Detection of Patients with Congenital and Often Concealed Long-QT Syndrome by Novel Deep Learning Models

Artificial intelligence-driven intelligent learning models for identification and prediction of cardioneurological disorders: A comprehensive study

The integration of Artificial Intelligence (AI) and Intelligent Learning Models (ILMs) in healthcare has transformed the field, offering precise diagnostics, remote monitoring, personalized treatment, and more. Cardioneurological disorders (CD), affecting the cardiovascular and neurological systems, present significant diagnostic and management challenges. Traditional testing methods often lack sensitivity and specificity, leading to delayed or inaccurate diagnoses. AI-driven ILMs trained on large datasets offer promise for accurate identification and prediction of CD by analyzing complex data patterns. However, there is a lack of comprehensive studies reviewing AI applications for the diagnosis of CD and inter related disorders. This paper comprehensively reviews existing integrated solutions involving AI and ILMs in CD, examining their clinical manifestations, epidemiology, diagnostic challenges, and therapeutic considerations. The study examines recent research on CD, reviews AI-driven models' landscape, evaluates existing models, addresses practical considerations, and outlines future research directions. Through this work, we aim to provide insights into the transformative potential of AI-driven ILMs in improving clinical practice and patient outcomes for CD.

Multimodal fusion learning for long QT syndrome pathogenic genotypes in a racially diverse population

Congenital long QT syndrome (LQTS) diagnosis is complicated by limited genetic testing at scale, low prevalence, and normal QT corrected interval in patients with high-risk genotypes. We developed a deep learning approach combining electrocardiogram (ECG) waveform and electronic health record data to assess whether patients had pathogenic variants causing LQTS. We defined patients with high-risk genotypes as having ≥1 pathogenic variant in one of the LQTS-susceptibility genes. We trained the model using data from United Kingdom Biobank (UKBB) and then fine-tuned in a racially/ethnically diverse cohort using Mount Sinai BioMe Biobank. Following group-stratified 5-fold splitting, the fine-tuned model achieved area under the precision-recall curve of 0.29 (95% confidence interval [CI] 0.28-0.29) and area under the receiver operating curve of 0.83 (0.82-0.83) on independent testing data from BioMe. Multimodal fusion learning has promise to identify individuals with pathogenic genetic mutations to enable patient prioritization for further work up.

From genes to clinical management: A comprehensive review of long QT syndrome pathogenesis and treatment

Background

Long QT syndrome (LQTS) is a rare cardiac disorder characterized by prolonged ventricular repolarization and increased risk of ventricular arrhythmias. This review summarizes current knowledge of LQTS pathogenesis and treatment strategies.

Objectives

The purpose of this study was to provide an in-depth understanding of LQTS genetic and molecular mechanisms, discuss clinical presentation and diagnosis, evaluate treatment options, and highlight future research directions.

Methods

A systematic search of PubMed, Embase, and Cochrane Library databases was conducted to identify relevant studies published up to April 2024.

Results

LQTS involves mutations in ion channel-related genes encoding cardiac ion channels, regulatory proteins, and other associated factors, leading to altered cellular electrophysiology. Acquired causes can also contribute. Diagnosis relies on clinical history, electrocardiographic findings, and genetic testing. Treatment strategies include lifestyle modifications, β-blockers, potassium channel openers, device therapy, and surgical interventions.

Conclusion

Advances in understanding LQTS have improved diagnosis and personalized treatment approaches. Challenges remain in risk stratification and management of certain patient subgroups. Future research should focus on developing novel pharmacological agents, refining device technologies, and conducting large-scale clinical trials. Increased awareness and education are crucial for early detection and appropriate management of LQTS.

Deep Learning-Augmented ECG Analysis for Screening and Genotype Prediction of Congenital Long QT Syndrome

Importance

Congenital long QT syndrome (LQTS) is associated with syncope, ventricular arrhythmias, and sudden death. Half of patients with LQTS have a normal or borderline-normal QT interval despite LQTS often being detected by QT prolongation on resting electrocardiography (ECG).

Objective

To develop a deep learning-based neural network for identification of LQTS and differentiation of genotypes (LQTS1 and LQTS2) using 12-lead ECG.

Design, Setting, and Participants

This diagnostic accuracy study used ECGs from patients with suspected inherited arrhythmia enrolled in the Hearts in Rhythm Organization Registry (HiRO) from August 2012 to December 2021. The internal dataset was derived at 2 sites and an external validation dataset at 4 sites within the HiRO Registry; an additional cross-sectional validation dataset was from the Montreal Heart Institute. The cohort with LQTS included probands and relatives with pathogenic or likely pathogenic variants in KCNQ1 or KCNH2 genes with normal or prolonged corrected QT (QTc) intervals.

Exposures

Convolutional neural network (CNN) discrimination between LQTS1, LQTS2, and negative genetic test results.

Main Outcomes and Measures

The main outcomes were area under the curve (AUC), F1 scores, and sensitivity for detecting LQTS and differentiating genotypes using a CNN method compared with QTc-based detection.

Results

A total of 4521 ECGs from 990 patients (mean [SD] age, 42 [18] years; 589 [59.5%] female) were analyzed. External validation within the national registry (101 patients) demonstrated the CNN's high diagnostic capacity for LQTS detection (AUC, 0.93; 95% CI, 0.89-0.96) and genotype differentiation (AUC, 0.91; 95% CI, 0.86-0.96). This surpassed expert-measured QTc intervals in detecting LQTS (F1 score, 0.84 [95% CI, 0.78-0.90] vs 0.22 [95% CI, 0.13-0.31]; sensitivity, 0.90 [95% CI, 0.86-0.94] vs 0.36 [95% CI, 0.23-0.47]), including in patients with normal or borderline QTc intervals (F1 score, 0.70 [95% CI, 0.40-1.00]; sensitivity, 0.78 [95% CI, 0.53-0.95]). In further validation in a cross-sectional cohort (406 patients) of high-risk patients and genotype-negative controls, the CNN detected LQTS with an AUC of 0.81 (95% CI, 0.80-0.85), which was better than QTc interval-based detection (AUC, 0.74; 95% CI, 0.69-0.78).

Conclusions and Relevance

The deep learning model improved detection of congenital LQTS from resting ECGs and allowed for differentiation between the 2 most common genetic subtypes. Broader validation over an unselected general population may support application of this model to patients with suspected LQTS.

ECG-based data-driven solutions for diagnosis and prognosis of cardiovascular diseases: A systematic review

Cardiovascular diseases (CVD) are a leading cause of death globally, and result in significant morbidity and reduced quality of life. The electrocardiogram (ECG) plays a crucial role in CVD diagnosis, prognosis, and prevention; however, different challenges still remain, such as an increasing unmet demand for skilled cardiologists capable of accurately interpreting ECG. This leads to higher workload and potential diagnostic inaccuracies. Data-driven approaches, such as machine learning (ML) and deep learning (DL) have emerged to improve existing computer-assisted solutions and enhance physicians' ECG interpretation of the complex mechanisms underlying CVD. However, many ML and DL models used to detect ECG-based CVD suffer from a lack of explainability, bias, as well as ethical, legal, and societal implications (ELSI). Despite the critical importance of these Trustworthy Artificial Intelligence (AI) aspects, there is a lack of comprehensive literature reviews that examine the current trends in ECG-based solutions for CVD diagnosis or prognosis that use ML and DL models and address the Trustworthy AI requirements. This review aims to bridge this knowledge gap by providing a systematic review to undertake a holistic analysis across multiple dimensions of these data-driven models such as type of CVD addressed, dataset characteristics, data input modalities, ML and DL algorithms (with a focus on DL), and aspects of Trustworthy AI like explainability, bias and ethical considerations. Additionally, within the analyzed dimensions, various challenges are identified. To these, we provide concrete recommendations, equipping other researchers with valuable insights to understand the current state of the field comprehensively.

More than 30 years of Brugada syndrome: a critical appraisal of achievements and open issues

Over the last three decades, what is referred to as Brugada syndrome (BrS) has developed from a clinical observation of initially a few cases of sudden cardiac death (SCD) in the absence of structural heart disease with ECG signs of "atypical right bundle brunch block" to a predominantly electrocardiographic, and to a lesser extent genetic, diagnosis. Today, BrS is diagnosed in patients without overt structural heart disease and a spontaneous Brugada type 1 ECG pattern regardless of symptoms. The diagnosis of BrS is less clear in those with an only transient or drug-induced type 1 Brugada pattern, but should be considered in the presence of an arrhythmic syncope, family history of BrS, or family history of sudden death. In addition to survived cardiac arrest, syncope is probably the single most decisive risk marker for future arrhythmias. For asymptomatic BrS, risk stratification remains challenging. General recommendations to lower the risk in BrS include avoidance of drugs/agents known to induce and/or increase right precordial ST-segment elevation, including treatment of fever with antipyretic drugs. Several ECG markers that have been associated with an increased risk of SCD have been incorporated into a recently published risk score for BrS. The aim of this article is to provide an overview of the status of risk stratification and to illustrate open issues und gaps in evidence in BrS.

The diagnostic value of electrocardiogram-based machine learning in long QT syndrome: a systematic review and meta-analysis

Introduction

To perform a meta-analysis to discover the performance of ML algorithms in identifying Congenital long QT syndrome (LQTS).

Methods

The searched databases included Cochrane, EMBASE, Web of Science, and PubMed. Our study considered all English-language studies that reported the detection of LQTS using ML algorithms. Quality was assessed using QUADAS-2 and QUADAS-AI tools. The bivariate mixed effects models were used in our study. Based on genotype data for LQTS, we performed a subgroup analysis.

Results

Out of 536 studies, 8 met all inclusion criteria. The pooled area under the receiving operating curve (SAUROC) for detecting LQTS was 0.95 (95% CI: 0.31-1.00); sensitivity was 0.87 (95% CI: 0.83-0.90), and specificity was 0.91 (95% CI: 0.88-0.93). Additionally, diagnostic odd ratio (DOR) was 65 (95% CI: 39-109). The positive likelihood ratio (PLR) was 9.3 (95% CI: 7.0-12.3) and the negative likelihood ratio (NLR) was 0.14 (95% CI: 0.11-0.20), with very low heterogeneity (I²= 16%).

Discussion

We found that machine learning can be used to detect features of rare cardiovascular disease like LQTS, thus increasing our understanding of intelligent interpretation of ECG. To improve ML performance in the classification of LQTS subtypes, further research is required.

Systematic Review Registration

identifier PROSPERO CRD42022360122.