Cost Effectiveness of an Electrocardiographic Deep Learning Algorithm to Detect Asymptomatic Left Ventricular Dysfunction

Tseng, Andrew; Thao, Viengneesee; Borah, Bijan J.; Attia, Zachi I.; Inojosa, Jose Medina; Kapa, Suraj; Carter, Rickey E.; Friedman, Paul A.; López-Jiménez, Francisco; Yao, Xiaoxi; Noseworthy, Peter A.

doi:10.1016/j.mayocp.2020.11.032

Cited by 19 publications

(11 citation statements)

References 20 publications

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“…Furthermore, the development and implementation of AI-powered decision support and communication tools embedded into existing EHR systems come with a cost, and the cost-effectiveness of such strategies needs to be evaluated before broad adoption. 142…”

Section: Discussionmentioning

confidence: 99%

Cardiovascular Disease Screening in Women: Leveraging Artificial Intelligence and Digital Tools

Adedinsewo

Pollak

Phillips

et al. 2022

Circulation Research

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Cardiovascular disease remains the leading cause of death in women. Given accumulating evidence on sex- and gender-based differences in cardiovascular disease development and outcomes, the need for more effective approaches to screening for risk factors and phenotypes in women is ever urgent. Public health surveillance and health care delivery systems now continuously generate massive amounts of data that could be leveraged to enable both screening of cardiovascular risk and implementation of tailored preventive interventions across a woman’s life span. However, health care providers, clinical guidelines committees, and health policy experts are not yet sufficiently equipped to optimize the collection of data on women, use or interpret these data, or develop approaches to targeting interventions. Therefore, we provide a broad overview of the key opportunities for cardiovascular screening in women while highlighting the potential applications of artificial intelligence along with digital technologies and tools.

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Section: Discussionmentioning

confidence: 99%

Cardiovascular Disease Screening in Women: Leveraging Artificial Intelligence and Digital Tools

Adedinsewo

Pollak

Phillips

et al. 2022

Circulation Research

Self Cite

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“…A retrospective design was used in eleven studies [12][13][14][17][18][19][20][21][22][23][24], one was a case series [15] and three were prospective cohort studies [16,25,26]. Apart from those 15 studies that fulfilled eligibility criteria for development and testing of algorithms, we identified one randomized controlled trial [1] and one cost-effectiveness study [27]. No studies examined the effects of using AI-enabled screening in relation to treatment changes, patient outcomes or quality of life.…”

Section: Overviewmentioning

confidence: 99%

“…ECG screening for LVSD is potentially feasible but costeffectiveness and clinical implication are yet to be fully investigated [35]. So far, only two studies have investigated cost-effectiveness of screening for LVSD [25,27]. Under most clinical scenarios, screening was cost-effective with a cost of < $50,000 per QALY [27].…”

Section: Clinical Implications Of Aieecgmentioning

confidence: 99%

“…So far, only two studies have investigated cost-effectiveness of screening for LVSD [25,27]. Under most clinical scenarios, screening was cost-effective with a cost of < $50,000 per QALY [27]. It was estimated that numbers needed to screen to identify one case of LVSD corresponds to 90.7 AIeECGs' and 8.8 echocardiograms when screening the total population.…”

Section: Clinical Implications Of Aieecgmentioning

confidence: 99%

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Artificial intelligence enabled ECG screening for left ventricular systolic dysfunction: a systematic review

et al. 2022

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Screening for left ventricular systolic dysfunction (LVSD), defined as reduced left ventricular ejection fraction (LVEF), deserves renewed interest as the medical treatment for the prevention and progression of heart failure improves. We aimed to review the updated literature to outline the potential and caveats of using artificial intelligence-enabled electrocardiography (AIeECG) as an opportunistic screening tool for LVSD. We searched PubMed and Cochrane for variations of the terms "ECG," "Heart Failure," "systolic dysfunction," and "Artificial Intelligence" from January 2010 to April 2022 and selected studies that reported the diagnostic accuracy and confounders of using AIeECG to detect LVSD. Out of 40 articles, we identified 15 relevant studies; eleven retrospective cohorts, three prospective cohorts, and one case series. Although various LVEF thresholds were used, AIeECG detected LVSD with a median AUC of 0.90 (IQR from 0.85 to 0.95), a sensitivity of 83.3% (IQR from 73 to 86.9%) and a specificity of 87% (IQR from 84.5 to 90.9%). AIeECG algorithms succeeded across a wide range of sex, age, and comorbidity and seemed especially useful in non-cardiology settings and when combined with natriuretic peptide testing. Furthermore, a false-positive AIeECG indicated a future development of LVSD. No studies investigated the effect on treatment or patient outcomes. This systematic review corroborates the arrival of a new generic biomarker, AIeECG, to improve the detection of LVSD. AIeECG, in addition to natriuretic peptides and echocardiograms, will improve screening for LVSD, but prospective randomized implementation trials with added therapy are needed to show cost-effectiveness and clinical significance.

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“…Through in-depth explanation, students can distinguish different algorithms, pay more attention to the characteristics of the Viscom course after the introduction of DL, and focus on the data preprocessing, model design, and parameter optimization of the DL models. The innovative course explains in detail the application principle of DL algorithm, model construction, model training, parameter selection, algorithm optimization, etc., and helps students quickly master the principle and application of DL [ 1 , 4 , 29 , 30 ].…”

Section: DL Algorithm and Viscom Coursementioning

confidence: 99%

Exploring Multiple Application Scenarios of Visual Communication Course Using Deep Learning Under the Digital Twins

Liu

2022

Computational Intelligence and Neuroscience

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The emergence of intelligent technology has brought a particular impact and allows for virtuality-reality interaction in the educational field. In particular, digital twins (DTs) feature virtuality-reality symbiosis, solid virtual simulation, and high real-time interaction. It has also seen extended applications to the field of education. This study optimizes the design of the visual communication (Viscom) course based on the deep learning (DL) algorithm. Firstly, the theory of DL is analyzed following the relevant literature, and the typical DL networks, network structures, and related algorithms are introduced. Secondly, Viscom technology is expounded, and DL technology is applied to the Viscom course. Then, the applicability and feasibility of DL in the Viscom course are analyzed through a questionnaire survey (QS) design by collecting students’ attitudes towards Viscom courses before and after the experiment. After introducing DL into the Viscom course, the results show that students’ learning interest and satisfaction with the practical knowledge mastery have increased. However, the satisfaction with theoretical knowledge mastery before practical courses has decreased; overall, the teaching effect of the Viscom course has been improved. Therefore, the introduction of DL into the DT-enabled Viscom can provide a reference for developing the Viscom course. The research content offers technical support (TS) for integrating DT technology and modern education.

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Cost Effectiveness of an Electrocardiographic Deep Learning Algorithm to Detect Asymptomatic Left Ventricular Dysfunction

Cited by 19 publications

References 20 publications

Cardiovascular Disease Screening in Women: Leveraging Artificial Intelligence and Digital Tools

Cardiovascular Disease Screening in Women: Leveraging Artificial Intelligence and Digital Tools

Artificial intelligence enabled ECG screening for left ventricular systolic dysfunction: a systematic review

Exploring Multiple Application Scenarios of Visual Communication Course Using Deep Learning Under the Digital Twins

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