Explainable-by-Design: Challenges, Pitfalls, and Opportunities for the Clinical Adoption of AI-Enabled ECG

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A clinician’s guide to understanding and critically appraising machine learning studies: a checklist for Ruling Out Bias Using Standard Tools in Machine Learning (ROBUST-ML)

Developing functional machine learning (ML)-based models to address unmet clinical needs requires unique considerations for optimal clinical utility. Recent debates about the rigours, transparency, explainability, and reproducibility of ML models, terms which are defined in this article, have raised concerns about their clinical utility and suitability for integration in current evidence-based practice paradigms. This featured article focuses on increasing the literacy of ML among clinicians by providing them with the knowledge and tools needed to understand and critically appraise clinical studies focused on ML. A checklist is provided for evaluating the rigour and reproducibility of the four ML building blocks: data curation, feature engineering, model development, and clinical deployment. Checklists like this are important for quality assurance and to ensure that ML studies are rigourously and confidently reviewed by clinicians and are guided by domain knowledge of the setting in which the findings will be applied. Bridging the gap between clinicians, healthcare scientists, and ML engineers can address many shortcomings and pitfalls of ML-based solutions and their potential deployment at the bedside

The role of machine learning applications in diagnosing and assessing critical and non-critical CHD: a scoping review

Machine learning uses historical data to make predictions about new data. It has been frequently applied in healthcare to optimise diagnostic classification through discovery of hidden patterns in data that may not be obvious to clinicians. Congenital Heart Defect (CHD) machine learning research entails one of the most promising clinical applications, in which timely and accurate diagnosis is essential. The objective of this scoping review is to summarise the application and clinical utility of machine learning techniques used in paediatric cardiology research, specifically focusing on approaches aiming to optimise diagnosis and assessment of underlying CHD. Out of 50 full-text articles identified between 2015 and 2021, 40% focused on optimising the diagnosis and assessment of CHD. Deep learning and support vector machine were the most commonly used algorithms, accounting for an overall diagnostic accuracy > 0.80. Clinical applications primarily focused on the classification of auscultatory heart sounds, transthoracic echocardiograms, and cardiac MRIs. The range of these applications and directions of future research are discussed in this scoping review

Depression and heart rate variability in firefighters

Introduction: Depression has been found to increase the risk of mortality in patients with coronary artery disease through a mechanism of changing cardiac autonomic tone which is reflected by alteration of heart rate variability indices. This study investigated whether such mechanism existed in firefighters who were at high risk of depression and sudden cardiac death. Methods and results: In total, 107 firefighters were recruited. All completed Beck Depression Inventory and underwent 24-h ambulatory electrocardiographic monitoring. The root-mean-square of successive differences, standard deviation of all normal-to-normal intervals index, and the percentage of differences between adjacent normal-to-normal intervals >50 ms were significantly lower in depressed than in non-depressed firefighters after controlling for hypertension, age, and body mass index (40.1 ± 18.8 vs 62.5 ± 77.4, p  < 0.01; 63.0 ± 19.2 vs 72.1 ± 34.8, p  < 0.01; 8.4 ± 7.2 vs 12.7 ± 10.9, p  < 0.01, respectively). Conclusion: Decreased vagal tone is a possible mechanism linking depression and sudden cardiac death in firefighters