Odin

Suprem, Abhijit; Arulraj, Joy; Pu, Calton; Ferreira, João Eduardo

doi:10.14778/3407790.3407837

Cited by 17 publications

(1 citation statement)

References 36 publications

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“…To address the data drift problem, a large body of literature [4], [37], [67], [70] has explored continuous learning, which aims to continuously adapt models to newly appearing data. This approach allows the models to stay up-to-date with the changing data distribution.…”

Section: B Continuously Learning For Video Analytics At Edgementioning

confidence: 99%

A deep learning model to predict recurrence of atrial fibrillation after pulmonary vein isolation

Kim

Oh³

et al. 2020

Int J Arrhythm

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Background and Objectives The efficacy of radiofrequency catheter ablation (RFCA) in atrial fibrillation (AF) is well established. The standard approach to RFCA in AF is pulmonary vein isolation (PVI). However, a large proportion of patients experiences recurrence of atrial tachyarrhythmia. The purpose of this study is to find out whether the AI model can assess AF recurrence in patients who underwent PVI. Materials and methods This study was a retrospective cohort study that enrolled consecutive patients who underwent catheter ablation for symptomatic, drug-refractory AF and PVI. We developed an AI algorithm to predict recurrence of AF after PVI using patient demographics and three-dimensional (3D) reconstructed left atrium (LA) images. Results We included 527 consecutive patients in the study. The overall mean LA diameter was 42.0 ± 6.8 mm, and the mean LA volume calculated using 3D reconstructed images was 151.1 ± 46.7 ml. During the follow-up period, atrial tachyarrhythmia recurred in 158 patients. The area under the curve (AUC) of the AI model based on a convolutional neural network (including 3D reconstruction images) was 0.61 (95% confidence interval [CI] 0.53–0.74) using the test dataset. The total test accuracy was 66.3% (57.0–75.6), and the sensitivity was 53.3% (34.8–71.9). The specificity was 73.2% (51.8–75.0), and the F1 score was 52.5% 34.5–66.7). Conclusion In this study, we developed an AI algorithm to predict recurrence of AF after catheter ablation of PVI using individual reconstructed LA images. This AI model was unable to predict recurrence of AF overwhelmingly; therefore, further large-scale study is needed.

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Section: B Continuously Learning For Video Analytics At Edgementioning

confidence: 99%

A deep learning model to predict recurrence of atrial fibrillation after pulmonary vein isolation

Kim

Oh³

et al. 2020

Int J Arrhythm

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Neural Network Based Drift Detection

Fellicious¹,

Wendlinger²

2023

Lecture Notes in Computer Science

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In real-world applications, input data distributions are rarely static over a period of time, a phenomenon known as concept drift. Such concept drifts degrade the model's prediction performance, and therefore we require methods to overcome these issues. The initial step is to identify concept drifts and have a training method in place to recover the model's performance. Most concept drift detection methods work on detecting concept drifts and signalling the requirement to retrain the model. However, in real-world cases, there could be concept drifts that recur over a period of time. In this paper, we present an unsupervised method based on Generative Adversarial Networks(GAN) to detect concept drifts and identify whether a specific concept drift occurred in the past. Our method reduces the time and data the model requires to get up to speed for recurring drifts. Our key results indicate that our proposed model can outperform the current state-of-the-art models in most datasets. We also test our method on a real-world use case from astrophysics, where we detect the bow shock and magnetopause crossings with better results than the existing methods in the domain.

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