Cardiovascular diseases are a leading cause of mortality globally. Electrocardiography (ECG) still represents the benchmark approach for identifying cardiac irregularities. Automatic detection of abnormalities from the ECG can aid in the early detection, diagnosis, and prevention of cardiovascular diseases. Deep Learning (DL) architectures have been successfully employed for arrhythmia detection and classification and offered superior performance to traditional shallow Machine Learning (ML) approaches. This survey categorizes and compares the DL architectures used in ECG arrhythmia detection from 2017–2023 that have exhibited superior performance. Different DL models such as Convolutional Neural Networks (CNNs), Multilayer Perceptrons (MLPs), Transformers, and Recurrent Neural Networks (RNNs) are reviewed, and a summary of their effectiveness is provided. This survey provides a comprehensive roadmap to expedite the acclimation process for emerging researchers willing to develop efficient algorithms for detecting ECG anomalies using DL models. Our tailored guidelines bridge the knowledge gap allowing newcomers to align smoothly with the prevailing research trends in ECG arrhythmia detection. We shed light on potential areas for future research and refinement in model development and optimization, intending to stimulate advancement in ECG arrhythmia detection and classification.

Deep learning for ECG Arrhythmia detection and classification: an overview of progress for period 2017–2023

Abstract Single locus (Mendelian) diseases are a leading cause of childhood hospitalization, intensive care unit (ICU) admission, mortality, and healthcare cost. Rapid genome sequencing (RGS), ultra-rapid genome sequencing (URGS), and rapid exome sequencing (RES) are diagnostic tests for genetic diseases for ICU patients. In 44 studies of children in ICUs with diseases of unknown etiology, 37% received a genetic diagnosis, 26% had consequent changes in management, and net healthcare costs were reduced by $14,265 per child tested by URGS, RGS, or RES. URGS outperformed RGS and RES with faster time to diagnosis, and higher rate of diagnosis and clinical utility. Diagnostic and clinical outcomes will improve as methods evolve, costs decrease, and testing is implemented within precision medicine delivery systems attuned to ICU needs. URGS, RGS, and RES are currently performed in &lt;5% of the ~200,000 children likely to benefit annually due to lack of payor coverage, inadequate reimbursement, hospital policies, hospitalist unfamiliarity, under-recognition of possible genetic diseases, and current formatting as tests rather than as a rapid precision medicine delivery system. The gap between actual and optimal outcomes in children in ICUs is currently increasing since expanded use of URGS, RGS, and RES lags growth in those likely to benefit through new therapies. There is sufficient evidence to conclude that URGS, RGS, or RES should be considered in all children with diseases of uncertain etiology at ICU admission. Minimally, diagnostic URGS, RGS, or RES should be ordered early during admissions of critically ill infants and children with suspected genetic diseases. 

Rapid genomic sequencing for genetic disease diagnosis and therapy in intensive care units: a review

Recently, significant efforts have been made to create Health Digital Twins (HDTs), Digital Twins for clinical applications. Heart modeling is one of the fastest-growing fields, which favors the effective application of HDTs. The clinical application of HDTs will be increasingly widespread in the future of healthcare services and has huge potential to form part of mainstream medicine. However, it requires the development of both models and algorithms for the analysis of medical data, and advances in Artificial Intelligence (AI)-based algorithms have already revolutionized image segmentation processes. Precise segmentation of lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapy. In this systematic review, a brief overview of recent achievements in HDT technologies in the field of cardiology, including interventional cardiology, was conducted. HDTs were studied taking into account the application of Extended Reality (XR) and AI, as well as data security, technical risks, and ethics-related issues. Special emphasis was put on automatic segmentation issues. In this study, 253 literature sources were taken into account. It appears that improvements in data processing will focus on automatic segmentation of medical imaging in addition to three-dimensional (3D) pictures to reconstruct the anatomy of the heart and torso that can be displayed in XR-based devices. This will contribute to the development of effective heart diagnostics. The combination of AI, XR, and an HDT-based solution will help to avoid technical errors and serve as a universal methodology in the development of personalized cardiology. Additionally, we describe potential applications, limitations, and further research directions.

Cardiac Healthcare Digital Twins Supported by Artificial Intelligence-Based Algorithms and Extended Reality—A Systematic Review

Amyloid transthyretin (ATTR) amyloidosis is a protein-misfolding disease characterized by fibril accumulation in the extracellular space that can result in local tissue disruption and organ dysfunction. Cardiac involvement drives morbidity and mortality, and the heart is the major organ affected by ATTR amyloidosis. Multimodality cardiac imaging (ie, echocardiography, scintigraphy, and cardiac magnetic resonance) allows accurate diagnosis of ATTR cardiomyopathy (ATTR-CM), and this is of particular importance because ATTR-targeting therapies have become available and probably exert their greatest benefit at earlier disease stages. Apart from establishing the diagnosis, multimodality cardiac imaging may help to better understand pathogenesis, predict prognosis, and monitor treatment response. The aim of this review is to give an update on contemporary and evolving cardiac imaging methods and their role in diagnosing and managing ATTR-CM. Further, an outlook is presented on how artificial intelligence in cardiac imaging may improve future clinical decision making and patient management in the setting of ATTR-CM. 

Current and Evolving Multimodality Cardiac Imaging in Managing Transthyretin Amyloid Cardiomyopathy.

Coronary artery disease (CAD) is one of the major causes of fatalities across the globe. The recent developments in convolutional neural networks (CNN) allow researchers to detect CAD from computed tomography (CT) images. The CAD detection model assists physicians in identifying cardiac disease at earlier stages. The recent CAD detection models demand a high computational cost and a more significant number of images. Therefore, this study intends to develop a CNN-based CAD detection model. The researchers apply an image enhancement technique to improve the CT image quality. The authors employed You look only once (YOLO) V7 for extracting the features. Aquila optimization is used for optimizing the hyperparameters of the UNet++ model to predict CAD. The proposed feature extraction technique and hyperparameter tuning approach reduces the computational costs and improves the performance of the UNet++ model. Two datasets are utilized for evaluating the performance of the proposed CAD detection model. The experimental outcomes suggest that the proposed method achieves an accuracy, recall, precision, F1-score, Matthews correlation coefficient, and Kappa of 99.4, 98.5, 98.65, 98.6, 95.35, and 95 and 99.5, 98.95, 98.95, 98.95, 96.35, and 96.25 for datasets 1 and 2, respectively. In addition, the proposed model outperforms the recent techniques by obtaining the area under the receiver operating characteristic and precision-recall curve of 0.97 and 0.95, and 0.96 and 0.94 for datasets 1 and 2, respectively. Moreover, the proposed model obtained a better confidence interval and standard deviation of [98.64–98.72] and 0.0014, and [97.41–97.49] and 0.0019 for datasets 1 and 2, respectively. The study’s findings suggest that the proposed model can support physicians in identifying CAD with limited resources.

/pdf/developing-a-deep-learning-based-coronary-artery-disease-35xki76d.pdf

Developing a Deep-Learning-Based Coronary Artery Disease Detection Technique Using Computer Tomography Images

https://doi.org/10.1016/j.jacasi.2022.12.005

Advances in Diagnosis, Therapy, and Prognosis of Coronary Artery Disease Powered by Deep Learning Algorithms

ABSTRACT Background: The demand for health care in the United States is increasing because of an aging population and an increase in the number of individuals insured. This has led to requests to revamp the primary care infrastructure fundamentally. Local Problem: The optimal use of nurse practitioners (NPs) and physician assistants (PAs) is still a subject of debate, but recently, it was reported that for many medical conditions, NP and PA-managed care outcomes are consistent with physician-managed care outcomes. Methods: Radiologists' productivity was measured according to relative value units (RVUs)/shift and professional billing changes. Patient care metrics measured were prescribed protocol to patient appointment lead time and number of same-day prescribed imaging protocol changes. Interventions: The focus was on radiologists' productivity and patient care for three months before and three months after integrating NP and PA into our abdominal radiology consult service. Results: We observed significant increases in the mean RVUs/shift (15.2 ± 0.9 vs. 6.2 ± 1.8; p = .02), studies read per shift (10.1 ± 0.5 vs. 4.4 ± 1.5; p = .003), revenue per shift hour ($756.20 ± 55.40 vs. $335.40 ± 32.60; p = .007), and protocol prescription to patient appointment lead time (39.3 ± 6.7 days vs. 16.3 ± 2.9 days; p = .005) and saw significant decreases in the mean prescribed CT (19.3 ± 0.6 vs. 3.3 ± 0.6; p = .001) and MRI (11.7 ± 0.6 vs. 8.30 ± 0.12; p = .011) same day protocol changes in NP and PA integrated workflow. Conclusions: These findings suggest that NP and PA can be effectively integrated into the abdominal radiology consult service, increasing radiologists' productivity and enhancing clinical care.

Engaging nurse practitioners and physician assistants to improve patient care and drive productivity in a radiology consult practice at a comprehensive cancer center

ABSTRACT Objective: To evaluate the effects on efficiency and patient care of the addition of physician assistants (PAs) and NPs to the abdominal radiology consult service. Methods: We obtained radiologist productivity and patient care metrics for 3 months before and 3 months after the integration of PAs and NPs into our consult service. Results: Integrating PAs and NPs into the workflow led to a significant increase in mean RVUs/shift (15.2 ± 0.9 versus 6.2 ± 1.8; P = .02), number of studies read per shift (10.1 ± 0.5 versus 4.4 ± 1.5; P = .003), revenue per shift hour ($756.20 ± $55.40 versus $335.40 ± $132.60; P = .007), protocol prescription to patient appointment lead time (39.3 ± 6.7 versus 16.3 ± 2.9 days; P = .005), and significant decreases in mean CT (19.3% ± 0.6 versus 3.3% ± 0.6; P = .001) and MRI (11.7% ± 0.6 versus 8.3% ± 0.12; P = .011) same-day protocol changes as patient appointments. Conclusions: PAs and NPs can be effectively integrated into abdominal radiology consult service, increasing the productivity of radiologists, and enhancing clinical care.

PAs and NPs improve patient care and productivity in a radiology consult practice

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Advances in Diagnosis, Therapy, and Prognosis of Coronary Artery Disease Powered by Deep Learning Algorithms

Engaging nurse practitioners and physician assistants to improve patient care and drive productivity in a radiology consult practice at a comprehensive cancer center

PAs and NPs improve patient care and productivity in a radiology consult practice