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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 -
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 -
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 -
Journal of the American Heart... Jul 2021Background Leadless pacemaker is a novel technology, and evidence supporting its use is uncertain. We performed a systematic review and meta-analysis to examine the... (Meta-Analysis)
Meta-Analysis
Background Leadless pacemaker is a novel technology, and evidence supporting its use is uncertain. We performed a systematic review and meta-analysis to examine the safety and efficacy of leadless pacemakers implanted in the right ventricle. Methods and Results We searched PubMed and Embase for studies published before June 6, 2020. The primary safety outcome was major complications, whereas the primary efficacy end point was acceptable pacing capture threshold (≤2 V). Pooled estimates were calculated using the Freedman-Tukey double arcsine transformation. Of 1281 records screened, we identified 36 observational studies of Nanostim and Micra leadless pacemakers, with most (69.4%) reporting outcomes for the Micra. For Micra, the pooled incidence of complications at 90 days (n=1608) was 0.46% (95% CI, 0.08%-1.05%) and at 1 year (n=3194) was 1.77% (95% CI, 0.76%-3.07%). In 5 studies with up to 1-year follow-up, Micra was associated with 51% lower odds of complications compared with transvenous pacemakers (3.30% versus 7.43%; odds ratio [OR], 0.49; 95% CI, 0.34-0.70). At 1 year, 98.96% (95% CI, 97.26%-99.94%) of 1376 patients implanted with Micra had good pacing capture thresholds. For Nanostim, the reported complication incidence ranged from 6.06% to 23.54% at 90 days and 5.33% to 6.67% at 1 year, with 90% to 100% having good pacing capture thresholds at 1 year (pooled result not estimated because of the low number of studies). Conclusions Most studies report outcomes for the Micra, which is associated with a low risk of complications and good electrical performance up to 1-year after implantation. Further data from randomized controlled trials are needed to support the widespread adoption of these devices in clinical practice.
Topics: Arrhythmias, Cardiac; Cardiac Pacing, Artificial; Equipment Design; Heart Ventricles; Humans; Pacemaker, Artificial; Treatment Outcome
PubMed: 34169736
DOI: 10.1161/JAHA.120.019212 -
The British Journal of Surgery Oct 2022Machine learning is a set of models and methods that can automatically detect patterns in vast amounts of data, extract information, and use it to perform...
BACKGROUND
Machine learning is a set of models and methods that can automatically detect patterns in vast amounts of data, extract information, and use it to perform decision-making under uncertain conditions. The potential of machine learning is significant, and breast surgeons must strive to be informed with up-to-date knowledge and its applications.
METHODS
A systematic database search of Embase, MEDLINE, the Cochrane database, and Google Scholar, from inception to December 2021, was conducted of original articles that explored the use of machine learning and/or artificial intelligence in breast surgery in EMBASE, MEDLINE, Cochrane database and Google Scholar.
RESULTS
The search yielded 477 articles, of which 14 studies were included in this review, featuring 73 847 patients. Four main areas of machine learning application were identified: predictive modelling of surgical outcomes; breast imaging-based context; screening and triaging of patients with breast cancer; and as network utility for detection. There is evident value of machine learning in preoperative planning and in providing information for surgery both in a cancer and an aesthetic context. Machine learning outperformed traditional statistical modelling in all studies for predicting mortality, morbidity, and quality of life outcomes. Machine learning patterns and associations could support planning, anatomical visualization, and surgical navigation.
CONCLUSION
Machine learning demonstrated promising applications for improving breast surgery outcomes and patient-centred care. Neveretheless, there remain important limitations and ethical concerns relating to implementing artificial intelligence into everyday surgical practices.
Topics: Artificial Intelligence; Breast Neoplasms; Databases, Factual; Female; Humans; Machine Learning; Quality of Life
PubMed: 35945894
DOI: 10.1093/bjs/znac224 -
Frontiers in Bioengineering and... 2021Artificial intelligence is widely used in medical field, and machine learning has been increasingly used in health care, prediction, and diagnosis and as a method of... (Review)
Review
Artificial intelligence is widely used in medical field, and machine learning has been increasingly used in health care, prediction, and diagnosis and as a method of determining priority. Machine learning methods have been features of several tools in the fields of obstetrics and childcare. This present review aims to summarize the machine learning techniques to predict perinatal complications. To identify the applicability and performance of machine learning methods used to identify pregnancy complications. A total of 98 articles were obtained with the keywords "machine learning," "deep learning," "artificial intelligence," and accordingly as they related to perinatal complications ("complications in pregnancy," "pregnancy complications") from three scientific databases: PubMed, Scopus, and Web of Science. These were managed on the Mendeley platform and classified using the PRISMA method. A total of 31 articles were selected after elimination according to inclusion and exclusion criteria. The features used to predict perinatal complications were primarily electronic medical records (48%), medical images (29%), and biological markers (19%), while 4% were based on other types of features, such as sensors and fetal heart rate. The main perinatal complications considered in the application of machine learning thus far are pre-eclampsia and prematurity. In the 31 studies, a total of sixteen complications were predicted. The main precision metric used is the AUC. The machine learning methods with the best results were the prediction of prematurity from medical images using the support vector machine technique, with an accuracy of 95.7%, and the prediction of neonatal mortality with the XGBoost technique, with 99.7% accuracy. It is important to continue promoting this area of research and promote solutions with multicenter clinical applicability through machine learning to reduce perinatal complications. This systematic review contributes significantly to the specialized literature on artificial intelligence and women's health.
PubMed: 35127665
DOI: 10.3389/fbioe.2021.780389 -
European Heart Journal. Digital Health Sep 2021Artificial intelligence (AI) and machine learning (ML) promise vast advances in medicine. The current state of AI/ML applications in cardiovascular medicine is largely... (Review)
Review
AIMS
Artificial intelligence (AI) and machine learning (ML) promise vast advances in medicine. The current state of AI/ML applications in cardiovascular medicine is largely unknown. This systematic review aims to close this gap and provides recommendations for future applications.
METHODS AND RESULTS
Pubmed and EMBASE were searched for applied publications using AI/ML approaches in cardiovascular medicine without limitations regarding study design or study population. The PRISMA statement was followed in this review. A total of 215 studies were identified and included in the final analysis. The majority (87%) of methods applied belong to the context of supervised learning. Within this group, tree-based methods were most commonly used, followed by network and regression analyses as well as boosting approaches. Concerning the areas of application, the most common disease context was coronary artery disease followed by heart failure and heart rhythm disorders. Often, different input types such as electronic health records and images were combined in one AI/ML application. Only a minority of publications investigated reproducibility and generalizability or provided a clinical trial registration.
CONCLUSIONS
A major finding is that methodology may overlap even with similar data. Since we observed marked variation in quality, reporting of the evaluation and transparency of data and methods urgently need to be improved.
PubMed: 36713608
DOI: 10.1093/ehjdh/ztab054 -
Europace : European Pacing,... Nov 2023Transvenous lead extraction (TLE) is performed using non-laser and laser techniques with overall high efficacy and safety. Variation in outcomes between the two... (Meta-Analysis)
Meta-Analysis
AIMS
Transvenous lead extraction (TLE) is performed using non-laser and laser techniques with overall high efficacy and safety. Variation in outcomes between the two approaches does exist with limited comparative evidence in the literature. We sought to compare non-laser and laser TLE in a meta-analysis.
METHODS AND RESULTS
We searched Medline, Embase, Scopus, ClinicalTrials.gov, and CENTRAL databases for TLE studies published between 1991 and 2021. From the included 68 studies, safety and efficacy data were carefully evaluated and extracted. Aggregated cases of outcomes were used to calculate odds ratio (OR), and pooled rates were synthesized from eligible studies to compare non-laser and laser techniques. Subgroup comparison of rotational tool and laser extraction was also performed. Non-laser in comparison with laser had lower procedural mortality (pooled rate 0% vs. 0.1%, P < 0.01), major complications (pooled rate 0.7% vs. 1.7%, P < 0.01), and superior vena cava (SVC) injury (pooled rate 0% vs. 0.5%, P < 0.001), with higher complete success (pooled rate 96.5% vs. 93.8%, P < 0.01). Non-laser comparatively to laser was more likely to achieve clinical [OR 2.16 (1.77-2.63), P < 0.01] and complete [OR 1.87 (1.69-2.08), P < 0.01] success, with a lower procedural mortality risk [OR 1.6 (1.02-2.5), P < 0.05]. In the subgroup analysis, rotational tool compared with laser achieved greater complete success (pooled rate 97.4% vs. 95%, P < 0.01) with lower SVC injury (pooled rate 0% vs. 0.7%, P < 0.01).
CONCLUSION
Non-laser TLE is associated with a better safety and efficacy profile when compared with laser methods. There is a greater risk of SVC injury associated with laser sheath extraction.
Topics: Humans; Defibrillators, Implantable; Vena Cava, Superior; Device Removal; Lasers; Cardiac Catheterization; Pacemaker, Artificial; Treatment Outcome; Retrospective Studies
PubMed: 37882609
DOI: 10.1093/europace/euad316