The Use of Artificial Intelligence Guidance for Rheumatic Heart Disease Screening by Novices

Peck, Daniel; Rwebembera, Joselyn; Nakagaayi, Doreen; Minja, Neema W.; Ollberding, Nicholas J.; Pulle, Jafesi; Klein, Jennifer; Adams, David H.; Martin, Randolph P.; Koepsell, Kilian; Sanyahumbi, Amy; Beaton, Andrea; Okello, Emmy; Sable, Craig

doi:10.1016/j.echo.2023.03.001

Cited by 14 publications

(8 citation statements)

References 30 publications

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“…35 One strategy uses echocardiographic artificial intelligence to further latent RHD detection. 36 Designed in the United States, software integrated into hand‐held devices (Caption Health) aids users in image acquisition and automated diagnosis. This approach aided novice providers in Uganda to successfully obtain screening echocardiograms for RHD after minimal training (efferent reciprocal innovation).…”

Section: Exemplar Digital Technology Projects Adaptable To Lower‐reso...mentioning

confidence: 99%

“…This approach aided novice providers in Uganda to successfully obtain screening echocardiograms for RHD after minimal training (efferent reciprocal innovation). 36 With anticipated success from this approach, there is a high potential to scale use of echocardiographic artificial intelligence to more rural settings in Uganda and to adapt this technology for diagnoses and management of other CVD and chronic conditions. This addresses barriers of transportation to specialist facilities, inability to see a specialist due to shortages of health care professionals, and so on.…”

Section: Exemplar Digital Technology Projects Adaptable To Lower‐reso...mentioning

confidence: 99%

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Advancing Cardiovascular Health Equity Globally Through Digital Technologies

Ogungbe,

Longenecker,

Beaton

et al. 2024

JAHA

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Cardiovascular diseases (CVDs) remain the leading cause of death and disability worldwide. Digital health technologies are important public health interventions for addressing the burden of cardiovascular disease. In this article, we discuss the importance of translating digital innovations in research‐funded projects to low‐resource settings globally to advance global cardiovascular health equity. We also discuss current global cardiovascular health inequities and the digital health divide within and between countries. We present various considerations for translating digital innovations across different settings across the globe, including reciprocal innovation , a “bidirectional, co‐constituted, and iterative exchange of ideas, resources, and innovations to address shared health challenges across diverse global settings.” In this case, afferent reciprocal innovations may flow from high‐income countries toward low‐ and middle‐income countries, and efferent reciprocal innovations may be exported to high‐income countries from low‐ and middle‐income countries with adaptation. Finally, we discuss opportunities for bidirectional learning between local and global institutions and highlight examples of projects funded through the American Heart Association Health and Innovation Strategically Funded Research Network that have been adapted to lower‐resource settings or have the potential to be adapted to lower‐resource settings.

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Section: Exemplar Digital Technology Projects Adaptable To Lower‐reso...mentioning

confidence: 99%

Section: Exemplar Digital Technology Projects Adaptable To Lower‐reso...mentioning

confidence: 99%

Advancing Cardiovascular Health Equity Globally Through Digital Technologies

Ogungbe,

Longenecker,

Beaton

et al. 2024

JAHA

Self Cite

View full text Add to dashboard Cite

show abstract

“…It can also help standardize image acquisition and measurements. 1,6 In this way, it would be a tool used to optimize training and echocardiographic studies, 5,25,26 with the possibility of acquiring images of diagnostic quality comparable to those acquired manually by experts. 5,19,20,[22][23][24] The technology is applicable in clinical practice, in emergencies, and in remote areas with scarce resources and few experienced professionals.…”

Section: Image Acquisitionmentioning

confidence: 99%

“…5,19,20,[22][23][24] The technology is applicable in clinical practice, in emergencies, and in remote areas with scarce resources and few experienced professionals. 5,21,25,27 Integration with clinical data…”

Section: Image Acquisitionmentioning

confidence: 99%

Use of Artificial Intelligence to Assess Cardiac Function by Echocardiography: Systematic Review of the State of the Art

Neves,

Yuasa,

Costa

et al. 2023

ABC Imagem Cardiovascular

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Background: Echocardiography is part of the routine investigation of most heart diseases. However, its operatordependent nature and the quality of the image obtained can cause significant variability in measurements and impair their interpretation. The method has advanced on its path of standardization and incorporation of new technologies in an attempt to reduce its deficiencies. Recently, part of this advancement was due to the incorporation of artificial intelligence (AI). Objectives: The objective of this work was to investigate the use of AI in the assessment of cardiac function through echocardiography, discussing its potential to improve the method. Methods: We conducted a systematic review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), searching for articles within the period of 2000 to 2023. Results: A total of 31 articles were included, classified into four categories of studies that used AI: 1) for image analysis and echocardiographic parameters (N = 14); 2) in the image acquisition process (N = 11); 3) for integration with clinical data (N = 3); and 4) for application to the exam flow (N = 3).Conclusion: AI provides automation and standardization of images and respective measurements that result in more accurate diagnoses, overcoming the limitations encountered by the conventional method. However, the use of AI is still an evolving reality within echocardiography.

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“…Given this, we sought to develop a real‐world implementation model for improving access to echocardiography within the Indian Health Service (IHS). Utilizing strategies proven effective within low‐ and middle‐income countries, 7 , 8 , 9 the American Indian Structural Heart Disease Partnership (IN‐STEP) aimed to integrate point‐of‐care (POC) echocardiography into primary care clinics to improve access to echocardiography, facilitate CVD diagnosis and management, and ultimately improve outcomes.…”

mentioning

confidence: 99%

Deployment of Point‐of‐Care Echocardiography to Improve Cardiac Diagnostic Access Among American Indians

de Loizaga,

Benashley,

Hoekzema

et al. 2024

JAHA

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Background American Indians face significant barriers to diagnosis and management of cardiovascular disease. We sought to develop a real‐world implementation model for improving access to echocardiography within the Indian Health Service, the American Indian Structural Heart Disease Partnership. Methods and Results The American Indian Structural Heart Disease Partnership was implemented and evaluated via a 4‐step process of characterizing the system where it would be instituted, building point‐of‐care echocardiography capacity, deploying active case finding for structural heart disease, and evaluating the approach from the perspective of the clinician and patient. Data were collected and analyzed using a parallel convergent mixed methods approach. Twelve health care providers successfully completed training in point‐of‐care echocardiography. While there was perceived usefulness of echocardiography, providers found it difficult to integrate screening point‐of‐care echocardiography into their workday given competing demands. By the end of 12 months, 6 providers continued to actively utilize point‐of‐care echocardiography. Patients who participated in the study felt it was an acceptable and effective approach. They also identified access to transportation as a notable challenge to accessing echocardiograms. Over the 12‐month period, a total of 639 patients were screened, of which 36 (5.6%) had a new clinically significant abnormal finding. Conclusions The American Indian Structural Heart Disease Partnership model exhibited several promising strategies to improve access to screening echocardiography for American Indian populations. However, competing priorities for Indian Health Service providers' time limited the amount of integration of screening echocardiography into outpatient practice. Future endeavors should explore community‐based solutions to develop a more sustainable model with greater impact on case detection, disease management, and improved outcomes.

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The Use of Artificial Intelligence Guidance for Rheumatic Heart Disease Screening by Novices

Cited by 14 publications

References 30 publications

Advancing Cardiovascular Health Equity Globally Through Digital Technologies

Advancing Cardiovascular Health Equity Globally Through Digital Technologies

Use of Artificial Intelligence to Assess Cardiac Function by Echocardiography: Systematic Review of the State of the Art

Deployment of Point‐of‐Care Echocardiography to Improve Cardiac Diagnostic Access Among American Indians

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