Assisted probe guidance in cardiac ultrasound: A review

Ferraz, Sofia; Coimbra, Miguel; Pedrosa, João

doi:10.3389/fcvm.2023.1056055

Cited by 6 publications

(4 citation statements)

References 49 publications

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“…AI algorithms provide standardized guidelines and measurements, automating the generation of echocardiography reports [54,55]. Additionally, they identify errors and inconsistencies in interpretations, flagging measurement discrepancies and highlighting abnormalities [56][57][58][59]. This ensures thorough and guidelinealigned interpretations, enhancing research outcomes and evidence-based practice.…”

Section: Benefits and Implicationsmentioning

confidence: 99%

Embracing AI: The Imperative Tool for Echo Labs to Stay Ahead of the Curve

Vasile,

Iriart

2023

Diagnostics

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Advancements in artificial intelligence (AI) have rapidly transformed various sectors, and the field of echocardiography is no exception. AI-driven technologies hold immense potential to revolutionize echo labs’ diagnostic capabilities and improve patient care. This paper explores the importance for echo labs to embrace AI and stay ahead of the curve in harnessing its power. Our manuscript provides an overview of the growing impact of AI on medical imaging, specifically echocardiography. It highlights how AI-driven algorithms can enhance image quality, automate measurements, and accurately diagnose cardiovascular diseases. Additionally, we emphasize the importance of training echo lab professionals in AI implementation to optimize its integration into routine clinical practice. By embracing AI, echo labs can overcome challenges such as workload burden and diagnostic accuracy variability, improving efficiency and patient outcomes. This paper highlights the need for collaboration between echocardiography laboratory experts, AI researchers, and industry stakeholders to drive innovation and establish standardized protocols for implementing AI in echocardiography. In conclusion, this article emphasizes the importance of AI adoption in echocardiography labs, urging practitioners to proactively integrate AI technologies into their workflow and take advantage of their present opportunities. Embracing AI is not just a choice but an imperative for echo labs to maintain their leadership and excel in delivering state-of-the-art cardiac care in the era of advanced medical technologies.

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Section: Benefits and Implicationsmentioning

confidence: 99%

Embracing AI: The Imperative Tool for Echo Labs to Stay Ahead of the Curve

Vasile,

Iriart

2023

Diagnostics

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“…Considering the current state of cardiac US autonomous scanning technology, which remains in its infancy with few existing studies on US robots possessing both autonomous path planning and scanning pose compensation capabilities ( Ferraz et al, 2023 ), this paper addresses these technical challenges with two hypotheses: Firstly, ensuring the human body remains symmetrical and centered within the camera’s positioning window is crucial for the accuracy and stability of ROI localization. This objective can be achieved by utilizing camera-based recognition of human keypoints followed by pose correction.…”

Section: Introductionmentioning

confidence: 99%

Autonomous ultrasound scanning robotic system based on human posture recognition and image servo control: an application for cardiac imaging

Tang,

Wang,

Luo

et al. 2024

Front. Robot. AI

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In traditional cardiac ultrasound diagnostics, the process of planning scanning paths and adjusting the ultrasound window relies solely on the experience and intuition of the physician, a method that not only affects the efficiency and quality of cardiac imaging but also increases the workload for physicians. To overcome these challenges, this study introduces a robotic system designed for autonomous cardiac ultrasound scanning, with the goal of advancing both the degree of automation and the quality of imaging in cardiac ultrasound examinations. The system achieves autonomous functionality through two key stages: initially, in the autonomous path planning stage, it utilizes a camera posture adjustment method based on the human body’s central region and its planar normal vectors to achieve automatic adjustment of the camera’s positioning angle; precise segmentation of the human body point cloud is accomplished through efficient point cloud processing techniques, and precise localization of the region of interest (ROI) based on keypoints of the human body. Furthermore, by applying isometric path slicing and B-spline curve fitting techniques, it independently plans the scanning path and the initial position of the probe. Subsequently, in the autonomous scanning stage, an innovative servo control strategy based on cardiac image edge correction is introduced to optimize the quality of the cardiac ultrasound window, integrating position compensation through admittance control to enhance the stability of autonomous cardiac ultrasound imaging, thereby obtaining a detailed view of the heart’s structure and function. A series of experimental validations on human and cardiac models have assessed the system’s effectiveness and precision in the correction of camera pose, planning of scanning paths, and control of cardiac ultrasound imaging quality, demonstrating its significant potential for clinical ultrasound scanning applications.

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“…Such systems have been developed for imaging the cervix 6 , foetus 7 , carotid artery 8 , and breast 9 . Note that automated systems differ from assisted systems that guide the user to position the probe correctly, for which a comprehensive review of such systems for cardiac imaging was done in 10 . Except for proof-of-concept studies as in 11 where a bladder ultrasound was used to image the heart in the case of porcine cardiac arrest, no automated systems have been developed for use in human cardiac image acquisition.…”

Section: Introductionmentioning

confidence: 99%

An AI-powered navigation framework to achieve an automated acquisition of cardiac ultrasound images

Soemantoro,

Kardos,

Tang

et al. 2023

Sci Rep

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Echocardiography is an effective tool for diagnosing cardiovascular disease. However, numerous challenges affect its accessibility, including skill requirements, workforce shortage, and sonographer strain. We introduce a navigation framework for the automated acquisition of echocardiography images, consisting of 3 modules: perception, intelligence, and control. The perception module contains an ultrasound probe, a probe actuator, and a locator camera. Information from this module is sent to the intelligence module, which grades the quality of an ultrasound image for different echocardiography views. The window search algorithm in the control module governs the decision-making process in probe movement, finding the best location based on known probe traversal positions and image quality. We conducted a series of simulations using the HeartWorks simulator to assess the proposed framework. This study achieved an accuracy of 99% for the image quality model, 96% for the probe locator model, and 99% for the view classification model, trained on an 80/20 training and testing split. We found that the best search area corresponds with general guidelines: at the anatomical left of the sternum between the 2nd and 5th intercostal space. Additionally, the likelihood of successful acquisition is also driven by how long it stores past coordinates and how much it corrects itself. Results suggest that achieving an automated echocardiography system is feasible using the proposed framework. The long-term vision is of a widely accessible and accurate heart imaging capability within hospitals and community-based settings that enables timely diagnosis of early-stage heart disease.

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Assisted probe guidance in cardiac ultrasound: A review

Cited by 6 publications

References 49 publications

Embracing AI: The Imperative Tool for Echo Labs to Stay Ahead of the Curve

Embracing AI: The Imperative Tool for Echo Labs to Stay Ahead of the Curve

Autonomous ultrasound scanning robotic system based on human posture recognition and image servo control: an application for cardiac imaging

An AI-powered navigation framework to achieve an automated acquisition of cardiac ultrasound images

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