Artificial intelligence-enhanced electrocardiography for accurate diagnosis and management of cardiovascular diseases

Electrocardiography (ECG), improved by artificial intelligence (AI), has become a potential technique for the precise diagnosis and treatment of cardiovascular disorders. The conventional ECG is a frequently used, inexpensive, and easily accessible test that offers important information about the physiological and anatomical state of the heart. However, the ECG can be interpreted differently by humans depending on the interpreter's level of training and experience, which could make diagnosis more difficult. Using AI, especially deep learning convolutional neural networks (CNNs), to look at single, continuous, and intermittent ECG leads that has led to fully automated AI models that can interpret the ECG like a human, possibly more accurately and consistently. These AI algorithms are effective non-invasive biomarkers for cardiovascular illnesses because they can identify subtle patterns and signals in the ECG that may not be readily apparent to human interpreters. The use of AI in ECG analysis has several benefits, including the quick and precise detection of problems like arrhythmias, silent cardiac illnesses, and left ventricular failure. It has the potential to help doctors with interpretation, diagnosis, risk assessment, and illness management. Aside from that, AI-enhanced ECGs have been demonstrated to boost the identification of heart failure and other cardiovascular disorders, particularly in emergency department settings, allowing for quicker and more precise treatment options. The use of AI in cardiology, however, has several limitations and obstacles, despite its potential. The effective implementation of AI-powered ECG analysis is limited by issues such as systematic bias. Biases based on age, gender, and race result from unbalanced datasets. A model's performance is impacted when diverse demographics are inadequately represented. Potentially disregarded age-related ECG variations may result from skewed age data in training sets. ECG patterns are affected by physiological differences between the sexes; a dataset that is inclined toward one sex may compromise the accuracy of the others. Genetic variations influence ECG readings, so racial diversity in datasets is significant. Furthermore, issues such as inadequate generalization, regulatory barriers, and interpretability concerns contribute to deployment difficulties. The lack of robustness in models when applied to disparate populations frequently hinders their practical applicability. The exhaustive validation required by regulatory requirements causes a delay in deployment. Difficult models that are not interpretable erode the confidence of clinicians. Diverse dataset curation, bias mitigation strategies, continuous validation across populations, and collaborative efforts for regulatory approval are essential for the successful deployment of AI ECG in clinical settings and must be undertaken to address these issues. To guarantee a safe and successful deployment in clinical practice, the use of AI in cardiology must be done with a thorough understanding of the algorithms and their limits. In summary, AI-enhanced electrocardiography has enormous potential to improve the management of cardiovascular illness by delivering precise and timely diagnostic insights, aiding clinicians, and enhancing patient outcomes. Further study and development are required to fully realize AI's promise for improving cardiology practices and patient care as technology continues to advance.Other Information Published in: Journal of Electrocardiology License: http://creativecommons.org/licenses/by/4.0/See article on publisher's website: https://dx.doi.org/10.1016/j.jelectrocard.2024.01.006</p

Efficacy and safety of topical ruxolitinib cream for the treatment of vitiligo: a systematic review and meta-analysis of randomized controlled trials

Vitiligo is an autoimmune condition with an estimated prevalence of 0.5%–2% worldwide.1 Besides the visible cosmetic concerns associated with vitiligo, it is marked by psychological concerns such as anxiety and distress leading to reduced quality of life.1 The disease is known to be mediated by interferon-gamma (IFN-γ) which acts through the Janus kinase-signal transducer and activator of transcription (JAK–STAT) pathway to recruit CD8+ T cells, which in turn drive the cytotoxicity directed against melanocytes via detachment and apoptosis, leading to the characteristic white skin patches.1 Ruxolitinib acts as a JAK1 and JAK2 inhibitor to suppress the IFN-γ-mediated pathway and prevent melanocyte damage, allowing them to heal and re-pigment.2 Phase 3 trials of ruxolitinib have recently been published and demonstrate encouraging outcomes in vitiligo patients. This meta-analysis aims to systematically collate outcomes from the relatively limited data available and evaluate the efficacy and safety of ruxolitinib in vitiligo patients.</p

Datasheet1_Efficacy and safety of sodium-glucose cotransporter-2 inhibitors for heart failure with mildly reduced or preserved ejection fraction: a systematic review and meta-analysis of randomized controlled trials.docx

AimsWe sought to conduct a meta-analysis to evaluate the efficacy and safety of sodium-glucose cotransporter-2 inhibitors (SGLT2i) in patients with heart failure (HF) with preserved ejection fraction (HFpEF) and HF with mildly reduced ejection fraction (HFmrEF).MethodsWe searched the Cochrane Library, MEDLINE (via PubMed), Embase, and ClinicalTrials.gov till March 2023 to retrieve all randomized controlled trials of SGLT2i in patients with HFpEF or HFmrEF. Risk ratios (RRs) and standardized mean differences (SMDs) with their 95% confidence intervals (95% CIs) were pooled using a random-effects model.ResultsWe included data from 14 RCTs. SGLT2i reduced the risk of the primary composite endpoint of first HF hospitalization or cardiovascular death (RR 0.81, 95% CI: 0.76, 0.87; I2 = 0%); these results were consistent across the cohorts of HFmrEF and HFpEF patients. There was no significant decrease in the risk of cardiovascular death (RR 0.96, 95% CI: 0.82, 1.13; I2 = 36%) and all-cause mortality (RR 0.97, 95% CI: 0.89, 1.05; I2 = 0%). There was a significant improvement in the quality of life in the SGLT2i group (SMD 0.13, 95% CI: 0.06, 0.20; I2 = 51%).ConclusionThe use of SGLT2i is associated with a lower risk of the primary composite outcome and a higher quality of life among HFpEF/HFmrEF patients. However, further research involving more extended follow-up periods is required to draw a comprehensive conclusion.Systematic Review RegistrationPROSPERO (CRD42022364223).</p

Data_Sheet_1_Knowledge, attitude, and practice of artificial intelligence among doctors and medical students in Syria: A cross-sectional online survey.PDF

Artificial intelligence has been prevalent recently as its use in the medical field is noticed to be increased. However, middle east countries like Syria are deficient in multiple AI implementation methods in the field of medicine. So, holding these AI implementation methods in the medical field is necessary, which may be incredibly beneficial for making diagnosis more accessible and help in the treatment. This paper intends to determine AI's knowledge, attitude, and practice among doctors and medical students in Syria. A questionnaire conducted an online cross-sectional study on the google form website consisting of demographic data, knowledge, and perception of AI. There were 1,494 responses from both doctors and medical students. We included Syrian medical students and doctors who are currently residing in Syria. Of the 1,494 participants, 255 (16.9%) are doctors, while the other 1,252 (83.1%) are undergraduate medical students. About 1,055 (70%) participants have previous knowledge about AI. However, only 357 (23.7%) participants know about its application in the medical field. Most have shown positive attitudes toward its necessity in the medical field; 689 (45.7%) individuals strongly agree, and 628 (41.7%) agree. The undergraduate students had 3.327 times more adequate knowledge of AI than students in the first year. In contrast, the undergraduate 6th-year students had 2.868 times the attitude toward AI higher than students in the first year. The residents and assistant professors had 2.371 and 4.422 times the practice of AI higher than students, respectively. Although most physicians and medical students do not sufficiently understand AI and its significance in the medical field, they have favorable views regarding using AI in the medical field. Syrian medical authorities and international organizations should suggest including artificial intelligence in the medical field, particularly when training residents and fellowship physicians.</p