Identification of Atrial Fibrillation With Single-Lead Mobile ECG During Normal Sinus Rhythm Using Deep Learning.

BACKGROUND: The acquisition of single-lead electrocardiogram (ECG) from mobile devices offers a more practical approach to arrhythmia detection. Using artificial intelligence for atrial fibrillation (AF) identification enhances screening efficiency. However, the potential of single-lead ECG for AF identification during normal sinus rhythm (NSR) remains under-explored. This study introduces a method to identify AF using single-lead mobile ECG during NSR. METHODS: We employed three deep learning models: recurrent neural network (RNN), long short-term memory (LSTM), and residual neural networks (ResNet50). From a dataset comprising 13,509 ECGs from 6,719 patients, 10,287 NSR ECGs from 5,170 patients were selected. Single-lead mobile ECGs underwent noise filtering and segmentation into 10-second intervals. A random under-sampling was applied to reduce bias from data imbalance. The final analysis involved 31,767 ECG segments, including 15,157 labeled as masked AF and 16,610 as Healthy. RESULTS: ResNet50 outperformed the other models, achieving a recall of 79.3%, precision of 65.8%, F1-score of 71.9%, accuracy of 70.5%, and an area under the receiver operating characteristic curve (AUC) of 0.79 in identifying AF from NSR ECGs. Comparative performance scores for RNN and LSTM were 0.75 and 0.74, respectively. In an external validation set, ResNet50 attained an F1-score of 64.1%, recall of 68.9%, precision of 60.0%, accuracy of 63.4%, and AUC of 0.68. CONCLUSION: The deep learning model using single-lead mobile ECG during NSR effectively identified AF at risk in future. However, further research is needed to enhance the performance of deep learning models for clinical application.

Outcome Prediction Model Following Proximal Femoral Osteotomy in Legg-Calvé-Perthes Disease Using Machine Learning Algorithms.

BACKGROUND: The purpose of the current study was (1) to analyze various factors that may be associated with the outcomes of Legg-Calvé-Perthes disease (LCPD), and (2) to develop and internally validate machine learning algorithms capable of providing patient-specific predictions of which patients with LCPD will achieve relevant improvement in radiologic outcomes after proximal femoral varus osteotomy (PFVO). We examined several variables, previously identified as factors, that may influence the outcome of LCPD and developed a machine learning algorithm based on them. METHODS: In this retrospective study, we analyzed patients aged older than  6 years at the time of LCPD diagnosis who underwent PFVO at our institution between 1979 and 2015. Univariate and multivariate logistic regression analyses were used to examine the effects of variables on the sphericity of the femoral head at skeletal maturity, including age at onset, sex, stage at operation, extent of epiphyseal involvement and collapse, presence of specific epiphyseal, metaphyseal, and acetabular changes, and postoperative neck shaft angle (NSA). Recursive feature selection was used to identify the combination of variables from an initial pool of 13 features that optimized the model performance. Five machine learning algorithms [extreme gradient boosting (XGBoost), multilayer perception, support vector machine, elastic-net penalized logistic regression, and random forest) were trained using 5-fold cross-validation 3 times and applied to an independent testing set of patients. RESULTS: Ninety patients with LCPD who underwent PFVO were included in this study. The mean age at diagnosis was 7.93 (range, 6.0 to 12.33) years. The average follow-up period was 10.11 (range, 5.25 to 22.92) years. A combination of 8 variables, optimized algorithm performance, and specific cutoffs were found to decrease the likelihood of achieving the 1 or 2 Stulberg classification: age at onset ≤ 8.06, lateral classification ≤ B, 12.40 < preoperative migration percentage (MP) ≤ 22.85, Catterall classification ≤ 2, 117.4 < postoperative NSA ≤ 122.90, -10.8 < postoperative MP ≤ 6.5, 139.65 < preoperative NSA ≤ 144.67, and operation at stage 1. The XGBoost model demonstrated the best performance (F1 score: 0.78; area under the curve: 0.84). CONCLUSIONS: The XGBoost machine learning algorithm achieved the best performance in predicting the postoperative radiologic outcomes in patients with LCPD who underwent PFVO. In our population, age at onset ≤ 8.06, lateral classification ≤ B, 12.40 < preoperative MP ≤ 22.85, Catterall classification ≤ 2, 117.4 < postoperative NSA ≤ 122.90, -10.8 < postoperative MP ≤ 6.5, 139.65 < preoperative NSA ≤ 144.67, and operation at an early stage had the likelihood of achieving the spherical femoral head for the patients with LCPD that underwent PFVO. After external validation, the online application of this model may enhance shared decision-making. LEVEL OF EVIDENCE: Level III-retrospective cohort study.