Automated Spectral Doppler Profile Tracing

Gosling, Andre F.; Thalappillil, Richard; Ortoleva, Jamel; Datta, Pranav; Cobey, Frederick C.

doi:10.1053/j.jvca.2019.06.018

Cited by 10 publications

(2 citation statements)

References 28 publications

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“…Hence, even though technical difficulties are described in the literature, we obtained reliable CO and SV measurements after a short training program. As already known in the literature, automatic tools correlate closely with manual measurements for LVOT-VTI measurements [ 24 ]; the vantage we could have was that the automatic method could allow realizing measurements within a much shorter time than the standard manual tracing method [ 25 ]. We think we could obtain the same result by calculating the LVOT-D measurement manually, as we did, and also tracing the LVOT-VTI measurement manually.…”

Section: Discussionmentioning

confidence: 99%

Intra-and inter-observer variability of point of care ultrasound measurements to evaluate hemodynamic parameters in healthy volunteers

et al. 2023

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Background Point-of-care ultrasound (POCUS) is a valuable tool for assessing the hemodynamic status of acute patients. Even though POCUS often uses a qualitative approach, quantitative measurements have potential advantages in evaluating hemodynamic status. Several quantitative ultrasound parameters can be used to assess the hemodynamic status and cardiac function. However, only limited data on the feasibility and reliability of the quantitative hemodynamic measurements in the point-of-care setting are available. This study investigated the intra- and inter-observer variability of PoCUS measurements of quantitative hemodynamic parameters in healthy volunteers. Methods In this prospective observational study, three sonographers performed three repeated measurements of eight different hemodynamic parameters in healthy subjects. An expert panel of two experienced sonographers evaluated the images’ quality. The repeatability (intra-observer variability) was determined by calculating the coefficient of variation (CV) between the separate measurements for each observer. The reproducibility (inter-observer variability) was assessed by determining the intra-class correlation coefficient (ICC). Results 32 subjects were included in this study, on whom, in total, 1502 images were obtained for analysis. All parameters were in a normal physiological range. Stroke volume (SV), cardiac output (CO), and inferior vena cava diameter (IVC-D) showed high repeatability (CV under 10%) and substantial reproducibility (ICC 0.61–0.80). The other parameters had only moderate repeatability and reproducibility. Conclusions We demonstrated good inter-observer reproducibility and good intra-observer repeatability for CO, SV and IVC-D taken in healthy subjects by emergency care physicians.

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Section: Discussionmentioning

confidence: 99%

Intra-and inter-observer variability of point of care ultrasound measurements to evaluate hemodynamic parameters in healthy volunteers

et al. 2023

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“…Similarly, little has been published on AI automation of CW and PW measurements. From one publication, compared with a board-certified echocardiographer, AI automation of CW and PW measurement of peak velocity, mean gradients and velocity time integral showed excellent correlation, with all correlation coefficients greater than 0.9 30. Commercial software has been developed to perform semiautomated 3D proximal isovelocity surface area (PISA) measurements with good accuracy and reproducibility, as multiple measurements can be made, although it is unclear if AI is used in modelling 31…”

Section: Ai To Improve Echocardiographic Image Valve Assessmentmentioning

confidence: 99%

Artificial intelligence for the echocardiographic assessment of valvular heart disease

Nedadur

Wang

Tsang

2022

Heart

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Developments in artificial intelligence (AI) have led to an explosion of studies exploring its application to cardiovascular medicine. Due to the need for training and expertise, one area where AI could be impactful would be in the diagnosis and management of valvular heart disease. This is because AI can be applied to the multitude of data generated from clinical assessments, imaging and biochemical testing during the care of the patient. In the area of valvular heart disease, the focus of AI has been on the echocardiographic assessment and phenotyping of patient populations to identify high-risk groups. AI can assist image acquisition, view identification for review, and segmentation of valve and cardiac structures for automated analysis. Using image recognition algorithms, aortic and mitral valve disease states have been directly detected from the images themselves. Measurements obtained during echocardiographic valvular assessment have been integrated with other clinical data to identify novel aortic valve disease subgroups and describe new predictors of aortic valve disease progression. In the future, AI could integrate echocardiographic parameters with other clinical data for precision medical management of patients with valvular heart disease.

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Deep learning: Integrating artificial intelligence into the sonography curriculum

Stoodley,

Oomens,

Robinson

2024

Sonography

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Artificial intelligence (AI) tools that enhance image acquisition, measurement, and interpretation are a valuable part of contemporary sonographic imaging systems, and emerging AI technology is forecast to feature more extensively in future systems. In this commentary, we draw upon contemporary literature and our experience in curriculum design to indicate how educators should approach integrating AI into the pre‐qualification sonography curriculum. This integration will promote understanding of and engagement with the technology to ensure that graduates are prepared for work with AI‐assisted systems. Knowledge of the technical and interpretive applications, the legal and ethical concerns, plus industry and accreditation body collaboration are proffered as important parts of an AI‐integrated training program.

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Automated Spectral Doppler Profile Tracing

Cited by 10 publications

References 28 publications

Intra-and inter-observer variability of point of care ultrasound measurements to evaluate hemodynamic parameters in healthy volunteers

Intra-and inter-observer variability of point of care ultrasound measurements to evaluate hemodynamic parameters in healthy volunteers

Artificial intelligence for the echocardiographic assessment of valvular heart disease

Deep learning: Integrating artificial intelligence into the sonography curriculum

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