Automated Affect and Emotion Recognition from Cardiovascular Signals - A Systematic Overview Of The Field

Currently, artificial intelligence is increasingly used to recognize and differentiate emotions. Through the action of the nervous system, the heart and vascular system can respond differently depending on the type of arousal. With the growing popularity of wearable devices able to measure such signals, people may monitor their states and manage their wellness. Our goal was to explore and summarize the field of automated emotion and affect recognition from cardiovascular signals. According to our protocol, we searched electronic sources (MEDLINE, EMBASE, Web of Science, Scopus, dblp, Cochrane Library, IEEE Explore, arXiv and medRxiv) up to 31 August 2020. In the case of all identified studies, two independent reviewers were involved at each stage: screening, full-text assessment, data extraction, and quality evaluation. All conflicts were resolved during the discussion. The credibility of included studies was evaluated using a proprietary tool based on QUADAS, PROBAST. After screening 4649 references, we identified 195 eligible studies. From artificial intelligence most used methods in emotion or affect recognition were Support Vector Machines (42.86%), Neural Network (21.43%), and k-Nearest Neighbors (11.67%). Among the most explored datasets were DEAP (10.26%), MAHNOB-HCI (10.26%), AMIGOS (6.67%) and DREAMER (2.56%). The most frequent cardiovascular signals involved electrocardiogram (63.16%), photoplethysmogram (15.79%), blood volume pressure (13.16%) and heart rate (6.58%). Sadness, fear, and anger were the most examined emotions. However, there is no standard set of investigated internal feelings. On average, authors explore 4.50 states (range from 4 to 24 feelings). Research using artificial intelligence in recognizing emotions or affect using cardiovascular signals shows an upward trend. There are significant variations in the quality of the datasets, the choice of states to detect, and the classifiers used for analysis. Research project supported by program Excellence initiative - research university for the University of Science and Technology. The authors declare that they have no conflict of interest

Mortality in patients after acute myocardial infarction managed by cardiologists and primary care physicians : a systematic review

Introduction Mortality following acute myocardial infarction (AMI) remains high despite of progress in invasive and noninvasive treatments. Objectives This study aimed to compare the outcomes of ambulatory treatment provided by cardiologists versus general practitioners (GPs) in post‑AMI patients. Patients and methods We conducted a systematic search in 3 electronic databases for interventional and observational studies that reported all‑cause mortality, mortality from cardiovascular causes, stroke, and myocardial infarction at long‑term follow‑up following AMI. We assessed the risk of bias of the included studies using the Risk of Bias in Nonrandomized Studies of Interventions (ROBINS‑I) tool. For randomized trials, we used the revised Cochrane risk of bias tool (RoB 2.0). Results Two nonrandomized studies fulfilled the inclusion criteria. We assessed these studies as having a moderate risk of bias. We did not pool the results owing to significant heterogeneity between the studies. Patients consulted by both a cardiologist and a GP were at lower risk of all‑cause death as compared with patients consulted by a cardiologist only (risk ratio [RR], 0.92; 95% CI, 0.85–0.99). Patients consulted by a cardiologist with or without GP consultation were at lower risk of all‑cause death compared with those consulted by a GP only in both studies (RR, 0.8; 95% CI, 0.75–0.85 and RR, 0.44; 95% CI, 0.41–0.47). Conclusions Patients after AMI consulted by both a cardiologist and a GP may beat lower risk of death compared with patients consulted by a GP or a cardiologist only. However, these findings are based on moderate‑quality nonrandomized studies. We found no evidence on the relation between the specialization of the physician and the risk of cardiovascular death, stroke, or myocardial infarction in AMI survivors

Impact of maternal reproductive factors on cancer risks of offspring : a systematic review and meta-analysis of cohort studies

BackgroundA number of studies have reported on associations between reproductive factors, such as delivery methods, number of birth and breastfeeding, and incidence of cancer in children, but systematic reviews addressing this issue to date have important limitations, and no reviews have addressed the impact of reproductive factors on cancer over the full life course of offspring.MethodsWe performed a comprehensive search in MEDLINE, and Embase up to January 2020 and Web of Science up to 2018 July, including cohort studies reporting the association between maternal reproductive factors of age at birth, birth order, number of births, delivery methods, and breastfeeding duration and cancer in children. Teams of two reviewers independently extracted data and assessed risk of bias. We conducted random effects meta-analyses to estimate summary relative estimates, calculated absolute differences between those with and without risk factors, and used the GRADE approach to evaluate the certainty of evidence.ResultsFor most exposures and most cancers, we found no suggestion of a causal relation. We found low to very low certainty evidence of the following very small possible impact: higher maternal age at birth with adult multiple myeloma and lifetime uterine cervix cancer incidence; lower maternal age at birth with childhood overall cancer mortality (RR = 1.15, 95% CI = 1.01-1.30; AR/10,000 = 1, 95% CI = 0 to 2), adult leukemia and lifetime uterine cervix cancer incidence; higher birth order with adult melanoma, cervix uteri, corpus uteri, thyroid cancer incidence, lifetime lung, corpus uteri, prostate, testis, sarcoma, thyroid cancer incidence; larger number of birth with childhood brain (RR = 1.27, 95% CI = 1.06-1.52; AR/10,000 = 1, 95% CI = 0 to 2), leukemia (RR = 2.11, 95% CI = 1.62-2.75; AR/10,000 = 9, 95% CI = 5 to 14), lymphoma (RR = 4.66, 95% CI = 1.40-15.57; AR/10,000 = 11, 95% CI = 1 to 44) incidence, adult stomach, corpus uteri cancer incidence and lung cancer mortality, lifetime stomach, lung, uterine cervix, uterine corpus, multiple myeloma, testis cancer incidence; Caesarean delivery with childhood kidney cancer incidence (RR = 1.25, 95% CI = 1.01-1.55; AR/10,000 = 0, 95% CI = 0 to 1); and breastfeeding with adult colorectal cancer incidence.ConclusionVery small impacts existed between a number of reproductive factors and cancer incidence and mortality in children and the certainty of evidence was low to very low primarily due to observational design

Predictors of Higher Quality of Systematic Reviews Addressing Nutrition and Cancer Prevention

Systematic reviews/meta-analyses (SR/MAs) are considered a reliable source of information in healthcare. We aimed to explore the association of several characteristics of SR/MAs addressing nutrition in cancer prevention and their quality/risk of bias (using assessments from AMSTAR-2 and ROBIS tools). The analysis included 101 SR/MAs identified in a systematic survey. Associations of each specified characteristic (e.g., information about the protocol, publication year, reported use of GRADE, or other methods for assessing overall certainty of evidence) with the number of AMSTAR-2 not met (‘No’ responses) and the number of ROBIS items met (‘Probably Yes’ or “Yes’ responses) were examined. Poisson regression was used to identify predictors of the number of ‘No’ answers (indicating lower quality) for all AMSTAR-2 items and the number of ‘Yes’ or ‘Probably Yes’ answers (indicating higher quality/lower concern for bias) for all ROBIS items. Logistic regression was used to identify variables associated with at least one domain assessed as ‘low concern for bias’ in the ROBIS tool. In multivariable analysis, SR/MAs not reporting use of any quality/risk of bias assessment instrument for primary studies were associated with a higher number of ‘No’ answers for all AMSTAR-2 items (incidence rate ratio (IRR) 1.26, 95% confidence interval (CI) 1.09–1.45), and a lower number of ‘Yes’ or ‘Probably Yes’ answers for all ROBIS items (IRR 0.76, 95% CI 0.66–0.87). Providing information about the protocol and search for unpublished studies was associated with a lower number of ‘No’ answers (IRR 0.73, 95% CI 0.56–0.97 and IRR 0.75, 95% CI 0.59–0.95, respectively) and a higher number of ‘Yes’ or ‘Probably Yes’ answers (IRR 1.43, 95% CI 1.17–1.74 and IRR 1.28, 95% CI 1.07–1.52, respectively). Not using at least one quality/risk of bias assessment tool for primary studies within an SR/MA was associated with lower odds that a study would be assessed as ‘low concern for bias’ in at least one ROBIS domain (odds ratio 0.061, 95% CI 0.007–0.527). Adherence to methodological standards in the development of SR/MAs was associated with a higher overall quality of SR/MAs addressing nutrition for cancer prevention

Artificial intelligence for COVID-19 detection in medical imaging - diagnostic measures and wasting : a Systematic Umbrella Review

The COVID-19 pandemic has sparked a barrage of primary research and reviews. We investigated the publishing process, time and resource wasting, and assessed the methodological quality of the reviews on artificial intelligence techniques to diagnose COVID-19 in medical images. We searched nine databases from inception until 1 September 2020. Two independent reviewers did all steps of identification, extraction, and methodological credibility assessment of records. Out of 725 records, 22 reviews analysing 165 primary studies met the inclusion criteria. This review covers 174,277 participants in total, including 19,170 diagnosed with COVID-19. The methodological credibility of all eligible studies was rated as critically low: 95% of papers had significant flaws in reporting quality. On average, 7.24 (range: 0–45) new papers were included in each subsequent review, and 14% of studies did not include any new paper into consideration. Almost three-quarters of the studies included less than 10% of available studies. More than half of the reviews did not comment on the previously published reviews at all. Much wasting time and resources could be avoided if referring to previous reviews and following methodological guidelines. Such information chaos is alarming. It is high time to draw conclusions from what we experienced and prepare for future pandemics