What Every Intensivist should Know about Impairment of Cardiac Function and Arrhythmias in Liver Disease Patients: A Review.

OBJECTIVES: Impairment of cardiac function and arrhythmias often coexist in patients with liver diseases. Many studies have proved this coexistence and put forward various theories toward its pathophysiology. This narrative review tries to find the answers with supporting evidence on five main questions:Do high serum bilirubin levels have a strong association with cardiac arrhythmias?Can corrected QT interval (QTc) be relied upon for predicting a risk factor toward imminent arrhythmias?Is there an association between QTc prolongation and mortality?Are high serum bilirubin and cardiac dysfunction closely associated?What is the probable pathophysiology behind this association? MATERIALS AND METHODS: Clinical evidence was obtained by using search engines, namely, Cochrane Library, PubMed, and Google Scholar. Studies published in journals in the English language, between January 1969 and December 2019, which mentioned the relationship between cardiac arrhythmia and liver disease, were included. We used the keywords: jaundice, bilirubin, arrhythmia, ECG, QTc interval, QT dispersion, liver, and cirrhosis. Relevant animal or human studies answering the five main questions were extracted and reviewed. CONCLUSION: The evidence included in our review sheds light on the fact that approximately 50% of liver cirrhosis cases develop cirrhotic cardiomyopathy (CC) and there has been an association between liver abnormalities and cardiac pathology. The present review also supports that there exists a strong association between high levels of serum bilirubin levels and cardiac arrhythmias, QTc value can be relied upon as a risk factor for predicting imminent arrhythmias, and that it is associated with mortality. Its basic pathophysiology can be explained by the potential action of bile acids in prolonging the QT interval. It also causes cardiac hypertrophy and apoptosis of cardiomyocytes leading to cardiac dysfunction. HOW TO CITE THIS ARTICLE: Arya S, Kumar P, Tiwari B, Belwal S, Saxena S, Abbas H. What Every Intensivist should Know about Impairment of Cardiac Function and Arrhythmias in Liver Disease Patients: A Review. Indian J Crit Care Med 2020;24(12):1251-1255.

Bioevaluation of (125) I Ocu-Prosta seeds for application in prostate cancer brachytherapy.

BACKGROUND & OBJECTIVES: In recent years, brachytherapy involving permanent radioactive seed implantation has emerged as an effective modality for the management of cancer of prostate. 125 I-Ocu-Prosta seeds were indigenously developed and studies were carried out to assess the safety of the indigenously developed 125 I-Ocu-Prosta seeds for treatment of prostate cancer. METHODS: Animal experiments were performed to assess the likelihood of in vivo release of 125 I from radioactive seeds and migration of seeds implanted in the prostate gland of the rabbit. In vivo release of 125 I activity was monitored by serial blood sampling from the auricular vein and subsequent measurement of 125 I activity. Serial computed tomography (CT) scans were done at regular intervals till 6 months post implant to assess the physical migration of the seeds. RESULTS: The laser welded seeds maintained their hermeticity and prevented the in vivo release of 125 I activity into the blood as no radioactivity was detected during follow up blood measurements. Our study showed that the miniature 125 I seeds were clearly resolved in CT images. Seeds remained within the prostate gland during the entire study period. Moreover, the seed displacement was minimal even within the prostate gland. INTERPRETATION & CONCLUSIONS: Our findings have demonstrated that indigenously developed 125 I-Ocu-Prosta seeds may be suitable for application in treatment of prostate cancer.

Cor triatriatum sinistrum presenting as cardioembolic stroke: an unusual cause of adolescent hemiparesis.

Cor triatriatum sinistrum is a rare congenital cardiac malformation, in which the left atrium (LA) is divided into two distinct chambers by a fibromuscular membrane. Classically, the proximal (upper or superior) chamber of the LA receives pulmonary venous connections, whereas the distal (lower or inferior) chamber contains LA appendage and true atrial septum containing fossa ovalis. The distal chamber is in continuity with the atrioventricular valve, while the two chambers communicate through a defect in the membrane. The hemodynamics of cor triatriatum sinistrum are similar to that of mitral stenosis due to obstructive property of membrane. The majority of reported cases of cor triatriatum occur in infants with symptoms of pulmonary venous obstruction, with adult cases being rare. Herein, we describe an unusual case of cor triatriatum in a 17-year-old boy who presented for the first time with embolic cerebral infarction with left hemiparesis.

Hyperkalemia: A rare cause of acute flaccid quadriparesis.

Acute flaccid quadriparesis secondary to hyperkalemia is a very rare and serious but reversible medical emergency. We present a case of a 73-year-old female who was admitted with rapidly progressive ascending paraparesis progressing to quadriparesis in about 10 h due to hyperkalemia. Patient was treated with antihyperkalemic measures. Her power improved dramatically as potassium levels normalized and she had an uneventful recovery.

AI-driven estimation of O6 methylguanine-DNA-methyltransferase (MGMT) promoter methylation in glioblastoma patients: a systematic review with bias analysis.

BACKGROUND: Accurate and non-invasive estimation of MGMT promoter methylation status in glioblastoma (GBM) patients is of paramount clinical importance, as it is a predictive biomarker associated with improved overall survival (OS). In response to the clinical need, recent studies have focused on the development of non-invasive artificial intelligence (AI)-based methods for MGMT estimation. In this systematic review, we not only delve into the technical aspects of these AI-driven MGMT estimation methods but also emphasize their profound clinical implications. Specifically, we explore the potential impact of accurate non-invasive MGMT estimation on GBM patient care and treatment decisions. METHODS: Employing a PRISMA search strategy, we identified 33 relevant studies from reputable databases, including PubMed, ScienceDirect, Google Scholar, and IEEE Explore. These studies were comprehensively assessed using 21 diverse attributes, encompassing factors such as types of imaging modalities, machine learning (ML) methods, and cohort sizes, with clear rationales for attribute scoring. Subsequently, we ranked these studies and established a cutoff value to categorize them into low-bias and high-bias groups. RESULTS: By analyzing the 'cumulative plot of mean score' and the 'frequency plot curve' of the studies, we determined a cutoff value of 6.00. A higher mean score indicated a lower risk of bias, with studies scoring above the cutoff mark categorized as low-bias (73%), while 27% fell into the high-bias category. CONCLUSION: Our findings underscore the immense potential of AI-based machine learning (ML) and deep learning (DL) methods in non-invasively determining MGMT promoter methylation status. Importantly, the clinical significance of these AI-driven advancements lies in their capacity to transform GBM patient care by providing accurate and timely information for treatment decisions. However, the translation of these technical advancements into clinical practice presents challenges, including the need for large multi-institutional cohorts and the integration of diverse data types. Addressing these challenges will be critical in realizing the full potential of AI in improving the reliability and accessibility of MGMT estimation while lowering the risk of bias in clinical decision-making.

Monte Carlo-based dosimetry of proposed bi-radionuclide (125I and 106Ru/106Rh) eye plaque: A feasibility study.

BACKGROUND: Combining the sharp dose fall off feature of beta-emitting 106Ru/106Rh radionuclide with larger penetration depth feature of photon-emitting125I radionuclide in a bi-radionuclide plaque, prescribed dose to the tumor apex can be delivered while maintaining the tumor dose uniformity and sparing the organs at risk. The potential advantages of bi-radionuclide plaque could be of interest in context of ocular brachytherapy. PURPOSE: The aim of the study is to evaluate the dosimetric advantages of a proposed bi-radionuclide plaque for two different designs, consisting of indigenous 125I seeds and 106Ru/106Rh plaque, using Monte Carlo technique. The study also explores the influence of other commercial 125I seed models and presence or absence of silastic/acrylic seed carrier on the calculated dose distributions. The study further included the calculation of depth dose distributions for the bi-radionuclide eye plaque for which experimental data are available. METHODS: The proposed bi-radionuclide plaque consists of a 1.2-mm-thick silver (Ag) spherical shell with radius of curvature of 12.5 mm, 20 µm-thick-106Ru/106Rh encapsulated between 0.2 mm Ag disk, and a 0.1-mm-thick Ag window, and water-equivalent gel containing 12 symmetrically arranged 125I seeds. Two bi-radionuclide plaque models investigated in the present study are designated as Design I and Design II. In Design I, 125I seeds are placed on the top of the plaque, while in Design II 106Ru/106Rh source is positioned on the top of the plaque. In Monte Carlo calculations, the plaque is positioned in a spherical water phantom of 30 cm diameter. RESULTS: The proposed bi-radionuclide eye plaque demonstrated superior dose distributions as compared to 125I or 106Ru plaque for tumor thicknesses ranges from 5 to 10 mm. Amongst the designs, dose at a given voxel for Design I is higher as compared to the corresponding voxel dose for Design II. This difference is attributed to the higher degree of attenuation of 125I photons in Ag as compared to beta particles. Influence of different 125I seed models on the normalized lateral dose profiles of Design I (in the absence of carrier) is negligible and within 5% on the central axis depth dose distribution as compared to the corresponding values of the plaque that has indigenous 125I seeds. In the presence of a silastic/acrylic seed carrier, the normalized central axis dose distributions of Design I are smaller by 3%-12% as compared to the corresponding values in the absence of a seed carrier. For the published bi-radionuclide plaque model, good agreement is observed between the Monte Carlo-calculated and published measured depth dose distributions for clinically relevant depths. CONCLUSION: Regardless of the type of 125I seed model utilized and whether silastic/acrylic seed carrier is present or not, Design I bi-radionuclide plaque offers superior dose distributions in terms of tumor dose uniformity, rapid dose fall off and lesser dose to nearby critical organs at risk over the Design II plaque. This shows that Design I bi-radionuclide plaque could be a promising alternative to 125I plaque for treatment of tumor sizes in the range 5 to 10 mm.

Iodine-125 brachytherapy for choroidal melanoma by using Ocuprosta seeds with indigenous non-collimated plaques: Our initial experience.

PURPOSE: Brachytherapy is the gold-standard treatment for choroidal melanoma. This study evaluated iodine-125 brachytherapy by using Ocuprosta seeds with indigenous non-collimated plaques in Asian patients. METHODS: Retrospective single-center study in a tertiary care hospital of 12 eyes with choroidal melanoma in 12 Asian patients who underwent brachytherapy with Ocuprosta seeds fixed on non-collimated plaques and had a follow-up of at least 32 months (mean: 42.4 ± 9.5 months; median: 40 months). Radiotherapy was planned after developing the digital 3D model of the tumor within the eye by using radiological images and clinical pictures. Ocuprosta iodine-125 seeds were used on indigenous non-collimated gold plaques to deliver the radiation for precalculated time. "Successful outcome" was taken as a decrease in the volume of the tumor, and "unsuccessful outcome" was defined as no change in the tumor volume or increase in the tumor volume at 24 months after brachytherapy. RESULTS: The mean decrease in tumor volume was 21% (914.5 ± 912.2 mm3 to 495.7 ± 633.6 mm3) after brachytherapy, which correlated with the baseline volume of the tumor. Ten eyes (83.3%) showed a reduction in tumor volume, whereas two eyes showed an increase in the volume of the tumor after brachytherapy. One of the cases with a reduction in tumor size developed neovascular glaucoma. Enucleation was done in three eyes. A globe salvage rate of 75% and tumor regression rate of 83% were seen in the present study using Ocuprosta seeds. CONCLUSIONS: Iodine-125 brachytherapy with uncollimated indigenous gold plaques is an effective treatment modality for choroidal melanomas in Asian patients.

Allies in value-based care.

Payers and providers can collaborate on three models of quality care.

Effect of learning parameters on the performance of the U-Net architecture for cell nuclei segmentation from microscopic cell images.

Nuclei segmentation of cells is the preliminary and essential step of pathological image analysis. However, robust and accurate cell nuclei segmentation is challenging due to the enormous variability of staining, cell sizes, morphologies, cell adhesion or overlapping of the nucleus. The automation process to find the cell's nuclei is a giant leap in this direction and has an important step toward bioimage analysis using software tools. This article extensively analyzes deep U-Net architecture and has been applied to the Data Science Bowl dataset to segment the cell nuclei. The dataset undergoes various preprocessing tasks such as resizing, intensity normalization and data augmentation prior to segmentation. The complete dataset then undergoes the rigorous training and validation process to find the optimized hyperparameters and then the optimized model selection. The mean (m) ± standard deviation (SD) of Intersection over Union (IoU) and F1-score (Dice score) have been calculated along with accuracy during the training and validation process, respectively. The optimized U-Net model results in a training IoU of 0.94 ± 0.16 (m ± SD), an F1-score of 0.94 ± 0.17 (m ± SD), a training accuracy of 95.54 and validation accuracy of 95.45. With this model, we applied a completely independent test cohort of the dataset and obtained the mean IOU of 0.93, F1-score of 0.9311, and mean accuracy of 94.12, respectively to measure the segmentation performance.

Fused deep learning paradigm for the prediction of o6-methylguanine-DNA methyltransferase genotype in glioblastoma patients: A neuro-oncological investigation.

BACKGROUND: The O6-methylguanine-DNA methyltransferase (MGMT) is a deoxyribonucleic acid (DNA) repairing enzyme that has been established as an essential clinical brain tumor biomarker for Glioblastoma Multiforme (GBM). Knowing the status of MGMT methylation biomarkers using multi-parametric MRI (mp-MRI) helps neuro-oncologists to analyze GBM and its treatment plan. METHOD: The hand-crafted radiomics feature extraction of GBM's subregions, such as edema(ED), tumor core (TC), and enhancing tumor (ET) in the machine learning (ML) framework, was investigated using support vector machine(SVM), K-Nearest Neighbours (KNN), random forest (RF), LightGBM, and extreme gradient boosting (XGB). For tissue-level analysis of the promotor genes in GBM, we used the deep residual neural network (ResNet-18) with 3D architecture, followed by EfficientNet-based investigation for variants as B0 and B1. Lastly, we analyzed the fused deep learning (FDL) framework that combines ML and DL frameworks. RESULT: Structural mp-MRI consisting of T1, T2, FLAIR, and T1GD having a size of 400 and 185 patients, respectively, for discovery and replication cohorts. Using the CV protocol in the ResNet-3D framework, MGMT methylation status prediction in mp-MRI gave the AUC of 0.753 (p < 0.0001) and 0.72 (p < 0.0001) for the discovery and replication cohort, respectively. We presented that the FDL is ∼7% superior to solo DL and ∼15% to solo ML. CONCLUSION: The proposed study aims to provide solutions for building an efficient predictive model of MGMT for GBM patients using deep radiomics features obtained from mp-MRI with the end-to-end ResNet-18 3D and FDL imaging signatures.

Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look.

BACKGROUND AND MOTIVATION: Coronary artery disease (CAD) has the highest mortality rate; therefore, its diagnosis is vital. Intravascular ultrasound (IVUS) is a high-resolution imaging solution that can image coronary arteries, but the diagnosis software via wall segmentation and quantification has been evolving. In this study, a deep learning (DL) paradigm was explored along with its bias. METHODS: Using a PRISMA model, 145 best UNet-based and non-UNet-based methods for wall segmentation were selected and analyzed for their characteristics and scientific and clinical validation. This study computed the coronary wall thickness by estimating the inner and outer borders of the coronary artery IVUS cross-sectional scans. Further, the review explored the bias in the DL system for the first time when it comes to wall segmentation in IVUS scans. Three bias methods, namely (i) ranking, (ii) radial, and (iii) regional area, were applied and compared using a Venn diagram. Finally, the study presented explainable AI (XAI) paradigms in the DL framework. FINDINGS AND CONCLUSIONS: UNet provides a powerful paradigm for the segmentation of coronary walls in IVUS scans due to its ability to extract automated features at different scales in encoders, reconstruct the segmented image using decoders, and embed the variants in skip connections. Most of the research was hampered by a lack of motivation for XAI and pruned AI (PAI) models. None of the UNet models met the criteria for bias-free design. For clinical assessment and settings, it is necessary to move from a paper-to-practice approach.

Severe suicidal digoxin toxicity managed with resin hemoperfusion: A case report.

Owing to its wide and easy availability, digoxin has got a significant abuse potential and may be used for suicidal purposes. Digoxin-specific antibody (Fab) fragments have become the mainstay of therapy for severe digoxin toxicity and have significantly helped in reducing mortality. However, due to its high cost and limited availability alternative measures may need to be used to manage severe intoxications especially in countries like India, where Fab fragments are unavailable. Here, we present a case of a young female who presented to our casualty with alleged history of consumption of 17.5 mg of digoxin tablets. After admission to ICU, she developed atrioventricular blocks with hemodynamic instability which had to be managed with temporary pacemaker. Her serum digoxin levels were high (12.63 ng/ml) and in the absence of Fab fragments, resin hemoperfusion was done which drastically reduced the serum digoxin levels and reverted the symptoms.

An artificial intelligence framework and its bias for brain tumor segmentation: A narrative review.

BACKGROUND: Artificial intelligence (AI) has become a prominent technique for medical diagnosis and represents an essential role in detecting brain tumors. Although AI-based models are widely used in brain lesion segmentation (BLS), understanding their effectiveness is challenging due to their complexity and diversity. Several reviews on brain tumor segmentation are available, but none of them describe a link between the threats due to risk-of-bias (RoB) in AI and its architectures. In our review, we focused on linking RoB and different AI-based architectural Cluster in popular DL framework. Further, due to variance in these designs and input data types in medical imaging, it is necessary to present a narrative review considering all facets of BLS. APPROACH: The proposed study uses a PRISMA strategy based on 75 relevant studies found by searching PubMed, Scopus, and Google Scholar. Based on the architectural evolution, DL studies were subsequently categorized into four classes: convolutional neural network (CNN)-based, encoder-decoder (ED)-based, transfer learning (TL)-based, and hybrid DL (HDL)-based architectures. These studies were then analyzed considering 32 AI attributes, with clusters including AI architecture, imaging modalities, hyper-parameters, performance evaluation metrics, and clinical evaluation. Then, after these studies were scored for all attributes, a composite score was computed, normalized, and ranked. Thereafter, a bias cutoff (AP(ai)Bias 1.0, AtheroPoint, Roseville, CA, USA) was established to detect low-, moderate- and high-bias studies. CONCLUSION: The four classes of architectures, from best-to worst-performing, are TL > ED > CNN > HDL. ED-based models had the lowest AI bias for BLS. This study presents a set of three primary and six secondary recommendations for lowering the RoB.

Bias Investigation in Artificial Intelligence Systems for Early Detection of Parkinson's Disease: A Narrative Review.

Background and Motivation: Diagnosis of Parkinson's disease (PD) is often based on medical attention and clinical signs. It is subjective and does not have a good prognosis. Artificial Intelligence (AI) has played a promising role in the diagnosis of PD. However, it introduces bias due to lack of sample size, poor validation, clinical evaluation, and lack of big data configuration. The purpose of this study is to compute the risk of bias (RoB) automatically. METHOD: The PRISMA search strategy was adopted to select the best 39 AI studies out of 85 PD studies closely associated with early diagnosis PD. The studies were used to compute 30 AI attributes (based on 6 AI clusters), using AP(ai)Bias 1.0 (AtheroPointTM, Roseville, CA, USA), and the mean aggregate score was computed. The studies were ranked and two cutoffs (Moderate-Low (ML) and High-Moderate (MH)) were determined to segregate the studies into three bins: low-, moderate-, and high-bias. RESULT: The ML and HM cutoffs were 3.50 and 2.33, respectively, which constituted 7, 13, and 6 for low-, moderate-, and high-bias studies. The best and worst architectures were "deep learning with sketches as outcomes" and "machine learning with Electroencephalography," respectively. We recommend (i) the usage of power analysis in big data framework, (ii) that it must undergo scientific validation using unseen AI models, and (iii) that it should be taken towards clinical evaluation for reliability and stability tests. CONCLUSION: The AI is a vital component for the diagnosis of early PD and the recommendations must be followed to lower the RoB.

Recommender System for the Efficient Treatment of COVID-19 Using a Convolutional Neural Network Model and Image Similarity.

Background: Hospitals face a significant problem meeting patients' medical needs during epidemics, especially when the number of patients increases rapidly, as seen during the recent COVID-19 pandemic. This study designs a treatment recommender system (RS) for the efficient management of human capital and resources such as doctors, medicines, and resources in hospitals. We hypothesize that a deep learning framework, when combined with search paradigms in an image framework, can make the RS very efficient. Methodology: This study uses a Convolutional neural network (CNN) model for the feature extraction of the images and discovers the most similar patients. The input queries patients from the hospital database with similar chest X-ray images. It uses a similarity metric for the similarity computation of the images. Results: This methodology recommends the doctors, medicines, and resources associated with similar patients to a COVID-19 patients being admitted to the hospital. The performance of the proposed RS is verified with five different feature extraction CNN models and four similarity measures. The proposed RS with a ResNet-50 CNN feature extraction model and Maxwell-Boltzmann similarity is found to be a proper framework for treatment recommendation with a mean average precision of more than 0.90 for threshold similarities in the range of 0.7 to 0.9 and an average highest cosine similarity of more than 0.95. Conclusions: Overall, an RS with a CNN model and image similarity is proven as an efficient tool for the proper management of resources during the peak period of pandemics and can be adopted in clinical settings.

Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine.

Radiogenomics, a combination of "Radiomics" and "Genomics," using Artificial Intelligence (AI) has recently emerged as the state-of-the-art science in precision medicine, especially in oncology care. Radiogenomics syndicates large-scale quantifiable data extracted from radiological medical images enveloped with personalized genomic phenotypes. It fabricates a prediction model through various AI methods to stratify the risk of patients, monitor therapeutic approaches, and assess clinical outcomes. It has recently shown tremendous achievements in prognosis, treatment planning, survival prediction, heterogeneity analysis, reoccurrence, and progression-free survival for human cancer study. Although AI has shown immense performance in oncology care in various clinical aspects, it has several challenges and limitations. The proposed review provides an overview of radiogenomics with the viewpoints on the role of AI in terms of its promises for computational as well as oncological aspects and offers achievements and opportunities in the era of precision medicine. The review also presents various recommendations to diminish these obstacles.

Brain Tumor Characterization Using Radiogenomics in Artificial Intelligence Framework.

Brain tumor characterization (BTC) is the process of knowing the underlying cause of brain tumors and their characteristics through various approaches such as tumor segmentation, classification, detection, and risk analysis. The substantial brain tumor characterization includes the identification of the molecular signature of various useful genomes whose alteration causes the brain tumor. The radiomics approach uses the radiological image for disease characterization by extracting quantitative radiomics features in the artificial intelligence (AI) environment. However, when considering a higher level of disease characteristics such as genetic information and mutation status, the combined study of "radiomics and genomics" has been considered under the umbrella of "radiogenomics". Furthermore, AI in a radiogenomics' environment offers benefits/advantages such as the finalized outcome of personalized treatment and individualized medicine. The proposed study summarizes the brain tumor's characterization in the prospect of an emerging field of research, i.e., radiomics and radiogenomics in an AI environment, with the help of statistical observation and risk-of-bias (RoB) analysis. The PRISMA search approach was used to find 121 relevant studies for the proposed review using IEEE, Google Scholar, PubMed, MDPI, and Scopus. Our findings indicate that both radiomics and radiogenomics have been successfully applied aggressively to several oncology applications with numerous advantages. Furthermore, under the AI paradigm, both the conventional and deep radiomics features have made an impact on the favorable outcomes of the radiogenomics approach of BTC. Furthermore, risk-of-bias (RoB) analysis offers a better understanding of the architectures with stronger benefits of AI by providing the bias involved in them.

Cardiovascular/Stroke Risk Stratification in Parkinson's Disease Patients Using Atherosclerosis Pathway and Artificial Intelligence Paradigm: A Systematic Review.

Parkinson's disease (PD) is a severe, incurable, and costly condition leading to heart failure. The link between PD and cardiovascular disease (CVD) is not available, leading to controversies and poor prognosis. Artificial Intelligence (AI) has already shown promise for CVD/stroke risk stratification. However, due to a lack of sample size, comorbidity, insufficient validation, clinical examination, and a lack of big data configuration, there have been no well-explained bias-free AI investigations to establish the CVD/Stroke risk stratification in the PD framework. The study has two objectives: (i) to establish a solid link between PD and CVD/stroke; and (ii) to use the AI paradigm to examine a well-defined CVD/stroke risk stratification in the PD framework. The PRISMA search strategy selected 223 studies for CVD/stroke risk, of which 54 and 44 studies were related to the link between PD-CVD, and PD-stroke, respectively, 59 studies for joint PD-CVD-Stroke framework, and 66 studies were only for the early PD diagnosis without CVD/stroke link. Sequential biological links were used for establishing the hypothesis. For AI design, PD risk factors as covariates along with CVD/stroke as the gold standard were used for predicting the CVD/stroke risk. The most fundamental cause of CVD/stroke damage due to PD is cardiac autonomic dysfunction due to neurodegeneration that leads to heart failure and its edema, and this validated our hypothesis. Finally, we present the novel AI solutions for CVD/stroke risk prediction in the PD framework. The study also recommends strategies for removing the bias in AI for CVD/stroke risk prediction using the PD framework.

Understanding the bias in machine learning systems for cardiovascular disease risk assessment: The first of its kind review.

BACKGROUND: Artificial Intelligence (AI), in particular, machine learning (ML) has shown promising results in coronary artery disease (CAD) or cardiovascular disease (CVD) risk prediction. Bias in ML systems is of great interest due to its over-performance and poor clinical delivery. The main objective is to understand the nature of risk-of-bias (RoB) in ML and non-ML studies for CVD risk prediction. METHODS: PRISMA model was used to shortlisting 117 studies, which were analyzed to understand the RoB in ML and non-ML using 46 and 32 attributes, respectively. The mean score for each study was computed and then ranked into three ML and non-ML bias categories, namely low-bias (LB), moderate-bias (MB), and high-bias (HB), derived using two cutoffs. Further, bias computation was validated using the analytical slope method. RESULTS: Five types of the gold standard were identified in the ML design for CAD/CVD risk prediction. The low-moderate and moderate-high bias cutoffs for 24 ML studies (5, 10, and 9 studies for each LB, MB, and HB) and 14 non-ML (3, 4, and 7 studies for each LB, MB, and HB) were in the range of 1.5 to 1.95. BiasML< Biasnon-ML by ∼43%. A set of recommendations were proposed for lowering RoB. CONCLUSION: ML showed a lower bias compared to non-ML. For a robust ML-based CAD/CVD prediction design, it is vital to have (i) stronger outcomes like death or CAC score or coronary artery stenosis; (ii) ensuring scientific/clinical validation; (iii) adaptation of multiethnic groups while practicing unseen AI; (iv) amalgamation of conventional, laboratory, image-based and medication-based biomarkers.

A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review.

Background and Motivation: Cardiovascular disease (CVD) causes the highest mortality globally. With escalating healthcare costs, early non-invasive CVD risk assessment is vital. Conventional methods have shown poor performance compared to more recent and fast-evolving Artificial Intelligence (AI) methods. The proposed study reviews the three most recent paradigms for CVD risk assessment, namely multiclass, multi-label, and ensemble-based methods in (i) office-based and (ii) stress-test laboratories. Methods: A total of 265 CVD-based studies were selected using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) model. Due to its popularity and recent development, the study analyzed the above three paradigms using machine learning (ML) frameworks. We review comprehensively these three methods using attributes, such as architecture, applications, pro-and-cons, scientific validation, clinical evaluation, and AI risk-of-bias (RoB) in the CVD framework. These ML techniques were then extended under mobile and cloud-based infrastructure. Findings: Most popular biomarkers used were office-based, laboratory-based, image-based phenotypes, and medication usage. Surrogate carotid scanning for coronary artery risk prediction had shown promising results. Ground truth (GT) selection for AI-based training along with scientific and clinical validation is very important for CVD stratification to avoid RoB. It was observed that the most popular classification paradigm is multiclass followed by the ensemble, and multi-label. The use of deep learning techniques in CVD risk stratification is in a very early stage of development. Mobile and cloud-based AI technologies are more likely to be the future. Conclusions: AI-based methods for CVD risk assessment are most promising and successful. Choice of GT is most vital in AI-based models to prevent the RoB. The amalgamation of image-based strategies with conventional risk factors provides the highest stability when using the three CVD paradigms in non-cloud and cloud-based frameworks.