Artificial intelligence-enhanced 12-lead electrocardiography for identifying atrial fibrillation during sinus rhythm (AIAFib) trial: protocol for a multicenter retrospective study.

Introduction: Atrial fibrillation (AF) is the most common arrhythmia, contributing significantly to morbidity and mortality. In a previous study, we developed a deep neural network for predicting paroxysmal atrial fibrillation (PAF) during sinus rhythm (SR) using digital data from standard 12-lead electrocardiography (ECG). The primary aim of this study is to validate an existing artificial intelligence (AI)-enhanced ECG algorithm for predicting PAF in a multicenter tertiary hospital. The secondary objective is to investigate whether the AI-enhanced ECG is associated with AF-related clinical outcomes. Methods and analysis: We will conduct a retrospective cohort study of more than 50,000 12-lead ECGs from November 1, 2012, to December 31, 2021, at 10 Korean University Hospitals. Data will be collected from patient records, including baseline demographics, comorbidities, laboratory findings, echocardiographic findings, hospitalizations, and related procedural outcomes, such as AF ablation and mortality. De-identification of ECG data through data encryption and anonymization will be conducted and the data will be analyzed using the AI algorithm previously developed for AF prediction. An area under the receiver operating characteristic curve will be created to test and validate the datasets and assess the AI-enabled ECGs acquired during the sinus rhythm to determine whether AF is present. Kaplan-Meier survival functions will be used to estimate the time to hospitalization, AF-related procedure outcomes, and mortality, with log-rank tests to compare patients with low and high risk of AF by AI. Multivariate Cox proportional hazards regression will estimate the effect of AI-enhanced ECG multimorbidity on clinical outcomes after stratifying patients by AF probability by AI. Discussion: This study will advance PAF prediction based on AI-enhanced ECGs. This approach is a novel method for risk stratification and emphasizes shared decision-making for early detection and management of patients with newly diagnosed AF. The results may revolutionize PAF management and unveil the wider potential of AI in predicting and managing cardiovascular diseases. Ethics and dissemination: The study findings will be published in peer-reviewed publications and disseminated at national and international conferences and through social media. This study was approved by the institutional review boards of all participating university hospitals. Data extraction, storage, and management were approved by the data review committees of all institutions. Clinical Trial Registration: [cris.nih.go.kr], identifier (KCT0007881).

Artificial intelligence-enhanced electrocardiography for early assessment of coronavirus disease 2019 severity.

Despite challenges in severity scoring systems, artificial intelligence-enhanced electrocardiography (AI-ECG) could assist in early coronavirus disease 2019 (COVID-19) severity prediction. Between March 2020 and June 2022, we enrolled 1453 COVID-19 patients (mean age: 59.7 ± 20.1 years; 54.2% male) who underwent ECGs at our emergency department before severity classification. The AI-ECG algorithm was evaluated for severity assessment during admission, compared to the Early Warning Scores (EWSs) using the area under the curve (AUC) of the receiver operating characteristic curve, precision, recall, and F1 score. During the internal and external validation, the AI algorithm demonstrated reasonable outcomes in predicting COVID-19 severity with AUCs of 0.735 (95% CI: 0.662-0.807) and 0.734 (95% CI: 0.688-0.781). Combined with EWSs, it showed reliable performance with an AUC of 0.833 (95% CI: 0.830-0.835), precision of 0.764 (95% CI: 0.757-0.771), recall of 0.747 (95% CI: 0.741-0.753), and F1 score of 0.747 (95% CI: 0.741-0.753). In Cox proportional hazards models, the AI-ECG revealed a significantly higher hazard ratio (HR, 2.019; 95% CI: 1.156-3.525, p = 0.014) for mortality, even after adjusting for relevant parameters. Therefore, application of AI-ECG has the potential to assist in early COVID-19 severity prediction, leading to improved patient management.

Artificial intelligence-estimated biological heart age using a 12-lead electrocardiogram predicts mortality and cardiovascular outcomes.

Background: There is a paucity of data on artificial intelligence-estimated biological electrocardiography (ECG) heart age (AI ECG-heart age) for predicting cardiovascular outcomes, distinct from the chronological age (CA). We developed a deep learning-based algorithm to estimate the AI ECG-heart age using standard 12-lead ECGs and evaluated whether it predicted mortality and cardiovascular outcomes. Methods: We trained and validated a deep neural network using the raw ECG digital data from 425,051 12-lead ECGs acquired between January 2006 and December 2021. The network performed a holdout test using a separate set of 97,058 ECGs. The deep neural network was trained to estimate the AI ECG-heart age [mean absolute error, 5.8 ± 3.9 years; R-squared, 0.7 (r = 0.84, p < 0.05)]. Findings: In the Cox proportional hazards models, after adjusting for relevant comorbidity factors, the patients with an AI ECG-heart age of 6 years older than the CA had higher all-cause mortality (hazard ratio (HR) 1.60 [1.42-1.79]) and more major adverse cardiovascular events (MACEs) [HR: 1.91 (1.66-2.21)], whereas those under 6 years had an inverse relationship (HR: 0.82 [0.75-0.91] for all-cause mortality; HR: 0.78 [0.68-0.89] for MACEs). Additionally, the analysis of ECG features showed notable alterations in the PR interval, QRS duration, QT interval and corrected QT Interval (QTc) as the AI ECG-heart age increased. Conclusion: Biological heart age estimated by AI had a significant impact on mortality and MACEs, suggesting that the AI ECG-heart age facilitates primary prevention and health care for cardiovascular outcomes.

A new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm.

Atrial fibrillation (AF) is the most prevalent arrhythmia and is associated with increased morbidity and mortality. Its early detection is challenging because of the low detection yield of conventional methods. We aimed to develop a deep learning-based algorithm to identify AF during normal sinus rhythm (NSR) using 12-lead electrocardiogram (ECG) findings. We developed a new deep neural network to detect subtle differences in paroxysmal AF (PAF) during NSR using digital data from standard 12-lead ECGs. Raw digital data of 2,412 12-lead ECGs were analyzed. The artificial intelligence (AI) model showed that the optimal interval to detect subtle changes in PAF was within 0.24 s before the QRS complex in the 12-lead ECG. We allocated the enrolled ECGs to the training, internal validation, and testing datasets in a 7:1:2 ratio. Regarding AF identification, the AI-based algorithm showed the following values in the internal and external validation datasets: area under the receiver operating characteristic curve, 0.79 and 0.75; recall, 82% and 77%; specificity, 78% and 72%; F1 score, 75% and 74%; and overall accuracy, 72.8% and 71.2%, respectively. The deep learning-based algorithm using 12-lead ECG demonstrated high accuracy for detecting AF during NSR.

Pressure-Pair-Based Floor Localization System Using Barometric Sensors on Smartphones.

As smartphone technology advances and its market penetration increases, indoor positioning for smartphone users is becoming an increasingly important issue. Floor localization is especially critical to indoor positioning techniques. Numerous research efforts have been proposed for improving the floor localization accuracy using information from barometers, accelerometers, Bluetooth Low Energy (BLE), and Wi-Fi signals. Despite these existing efforts, no approach has been able to determine what floor smartphone users are on with near 100% accuracy. To address this problem, we present a novel pressure-pair based method called FloorPair, which offers near 100% accurate floor localization. The rationale of FloorPair is to construct a relative pressure map using highly accurate relative pressure values from smartphones with two novel features: first, we marginalized the uncertainty from sensor drifts and unreliable absolute pressure values of barometers by paring the pressure values of two floors, and second, we maintained high accuracy over time by applying an iterative optimization method, making our method sustainable. We evaluated the validity of the FloorPair approach by conducting extensive field experiments in various types of buildings to show that FloorPair is an accurate and sustainable floor localization method.

Simulating Porous Magnetite Layer Deposited on Alloy 690TT Steam Generator Tubes.

In nuclear power plants, the main corrosion product that is deposited on the outside of steam generator tubes is porous magnetite. The objective of this study was to simulate porous magnetite that is deposited on thermally treated (TT) Alloy 690 steam generator tubes. A magnetite layer was electrodeposited on an Alloy 690TT substrate in an Fe(III)-triethanolamine solution. After electrodeposition, the dense magnetite layer was immersed to simulate porous magnetite deposits in alkaline solution for 50 days at room temperature. The dense morphology of the magnetite layer was changed to a porous structure by reductive dissolution reaction. The simulated porous magnetite layer was compared with flakes of steam generator tubes, which were collected from the secondary water system of a real nuclear power plant during sludge lancing. Possible nuclear research applications using simulated porous magnetite specimens are also proposed.

New clinical score for disease activity at diagnosis in Langerhans cell histiocytosis.

BACKGROUND: The clinical presentation and course of Langerhans cell histiocytosis (LCH) are variable, ranging from an isolated, spontaneously remitting bone lesion to multisystem disease with risk organ involvement. Treatment of LCH ranges from a wait-and-see attitude to intensive multidrug therapy and, in some cases, bone marrow transplantation. It is necessary to develop an objective score for assessing disease activity in patients with LCH. We propose a new clinical scoring system to evaluate disease activity at diagnosis that can predict the clinical outcomes of LCH and correlate it with clinical courses. METHODS: Clinical data, obtained from children diagnosed with LCH at Asan Medical Center and Hanyang University Hospital between March 1998 and February 2009, were studied retrospectively. The scoring system was developed according to the basic biological data, radiological findings, and physical findings and applied to a database containing information on 133 patients. RESULTS: The median age of the 133 patients (74 male, 59 female) was 52 months (range, 0.6-178 months), and LCH was diagnosed based on CD1a positivity. At diagnosis, the score distributions were highly asymmetrical: the score was between 1 and 2 in 75.9% of cases, 3-6 in 15.8%, and greater than 6 in 8.3%. Initial scores above 6 were highly predictive of reactivation and late complications. CONCLUSION: This new LCH disease activity score provides an objective tool for assessing disease severity, both at diagnosis and during follow-up.

Less keratinocyte-derived factors related to more keratinocyte apoptosis in depigmented than normally pigmented suction-blistered epidermis may cause passive melanocyte death in vitiligo.

Stem cell factor (SCF) of keratinocyte origin regulates melanocyte growth and survival. Deprivation of survival factors causes the apoptosis of melanocytes. Vitiligo often develops following physical trauma, even if this is minor. The exact mechanism of the Koebner phenomenon in vitiligo is unclear. Apoptosis of keratinocytes, which occurs more in depigmented suction-blistered epidermis than in the normally pigmented counterpart, could reduce levels of keratinocyte-derived factors such as SCF and basic fibroblast growth factor (bFGF). Levels of SCF expression were examined in the depigmented and normally pigmented paired epidermis of 19 patients with vitiligo, and bFGF expression in six patients. The expression of SCF (p<0.001) and bFGF was usually reduced in the depigmented compared with the normally pigmented epidermis. Apoptosis of cultured normal human keratinocytes, which was induced by staurosporine, resulted in a concentration-dependent decrease in levels of SCF mRNA and protein. Normal human melanocytes proliferated more in medium containing SCF or keratinocyte (XB-2) feeder than in medium with neither. Deprivation of SCF or keratinocyte feeder in the culture medium induced a marked decrease in melanocytes as a result of apoptosis. Therefore, lower expression of keratinocyte-derived factors, including SCF, in vitiliginous keratinocytes, which could result from keratinocyte apoptosis, might be responsible for passive melanocyte death and may explain the Koebner phenomenon.