Dataset Information

Identification of Brugada syndrome based on P-wave features: an artificial intelligence-based approach.

ABSTRACT:

Aims

Brugada syndrome (BrS) is an inherited disease associated with an increased risk of ventricular arrhythmias. Recent studies have reported the presence of an altered atrial phenotype characterized by abnormal P-wave parameters. The aim of this study was to identify BrS based exclusively on P-wave features through an artificial intelligence (AI)-based model.

Methods and results

Continuous 5 min 12-lead ECG recordings were obtained in sinus rhythm from (i) patients with spontaneous or ajmaline-induced BrS and no history of AF and (ii) subjects with suspected BrS and negative ajmaline challenge. The recorded ECG signals were processed and divided into epochs of 15 s each. Within these epochs, P-waves were first identified and then averaged. From the averaged P-waves, a total of 67 different features considered relevant to the classification task were extracted. These features were then used to train nine different AI-based supervised classifiers. A total of 2228 averaged P-wave observations, resulting from the analysis of 33 420 P-waves, were obtained from 123 patients (79 BrS+ and 44 BrS-). Averaged P-waves were divided using a patient-wise split, allocating 80% for training and 20% for testing, ensuring data integrity and reducing biases in AI-based model training. The BrS+ patients presented with longer P-wave duration (136 ms vs. 124 ms, P < 0.001) and higher terminal force in lead V1 (2.5 au vs. 1.7 au, P < 0.01) compared with BrS- subjects. Among classifiers, AdaBoost model had the highest values of performance for all the considered metrics, reaching an accuracy of over 81% (sensitivity 86%, specificity 73%).

Conclusion

An AI machine-learning model is able to identify patients with BrS based only on P-wave characteristics. These findings confirm the presence of an atrial hallmark and open new horizons for AI-guided BrS diagnosis.

SUBMITTER: Zanchi B

PROVIDER: S-EPMC10683037 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of Brugada syndrome based on P-wave features: an artificial intelligence-based approach.

Zanchi Beatrice B Faraci Francesca Dalia FD Gharaviri Ali A Bergonti Marco M Monga Tomas T Auricchio Angelo A Conte Giulio G

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology 20231101 11

<h4>Aims</h4>Brugada syndrome (BrS) is an inherited disease associated with an increased risk of ventricular arrhythmias. Recent studies have reported the presence of an altered atrial phenotype characterized by abnormal P-wave parameters. The aim of this study was to identify BrS based exclusively on P-wave features through an artificial intelligence (AI)-based model.<h4>Methods and results</h4>Continuous 5 min 12-lead ECG recordings were obtained in sinus rhythm from (i) patients with spontane ...[more]

PMID: 37944131

Similar Datasets

Project description:Endometriosis is a debilitating, chronic disease that is estimated to affect 11% of reproductive-age women. Diagnosis of endometriosis is difficult with diagnostic delays of up to 12 years reported. These delays can negatively impact health and quality of life. Vague, nonspecific symptoms, like pain, with multiple differential diagnoses contribute to the difficulty of diagnosis. By investigating previously imprecise symptoms of pain, we sought to clarify distinct pain symptoms indicative of endometriosis, using an artificial intelligence-based approach. We used data from 473 women undergoing laparoscopy or laparotomy for a variety of surgical indications. Multiple anatomical pain locations were clustered based on the associations across samples to increase the power in the probability calculations. A Bayesian network was developed using pain-related features, subfertility, and diagnoses. Univariable and multivariable analyses were performed by querying the network for the relative risk of a postoperative diagnosis, given the presence of different symptoms. Performance and sensitivity analyses demonstrated the advantages of Bayesian network analysis over traditional statistical techniques. Clustering grouped the 155 anatomical sites of pain into 15 pain locations. After pruning, the final Bayesian network included 18 nodes. The presence of any pain-related feature increased the relative risk of endometriosis (p-value < 0.001). The constellation of chronic pelvic pain, subfertility, and dyspareunia resulted in the greatest increase in the relative risk of endometriosis. The performance and sensitivity analyses demonstrated that the Bayesian network could identify and analyze more significant associations with endometriosis than traditional statistical techniques. Pelvic pain, frequently associated with endometriosis, is a common and vague symptom. Our Bayesian network for the study of pain-related features of endometriosis revealed specific pain locations and pain types that potentially forecast the diagnosis of endometriosis.

Project description:BackgroundThe diagnosis of sarcopenia relies extensively on human and equipment resources and requires individuals to personally visit medical institutions. The objective of this study was to develop a test-free, self-assessable approach to identify sarcopenia by utilizing artificial intelligence techniques and representative real-world data.MethodsThis multicentre study enrolled 11 661 middle-aged and older adults from a national survey initialized in 2011. Follow-up data from the baseline cohort collected in 2013 (n = 9403) and 2015 (n = 10 356) were used for validation. Sarcopenia was retrospectively diagnosed using the Asian Working Group for Sarcopenia 2019 framework. Baseline age, sex, height, weight and 20 functional capacity (FC)-related binary indices (activities of daily living = 6, instrumental activities of daily living = 5 and other FC indices = 9) were considered as predictors. Multiple machine learning (ML) models were trained and cross-validated using 70% of the baseline data to predict sarcopenia. The remaining 30% of the baseline data, along with two follow-up datasets (n = 9403 and n = 10 356, respectively), were used to assess model performance.ResultsThe study included 5634 men and 6027 women (median age = 57.0 years). Sarcopenia was identified in 1288 (11.0%) individuals. Among the 20 FC indices, the running/jogging 1 km item showed the highest predictive value for sarcopenia (AUC [95%CI] = 0.633 [0.620-0.647]). From the various ML models assessed, a 24-variable gradient boosting classifier (GBC) model was selected. This GBC model demonstrated favourable performance in predicting sarcopenia in the holdout data (AUC [95%CI] = 0.831 [0.808-0.853], accuracy = 0.889, recall = 0.441, precision = 0.475, F1 score = 0.458, Kappa = 0.396 and Matthews correlation coefficient = 0.396). Further model validation on the temporal scale using two longitudinal datasets also demonstrated good performance (AUC [95%CI]: 0.833 [0.818-0.848] and 0.852 [0.840-0.865], respectively). The model's built-in feature importance ranking and the SHapley Additive exPlanations method revealed that lifting 5 kg and running/jogging 1 km were relatively important variables among the 20 FC items contributing to the model's predictive capacity, respectively. The calibration curve of the model indicated good agreement between predictions and actual observations (Hosmer and Lemeshow p = 0.501, 0.451 and 0.374 for the three test sets, respectively), and decision curve analysis supported its clinical usefulness. The model was implemented as an online web application and exported as a deployable binary file, allowing for flexible, individualized risk assessment.ConclusionsWe developed an artificial intelligence model that can assist in the identification of sarcopenia, particularly in settings lacking the necessary resources for a comprehensive diagnosis. These findings offer potential for improving decision-making and facilitating the development of novel management strategies of sarcopenia.

Project description:Purpose:Next-generation sequencing technologies are actively applied in clinical oncology. Bioinformatics pipeline analysis is an integral part of this process; however, humans cannot yet realize the full potential of the highly complex pipeline output. As a result, the decision to include a variant in the final report during routine clinical sign-out remains challenging. Methods:We used an artificial intelligence approach to capture the collective clinical sign-out experience of six board-certified molecular pathologists to build and validate a decision support tool for variant reporting. We extracted all reviewed and reported variants from our clinical database and tested several machine learning models. We used 10-fold cross-validation for our variant call prediction model, which derives a contiguous prediction score from 0 to 1 (no to yes) for clinical reporting. Results:For each of the 19,594 initial training variants, our pipeline generates approximately 500 features, which results in a matrix of > 9 million data points. From a comparison of naive Bayes, decision trees, random forests, and logistic regression models, we selected models that allow human interpretability of the prediction score. The logistic regression model demonstrated 1% false negativity and 2% false positivity. The final models' Youden indices were 0.87 and 0.77 for screening and confirmatory cutoffs, respectively. Retraining on a new assay and performance assessment in 16,123 independent variants validated our approach (Youden index, 0.93). We also derived individual pathologist-centric models (virtual consensus conference function), and a visual drill-down functionality allows assessment of how underlying features contributed to a particular score or decision branch for clinical implementation. Conclusion:Our decision support tool for variant reporting is a practically relevant artificial intelligence approach to harness the next-generation sequencing bioinformatics pipeline output when the complexity of data interpretation exceeds human capabilities.

Dataset Information

Identification of Brugada syndrome based on P-wave features: an artificial intelligence-based approach.

Aims

Methods and results

Conclusion

Publications

Identification of Brugada syndrome based on P-wave features: an artificial intelligence-based approach.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets