A novel practical algorithm using machine learning to differentiate outflow tract ventricular arrhythmia origins

Shimojo, Makoto; Inden, Yasuya; Suzuki, Noriyuki; Tsurumi, Naoki; Watanabe, Ryo; Nakagomi, Takahiro; Okajima, Takashi; Suga, Kazumasa; Tsuji, Yukiomi; Murohara, Toyoaki

doi:10.1111/jce.15823

Cited by 7 publications

(8 citation statements)

References 24 publications

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“…Another study differentiates the left ventricular outflow tract (LVOT) from the right ventricular outflow tract (RVOT) of OTVA origin with V3TZ using ML. 68 Furthermore, VA is the cause of sudden death in both acquired and congenital cardiac diseases. If the occurrence of VA in susceptible patients can be predicted and intervened earlier, the rate of sudden death will be reduced.…”

Section: Supraventricular Arrhythmiamentioning

confidence: 99%

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

Guo,

Tian,

Bazoukis

et al. 2024

Pacing Clinical Electrophis

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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. Furthermore, ChatGPT, which has gained attention recently, is addressed in this paper along with its potential applications in the field of arrhythmia. Additionally, the accuracy of arrhythmia diagnosis can be improved by identifying electrode misplacement or erroneous swapping of electrode position using AI. Remote monitoring has expanded greatly due to the emergence of contactless monitoring technology as wearable devices continue to develop and flourish. Parallel advances in AI computing power, ChatGPT, availability of large data sets, and more have greatly expanded applications in arrhythmia diagnosis, risk assessment, and treatment. More precise algorithms based on big data, personalized risk assessment, telemedicine and mobile health, smart hardware and wearables, and the exploration of rare or complex types of arrhythmia are the future direction.

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Section: Supraventricular Arrhythmiamentioning

confidence: 99%

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

Guo,

Tian,

Bazoukis

et al. 2024

Pacing Clinical Electrophis

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“…selected a decision tree algorithm (with a practically chosen limit of three decision branches), which performed favorably in accuracy, as well as providing full transparency of the decision-making process. 8,10 Selected from 128 input variables, the four-most important input-features used by this algorithm were: aVF/II R-wave ratio, V2S/V3R index, QRS-amplitude in V3, and the R-Wave deflection slope in lead V3. Using these metrics, the performance of this algorithm was extremely high, with an accuracy of 94.4%, precision of 91.5%, recall of 100%, and an F1-score of 0.96.…”

Section: Patientsmentioning

confidence: 99%

“…In this issue, Shimojo et al apply a ML approach to the classifying left/right origin in outflow tract ventricular arrythmias (OTVA) 10 . Ablation of outflow tract ventricular arrhythmias (OTVA) is a common procedure and preprocedural identification of left‐ or right‐side origin is an important in procedural planning and patient consent.…”

mentioning

confidence: 99%

“…Simple pattern recognition of the standard 12‐lead electrocardiogram (ECG) measurements has been the traditional tool used to help differentiate right ventricular outflow tract (RVOT) from left ventricular outflow tract (LVOT) arrhythmias; derivation of signatures from these data have been an area of active study with the goal of providing accurate origin detection 11 . The new study by Shimojo et al applied supervised ML methods, aiming to classify Left‐ versus Right‐ventricular outflow tract origin, using specific 12‐lead electrocardiogram metrics 10 . An additional motivation was with respect to diagnosing the left/right origin of OTVA cases with a precordial transition in lead V3 (V3TZ), which are considered more challenging.…”

mentioning

confidence: 99%

“…Upon comparing several classifier algorithms, Shimojo et al selected a decision tree algorithm (with a practically chosen limit of three decision branches), which performed favorably in accuracy, as well as providing full transparency of the decision‐making process 8,10 . Selected from 128 input variables, the four‐most important input‐features used by this algorithm were: aVF/II R‐wave ratio, V2S/V3R index, QRS‐amplitude in V3, and the R‐Wave deflection slope in lead V3.…”

mentioning

confidence: 99%

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Machine learning in EP research: New tools for old problems

Figgett

Hawson

Lee

2023

Cardiovasc electrophysiol

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Enhancing origin prediction: deep learning model for diagnosing premature ventricular contractions with dual-rhythm analysis focused on cardiac rotation

Nakasone,

Nishimori,

Shinohara

et al. 2024

Europace

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Background Several algorithms can differentiate inferior axis premature ventricular contractions (PVCs) originating from the right side and left side on 12-lead electrocardiograms (ECGs). However, it is unclear whether distinguishing the origin should rely solely on PVC or incorporate sinus rhythm (SR). Aims We compared the Dual-Rhythm model (incorporating both SR and PVC) to the PVC model (using PVC alone), and quantified the contribution of each ECG lead in predicting the PVC origin for each cardiac rotation. Methods This multicenter study enrolled 593 patients from 11 centers—493 from Japan and Germany, and 100 from Belgium, which used as the external validation dataset. Using a hybrid approach combining a Resnet50-based convolutional neural network and a Transformer model, we developed two variants—the PVC and Dual-Rhythm models—to predict PVC origin. Results In the external validation dataset, the Dual-Rhythm model outperformed the PVC model in accuracy (0.84 vs. 0.74, respectively; p < 0.01), precision (0.73 vs. 0.55, respectively; p < 0.01), specificity (0.87 vs. 0.68, respectively; p < 0.01), area under the receiver operating characteristic curve (0.91 vs. 0.86, respectively; p = 0.03), and F1-Score (0.77 vs. 0.68, respectively; p = 0.03). The contributions to PVC origin prediction were 77.3% for PVC and 22.7% for the SR. However, in patients with counterclockwise rotation, SR had a greater contribution in predicting the origin of right-sided PVC. Conclusions Our deep learning-based model, incorporating both PVC and SR morphologies, resulted in a higher prediction accuracy for PVC origin. Considering SR is particularly important for predicting right-sided origin in patients with counterclockwise rotation.

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A novel practical algorithm using machine learning to differentiate outflow tract ventricular arrhythmia origins

Cited by 7 publications

References 24 publications

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

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

Machine learning in EP research: New tools for old problems

Enhancing origin prediction: deep learning model for diagnosing premature ventricular contractions with dual-rhythm analysis focused on cardiac rotation

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