One of the most challenging and prevalent side effects of LVAD implantation is that of right heart failure (RHF) that may develop afterwards. The purpose of this study is to review and highlight recent advances in the uses of AI in evaluating RHF after LVAD implantation. The available literature was scanned using certain key words (artificial intelligence, machine learning, left ventricular assist device, prediction of right heart failure after LVAD) was scanned within Pubmed, Web of Science, and Google Scholar databases. Conventional risk scoring systems were also summarized, with their pros and cons being included in the results section of this study in order to provide a useful contrast with AI-based models. There are certain interesting and innovative ML approaches towards RHF prediction among the studies reviewed as well as more straightforward approaches that identified certain important predictive clinical parameters. Despite their accomplishments, the resulting AUC scores were far from ideal for these methods to be considered fully sufficient. The reasons for this include the low number of studies, standardized data availability, and lack of prospective studies. Another topic briefly discussed in this study is that relating to the ethical and legal considerations of using AI-based systems in healthcare. In the end, we believe that it would be beneficial for clinicians to not ignore these developments despite the current research indicating more time is needed for AI-based prediction models to achieve a better performance.

(1) Background: The purpose of this study is to review and highlight recent advances in diagnostic uses of artificial intelligence (AI) for cardiac diseases, in order to emphasize expected benefits to both patients and healthcare specialists; (2) Methods: We focused on four key search terms (Cardiac Disease, diagnosis, artificial intelligence, machine learning) across three different databases (Pubmed, European Heart Journal, Science Direct) between 2017–2022 in order to reach relatively more recent developments in the field. Our review was structured in order to clearly differentiate publications according to the disease they aim to diagnose (coronary artery disease, electrophysiological and structural heart diseases); (3) Results: Each study had different levels of success, where declared sensitivity, specificity, precision, accuracy, area under curve and F1 scores were reported for every article reviewed; (4) Conclusions: the number and quality of AI-assisted cardiac disease diagnosis publications will continue to increase through each year. We believe AI-based diagnosis should only be viewed as an additional tool assisting doctors’ own judgement, where the end goal is to provide better quality of healthcare and to make getting medical help more affordable and more accessible, for everyone, everywhere.

Aim: Annually, millions of people die from kidney diseases making it a serious public health concern. Patients with renal insufficiency have hemostatic defects that enhance their risk of thrombosis and bleeding. Evaluating the advantages and hazards of anticoagulant thromboprophylaxis is challenging.Material and Methods: By using the fuzzy preference ranking organization method for enrichment evaluation (fuzzy PROMETHEE) and fuzzy multi-criteria optimization and compromise solution (fuzzy VIKOR), which are analytical multi-criteria decision-making (MCDM) techniques, this study aims to analyze diverse types of anticoagulants; specifically, Vitamin K-Antagonist (warfarin) and direct oral anticoagulants namely; Dabigatran, Rivaroxaban, Apixaban, and Edoxaban, by considering various criteria.Results: Direct oral anticoagulants outperformed warfarin in terms of safety and effectiveness. Among all the analyzed anticoagulants, Apixaban was found to be the best-ranked anticoagulant with both fuzzy PROMETHEE and fuzzy VIKOR, with a net flow of 0.0064 and the lowest Qj value of 0.0000, respectively, compared with other alternatives depending on the selected criteria while warfarin was found to be the least ranked anticoagulant with a net flow of -0.0043 and the highest number of Qj being 1, respectively.Conclusion: The MCDM techniques used were effective, highly accurate, and sensitive in the comparison of anticoagulant therapy for venous thrombosis in renal insufficiency based on the chosen criteria. The proper use of a safe and effective anticoagulant will boost better performance not only in patients’ survival and well-being but also in the health care center.

Right ventricular heart failure (RVHF) mostly occurs due to the failure of the left-side of the heart. RVHF is a serious disease that leads to swelling of the abdomen, ankles, liver, kidneys, and gastrointestinal (GI) tract. A total of 506 heart-failure subjects from the Faculty of Medicine, Cardiovascular Surgery Department, Ege University, Turkey, who suffered from a severe heart failure and are currently receiving support from a ventricular assistance device, were involved in the current study. Therefore, the current study explored the application of both the direct and inverse modelling approaches, based on the correlation analysis feature extraction performance of various pre-operative variables of the subjects, for the prediction of RVHF. The study equally employs both single and hybrid paradigms for the prediction of RVHF using different pre-operative variables. The visualized and quantitative performance of the direct and inverse modelling approach indicates the robust prediction performance of the hybrid paradigms over the single techniques in both the calibration and validation steps. Whereby, the quantitative performance of the hybrid techniques, based on the Nash–Sutcliffe coefficient (NC) metric, depicts its superiority over the single paradigms by up to 58.7%/75.5% and 80.3%/51% for the calibration/validation phases in the direct and inverse modelling approaches, respectively. Moreover, to the best knowledge of the authors, this is the first study to report the implementation of direct and inverse modelling on clinical data. The findings of the current study indicates the possibility of applying these novel hybridised paradigms for the prediction of RVHF using pre-operative variables.

Cardiovascular surgery includes operative procedures associated with the heart and big vessels in the body. Depending on their high risk and complexity., the requirement of blood transfusion is essential in those operations. Although technology is improving day by day in this regard., there is still no fully accepted blood transfusion method. The goal of this study is to evaluate blood preservation and transfusion techniques in cardiovascular surgery using a multi-criteria decision-making (MCDM) method namely Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE). In this study., the PROMETHEE evaluates several options according to these criteria: volume processed and salvage amount., hemoglobin levels after the process., residual heparin in the end process., citrate toxicity., trained specialist allergic., immunologic reactions., and use for special patients. The post-perfusion blood had outranking net flow of 0.0081 and is the most appropriate option for the preservation of blood in cardiovascular surgery. Therefore., modified ultrafiltration ranked second with net flow of 0.0066 and preoperative autologous donation was the least ranked with net flows of -0. 0083. This study requires opinions from experts. The most difficult task is assigning weights to criteria and evaluating the alternatives. Fuzzy PROMETHEE is offered as a solution in evaluating blood preservation techniques used in cardiovascular surgery.