patients with severe obesity is not well elucidated.
METHODS
We conducted a meta-analysis using Cochrane, Embase, PubMed, and Scopus databases to include articles from their inception to November 2022.
RESULTS
A total of 271 patients who underwent MBS during or after the LVAD implantation were included from eleven separate studies. After surgery, 67.4% of patients were listed on the heart transplant waitlist with 32.5% undergoing a successful transplant. We reported a mean listing time of 13.8 months. Finally, the pooled postoperative complication rate, 30-day readmission rate, and one-year mortality rate were 47.6%, 23.6% and 10.2% respectively.
CONCLUSIONS
MBS and LVAD is a safe and effective approach to bridge patients with severe obesity and end-stage heart failure for definitive heart transplantation.
Topics: Humans; Obesity, Morbid; Heart-Assist Devices; Heart Failure; Heart Transplantation; Bariatric Surgery; Treatment Outcome; Retrospective Studies
PubMed: 37355375
DOI: 10.1016/j.amjsurg.2023.06.014 -
Artificial Organs Aug 2022Mechanical circulatory support (MCS) devices, such as ventricular assist devices (VADs) and total artificial hearts (TAHs), have become a vital therapeutic option in the... (Review)
Review
BACKGROUND
Mechanical circulatory support (MCS) devices, such as ventricular assist devices (VADs) and total artificial hearts (TAHs), have become a vital therapeutic option in the treatment of end-stage heart failure for adult patients. Such therapeutic options continue to be limited for pediatric patients. Clinicians initially adapted or scaled existing adult devices for pediatric patients; however, these adult devices are not designed to support the anatomical structure and varying flow capacities required for this population and are generally operated "off-design," which risks complications such as hemolysis and thrombosis. Devices designed specifically for the pediatric population which seek to address these shortcomings are now emerging and gaining FDA approval.
METHODS
To analyze the competitive landscape of pediatric MCS devices, we conducted a systematic literature review. Approximately 27 devices were studied in detail: 8 were established or previously approved designs, and 19 were under development (11 VADs, 5 Fontan assist devices, and 3 TAHs).
RESULTS
Despite significant progress, there is still no pediatric pump technology that satisfies the unique and distinct design constraints and requirements to support pediatric patients, including the wide range of patient sizes, increased cardiovascular demand with growth, and anatomic and physiologic heterogeneity of congenital heart disease.
CONCLUSIONS
Forward-thinking design solutions are required to overcome these challenges and to ensure the translation of new therapeutic MCS devices for pediatric patients.
Topics: Child; Extracorporeal Membrane Oxygenation; Heart Failure; Heart, Artificial; Heart-Assist Devices; Humans; Technology
PubMed: 35357020
DOI: 10.1111/aor.14242 -
International Journal of Cardiology Nov 2023Right Ventricular Pacing (RVP) may have detrimental effects in ventricular function. Left Bundle Branch Area Pacing (LBBAP) is a new pacing strategy that appears to have... (Meta-Analysis)
Meta-Analysis
Safety and efficacy of left bundle branch area pacing compared with right ventricular pacing in patients with bradyarrhythmia and conduction system disorders: Systematic review and meta-analysis.
BACKGROUND
Right Ventricular Pacing (RVP) may have detrimental effects in ventricular function. Left Bundle Branch Area Pacing (LBBAP) is a new pacing strategy that appears to have better results. The aim of this systematic review and meta-analysis is to compare the safety and efficacy of LBBAP vs RVP in patients with bradyarrhythmia and conduction system disorders.
METHODS
MEDLINE, EMBASE and Pubmed databases were searched for studies comparing LBBAP with RVP. Outcomes were all-cause mortality, atrial fibrillation (AF) occurrence, heart failure hospitalizations (HFH) and complications. QRS duration, mechanical synchrony and LVEF changes were also assessed. Pairwise meta-analysis was conducted using random and fixed effects models.
RESULTS
Twenty-five trials with 4250 patients (2127 LBBAP) were included in the analysis. LBBAP was associated with lower risk for HFH (RR:0.33, CI 95%:0.21 to 0.50; p < 0.001), all-cause mortality (RR:0.52 CI 95%:0.34 to 0.80; p = 0.003), and AF occurrence (RR:0.43 CI 95%:0.27 to 0.68; p < 0.001) than RVP. Lead related complications were not different between the two groups (p = 0.780). QRSd was shorter in the LBBAP group at follow-up (WMD: -32.20 msec, CI 95%: -40.70 to -23.71; p < 0.001) and LBBAP achieved better intraventricular mechanical synchrony than RVP (SMD: -1.77, CI 95%: -2.45 to -1.09; p < 0.001). LBBAP had similar pacing thresholds (p = 0.860) and higher R wave amplitudes (p = 0.009) than RVP.
CONCLUSIONS
LBBAP has better clinical outcomes, preserves ventricular electrical and mechanical synchrony and has excellent pacing parameters, with no difference in complications compared to RVP.
Topics: Humans; Bradycardia; Cardiac Pacing, Artificial; Cardiac Conduction System Disease; Heart Conduction System; Atrial Fibrillation; Electrocardiography; Treatment Outcome; Bundle of His
PubMed: 37527751
DOI: 10.1016/j.ijcard.2023.131230