Artificial intelligence-based predictive model for relapse in acute myeloid leukemia patients following haploidentical hematopoietic cell transplantation.

Background And Objectives: Relapse is one of the most critical causes of transplant failure in patients with acute myeloid leukemia (AML) receiving haploidentical-related donor (HID) hematopoietic stem cell transplantation (HSCT). We aimed to develop an artificial intelligence (AI)-based predictive model for post-transplant relapse in patients with AML receiving HID HSCT.

Methods: This study included patients with consecutive AML (aged ≥ 12 years) receiving HID HSCT in complete remission (CR).

An Electrocardiogram Foundation Model Built on over 10 Million Recordings.

Background: Artificial intelligence (AI) has demonstrated significant potential in electrocardiogram (ECG) analysis and cardiovascular disease assessment. Recently, foundation models have played a remarkable role in advancing medical AI, bringing benefits such as efficient disease diagnosis and cross-domain knowledge transfer. The development of an ECG foundation model holds the promise of elevating AI-ECG research to new heights.

Sleep Temporal Entropy as a Novel Digital Biomarker of Sleep Fragmentation for Cardiometabolic and Mortality Risk.

This study introduces Sleep Temporal Entropy (STE), a novel entropy-based digital sleep biomarker derived from Shannon entropy theory, to quantify sleep fragmentation and explore its associations with cardiometabolic disorders and mortality. Unlike traditional metrics (e.g.

The Harvard-Emory ECG Database.

The Harvard-Emory ECG Database (HEEDB) is a large collection of 12-lead electrocardiogram (ECG) recordings, developed through a collaboration between Harvard University and Emory University. The database consists of 10,608,417 unique ECG recordings from 1,818,247 patients from Massachusetts General Hospital (MGH) and 1,452,964 recordings from 552,481 patients from Emory University Hospital (EUH) collected in clinical settings as part of routine patient care since the early 1990s. Continuously updated with new data, the recordings consist of 10-second, 12-lead ECGs sampled at 250 and 500 Hz, and stored in WFDB format.

Exploring artificial intelligence methods for cardiac syncope diagnosis combined with electrocardiogram parameters and clinical characteristics.

Background: Cardiac syncope can be life-threatening, but there is no clinical tool for initial screening. The study explored and developed optimal artificial intelligence methods for automatic diagnosis of cardiac syncope based on combinations of electrocardiogram parameters and clinical characteristics.

Methods: The patients presenting with syncope and hospitalized between June 21, 2018 and August 23, 2022 at the Second Hospital of Tianjin Medical University.

Continuous sleep depth index annotation with deep learning yields novel digital biomarkers for sleep health.

Traditional sleep staging categorizes sleep and wakefulness into five coarse-grained classes, overlooking subtle variations within each stage. We propose a deep learning method to annotate continuous sleep depth index (SDI) with existing discrete sleep staging labels, using polysomnography from over 10,000 recordings across four large-scale cohorts. The results showcased a strong correlation between the decrease in sleep depth index and the increase in duration of arousal.

Multi-modal artificial intelligence algorithm for the prediction of left atrial low-voltage areas in atrial fibrillation patient based on sinus rhythm electrocardiogram and clinical characteristics: a retrospective, multicentre study.

Aims: We aimed to develop an artificial intelligence (AI) algorithm capable of accurately predicting the presence of left atrial low-voltage areas (LVAs) based on sinus rhythm electrocardiograms (ECGs) in patients with atrial fibrillation (AF).

Methods And Results: The study included 1133 patients with AF who underwent catheter ablation procedures, with a total of 1787 12-lead ECG images analysed. Artificial intelligence-based algorithms were used to construct models for predicting the presence of LVAs.

Hospital-Treated Infections and 15-year Incidence of Musculoskeletal Disorders: A Large Population-Based Cohort Study.

Background: Basic science evidence reveals interactions between the immune and bone systems. However, population studies linking infectious diseases and musculoskeletal (MSK) disorders are limited and inconsistent. We aimed to examine the risk of six main MSK disorders (osteoarthritis, rheumatoid arthritis, osteoporosis, gout, low back pain, and neck pain) following hospital-treated infections in a large cohort with long follow-up periods.

Reliability and validity of a novel single-lead portable electrocardiogram device for pregnant women: a comparative study.

Background: WenXinWuYang, a novel portable Artificial Intelligence Electrocardiogram (AI-ECG) device, can detect many kinds of abnormal heart disease and perform a single-lead ECG, but its reliability and validity among pregnant women is unclear. The aim of this study was to assess the reliability and validity of heart rate, ECG measurements and diagnostic results by compared the portable device with a clinical 12-lead ECG among pregnant women.

Methods: We conducted a clinical study at a municipal-level maternal and child health care hospital.

Deep learning for detecting and early predicting chronic obstructive pulmonary disease from spirogram time series.

Chronic Obstructive Pulmonary Disease (COPD) is a chronic lung condition characterized by airflow obstruction. Current diagnostic methods primarily rely on identifying prominent features in spirometry (Volume-Flow time series) to detect COPD, but they are not adept at predicting future COPD risk based on subtle data patterns. In this study, we introduce a novel deep learning-based approach, DeepSpiro, aimed at the early prediction of future COPD risk.

The Mediating Effect of Concurrent Changes in Dietary Behaviors on the Associations Between Intervention and Changes in Adiposity Outcomes: Evidence from a Cluster-Randomized Controlled Trial.

Behavioral interventions have been shown to be effective in improving dietary behavior and reducing childhood obesity. There is limited evidence on how concurrent changes in dietary behavior from intervention studies affect childhood obesity. The present study aimed to evaluate the mediating effect of concurrent changes in dietary behaviors between the intervention and changes in adiposity indicators.

Moving Beyond Medical Statistics: A Systematic Review on Missing Data Handling in Electronic Health Records.

Missing data in electronic health records (EHRs) presents significant challenges in medical studies. Many methods have been proposed, but uncertainty exists regarding the current state of missing data addressing methods applied for EHR and which strategy performs better within specific contexts. All studies referencing EHR and missing data methods published from their inception until 2024 March 30 were searched via the MEDLINE, EMBASE, and Digital Bibliography and Library Project databases.

Development and external validation of an algorithm for self-identification of risk for microvascular complications in patients with type 1 diabetes.

Aims: Microvascular complications, such as diabetic retinopathy (DR), diabetic nephropathy (DN) and diabetic peripheral neuropathy (DPN), are common and serious outcomes of inadequately managed type 1 diabetes (T1D). Timely detection and intervention in these complications are crucial for improving patient outcomes. This study aimed to develop and externally validate machine learning (ML) models for self-identification of microvascular complication risks in T1D population.

Screening for severe coronary stenosis in patients with apparently normal electrocardiograms based on deep learning.

Background: Patients with severe coronary arterystenosis may present with apparently normal electrocardiograms (ECGs), making it difficult to detect adverse health conditions during routine screenings or physical examinations. Consequently, these patients might miss the optimal window for treatment.

Methods: We aimed to develop an effective model to distinguish severe coronary stenosis from no or mild coronary stenosis in patients with apparently normal ECGs.

Cross-modal similar clinical case retrieval using a modular model based on contrastive learning and k-nearest neighbor search.

Objective: Electronic health record systems have made it possible for clinicians to use previously encountered similar cases to support clinical decision-making. However, most studies for similar case retrieval were based on single-modal data. The existing studies on cross-modal clinical case retrieval were limited.

Screening Tool for Paroxysmal Atrial Fibrillation Based on a Deep-Learning Algorithm Using Printed 12-Lead Electrocardiographic Records during Sinus Rhythm.

Background: Recent advancements in artificial intelligence (AI) have significantly improved atrial fibrillation (AF) detection using electrocardiography (ECG) data obtained during sinus rhythm (SR). However, the utility of printed ECG (pECG) records for AF detection, particularly in developing countries, remains unexplored. This study aims to assess the efficacy of an AI-based screening tool for paroxysmal AF (PAF) using pECGs during SR.

Machine Learning in Cardio-Oncology: New Insights from an Emerging Discipline.

A growing body of evidence on a wide spectrum of adverse cardiac events following oncologic therapies has led to the emergence of cardio-oncology as an increasingly relevant interdisciplinary specialty. This also calls for better risk-stratification for patients undergoing cancer treatment. Machine learning (ML), a popular branch discipline of artificial intelligence that tackles complex big data problems by identifying interaction patterns among variables, has seen increasing usage in cardio-oncology studies for risk stratification.

An artificial intelligence-enabled electrocardiogram algorithm for the prediction of left atrial low-voltage areas in persistent atrial fibrillation.

Objectives: We aimed to construct an artificial intelligence-enabled electrocardiogram (ECG) algorithm that can accurately predict the presence of left atrial low-voltage areas (LVAs) in patients with persistent atrial fibrillation.

Methods: The study included 587 patients with persistent atrial fibrillation who underwent catheter ablation procedures between March 2012 and December 2023 and 942 scanned images of 12-lead ECGs obtained before the ablation procedures were performed. Artificial intelligence-based algorithms were used to construct models for predicting the presence of LVAs.

Data-driven inverse design of flexible pressure sensors.

Artificial skins or flexible pressure sensors that mimic human cutaneous mechanoreceptors transduce tactile stimuli to quantitative electrical signals. Conventional trial-and-error designs for such devices follow a forward structure-to-property routine, which is usually time-consuming and determines one possible solution in one run. Data-driven inverse design can precisely target desired functions while showing far higher productivity, however, it is still absent for flexible pressure sensors because of the difficulties in acquiring a large amount of data.

Relationship between HLA genetic variations, COVID-19 vaccine antibody response, and risk of breakthrough outcomes.

The rapid global distribution of COVID-19 vaccines, with over a billion doses administered, has been unprecedented. However, in comparison to most identified clinical determinants, the implications of individual genetic factors on antibody responses post-COVID-19 vaccination for breakthrough outcomes remain elusive. Here, we conducted a population-based study including 357,806 vaccinated participants with high-resolution HLA genotyping data, and a subset of 175,000 with antibody serology test results.

Application of artificial intelligence in the diagnosis and treatment of cardiac arrhythmia.

The rapid growth in computational power, sensor technology, and wearable devices has provided a solid foundation for all aspects of cardiac arrhythmia care. Artificial intelligence (AI) has been instrumental in bringing about significant changes in the prevention, risk assessment, diagnosis, and treatment of arrhythmia. This review examines the current state of AI in the diagnosis and treatment of atrial fibrillation, supraventricular arrhythmia, ventricular arrhythmia, hereditary channelopathies, and cardiac pacing.

Association of Regular Opioid Use With Incident Dementia and Neuroimaging Markers of Brain Health in Chronic Pain Patients: Analysis of UK Biobank.

Objectives: We aimed to investigate the association of regular opioid use, compared with non-opioid analgesics, with incident dementia and neuroimaging outcomes among chronic pain patients.

Design: The primary design is a prospective cohort study. To triangulate evidence, we also conducted a nested case-control study analyzing opioid prescriptions and a cross-sectional study analyzing neuroimaging outcomes.

Medication Usage Record-Based Predictive Modeling of Neurodevelopmental Abnormality in Infants under One Year: A Prospective Birth Cohort Study.

Early identification of children with neurodevelopmental abnormality is a major challenge, which is crucial for improving symptoms and preventing further decline in children with neurodevelopmental abnormality. This study focuses on developing a predictive model with maternal sociodemographic, behavioral, and medication-usage information during pregnancy to identify infants with abnormal neurodevelopment before the age of one. In addition, an interpretable machine-learning approach was utilized to assess the importance of the variables in the model.

simpleNomo: A Python Package of Making Nomograms for Visualizable Calculation of Logistic Regression Models.

Background: Logistic regression models are widely used in clinical prediction, but their application in resource-poor settings or areas without internet access can be challenging. Nomograms can serve as a useful visualization tool to speed up the calculation procedure, but existing nomogram generators often require the input of raw data, inhibiting the transformation of established logistic regression models that only provide coefficients. Developing a tool that can generate nomograms directly from logistic regression coefficients would greatly increase usability and facilitate the translation of research findings into patient care.