scholarly journals B-AB24-04 ARTIFICIAL INTELLIGENCE ENABLES DRAMATIC REDUCTION OF FALSE ATRIAL FIBRILLATION ALERTS FROM INSERTABLE CARDIAC MONITORS

Heart Rhythm ◽  
2021 ◽  
Vol 18 (8) ◽  
pp. S47
Author(s):  
Andrew P. Radtke ◽  
Kevin T. Ousdigian ◽  
Tarek D. Haddad ◽  
Jodi L. Koehler ◽  
Ilyas K. Colombowala
2020 ◽  
Vol 28 ◽  
Author(s):  
Valeria Visco ◽  
Germano Junior Ferruzzi ◽  
Federico Nicastro ◽  
Nicola Virtuoso ◽  
Albino Carrizzo ◽  
...  

Background: In the real world, medical practice is changing hand in hand with the development of new Artificial Intelligence (AI) systems and problems from different areas have been successfully solved using AI algorithms. Specifically, the use of AI techniques in setting up or building precision medicine is significant in terms of the accuracy of disease discovery and tailored treatment. Moreover, with the use of technology, clinical personnel can deliver a very much efficient healthcare service. Objective: This article reviews AI state-of-the-art in cardiovascular disease management, focusing on diagnostic and therapeutic improvements. Methods: To that end, we conducted a detailed PubMed search on AI application from distinct areas of cardiology: heart failure, arterial hypertension, atrial fibrillation, syncope and cardiovascular rehabilitation. Particularly, to assess the impact of these technologies in clinical decision-making, this research considers technical and medical aspects. Results: On one hand, some devices in heart failure, atrial fibrillation and cardiac rehabilitation represent an inexpensive, not invasive or not very invasive approach to long-term surveillance and management in these areas. On the other hand, the availability of large datasets (big data) is a useful tool to predict the development and outcome of many cardiovascular diseases. In summary, with this new guided therapy, the physician can supply prompt, individualised, and tailored treatment and the patients feel safe as they are continuously monitored, with a significant psychological effect. Conclusion: Soon, tailored patient care via telemonitoring can improve the clinical practice because AI-based systems support cardiologists in daily medical activities, improving disease detection and treatment. However, the physician-patient relationship remains a pivotal step.


Heart Rhythm ◽  
2021 ◽  
Vol 18 (8) ◽  
pp. S350-S351
Author(s):  
Chih-Min Liu ◽  
Yenn-Jiang Lin ◽  
Men-Tzung Lo ◽  
Chia Hsin Chiang ◽  
Shih-Lin Chang ◽  
...  

2020 ◽  
Vol 26 (10) ◽  
pp. S76
Author(s):  
Frederik Hendrik Verbrugge ◽  
Yogesh N.V. Reddy ◽  
Zachi I. Attia ◽  
Paul A. Friedman ◽  
Peter A. Noseworthy ◽  
...  

Heart Rhythm ◽  
2020 ◽  
Vol 17 (5) ◽  
pp. 847-853 ◽  
Author(s):  
Erdong Chen ◽  
Jie Jiang ◽  
Rui Su ◽  
Meng Gao ◽  
Sainan Zhu ◽  
...  

The Lancet ◽  
2019 ◽  
Vol 394 (10201) ◽  
pp. 861-867 ◽  
Author(s):  
Zachi I Attia ◽  
Peter A Noseworthy ◽  
Francisco Lopez-Jimenez ◽  
Samuel J Asirvatham ◽  
Abhishek J Deshmukh ◽  
...  

2019 ◽  
Vol 6 (5) ◽  
pp. 301-309 ◽  
Author(s):  
Shinichi Goto ◽  
Shinya Goto ◽  
Karen S Pieper ◽  
Jean-Pierre Bassand ◽  
Alan John Camm ◽  
...  

Abstract Aims Most clinical risk stratification models are based on measurement at a single time-point rather than serial measurements. Artificial intelligence (AI) is able to predict one-dimensional outcomes from multi-dimensional datasets. Using data from Global Anticoagulant Registry in the Field (GARFIELD)-AF registry, a new AI model was developed for predicting clinical outcomes in atrial fibrillation (AF) patients up to 1 year based on sequential measures of prothrombin time international normalized ratio (PT-INR) within 30 days of enrolment. Methods and results Patients with newly diagnosed AF who were treated with vitamin K antagonists (VKAs) and had at least three measurements of PT-INR taken over the first 30 days after prescription were analysed. The AI model was constructed with multilayer neural network including long short-term memory and one-dimensional convolution layers. The neural network was trained using PT-INR measurements within days 0–30 after starting treatment and clinical outcomes over days 31–365 in a derivation cohort (cohorts 1–3; n = 3185). Accuracy of the AI model at predicting major bleed, stroke/systemic embolism (SE), and death was assessed in a validation cohort (cohorts 4–5; n = 1523). The model’s c-statistic for predicting major bleed, stroke/SE, and all-cause death was 0.75, 0.70, and 0.61, respectively. Conclusions Using serial PT-INR values collected within 1 month after starting VKA, the new AI model performed better than time in therapeutic range at predicting clinical outcomes occurring up to 12 months thereafter. Serial PT-INR values contain important information that can be analysed by computer to help predict adverse clinical outcomes.


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