Artificial Intelligence Application on Point-of-Care Ultrasound

Wang, Hong; Uraco, Adam M; Hughes, James

doi:10.1053/j.jvca.2021.02.064

Cited by 15 publications

(6 citation statements)

References 12 publications

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“…The use of advanced algorithms in POCUS has been indicated to provide a standardized approach to observing and assessing cardiac function and improving "computation time and accuracy." 25…”

Section: Augmented Reality (Ar)mentioning

confidence: 99%

“…For example, AI POCUS settings in lung ultrasound allow for automatic B-line detection, offering efficiency that is not achieved with computed tomography and chest x-ray. 25 One study indicated that POCUS devices with deep learning functionality may improve cardiac image acquisition and interpretation by novice learners. 26 As software continues to develop, virtual reality (VR) trainers utilizing virtual patients offer realistic training for a wide variety of symptoms and pathologies in a setting that is more forgiving than authentic patient encounters.…”

Section: Benefitsmentioning

confidence: 99%

“…Artificial intelligence (AI) applications have been developed for various training purposes, and programs continue to be created for POCUS. The use of advanced algorithms in POCUS has been indicated to provide a standardized approach to observing and assessing cardiac function and improving “computation time and accuracy.” 25 …”

Section: Drawbacksmentioning

confidence: 99%

“…Furthermore, AI technology may alleviate the knowledge and procedural capabilities that exist between providers and generate more rapid assessments by utilizing large stores of input data. For example, AI POCUS settings in lung ultrasound allow for automatic B-line detection, offering efficiency that is not achieved with computed tomography and chest x-ray 25 . One study indicated that POCUS devices with deep learning functionality may improve cardiac image acquisition and interpretation by novice learners 26 .…”

Section: Benefitsmentioning

confidence: 99%

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Assessment of the Educational and Training Modalities in Point-of-Care Ultrasound (POCUS) for Anesthesiologists

Mozingo,

Neely

2024

International Anesthesiology Clinics

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Point-of-care ultrasound (POCUS) has been developed as a critical tool for diagnostic patient evaluation and clinical management. Its transcendence into anesthesiology necessitates appropriate and effective educational strategies to assist in the development of anesthesia POCUS learners. Several professional societies, including the American Society of Anesthesiologists (ASA), American Society of Regional Anesthesia (ASRA), and Accreditation Council for Graduate Medical Education (ACGME) for anesthesiology have established minimum training standards for POCUS education for anesthesiologists, residents, and fellows.1,4 The article at hand aims to summarize and provide insight into the various educational modalities utilized in POCUS training, incorporate these strategies in the established “Indication, Acquisition, Interpretation, and Medical decision-making” (I-AIM) framework, and include recommendations on the minimum number of POCUS exams to aid in achieving competency. 3

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“…The use of advanced algorithms in POCUS has been indicated to provide a standardized approach to observing and assessing cardiac function and improving "computation time and accuracy." 25…”

Section: Augmented Reality (Ar)mentioning

confidence: 99%

Section: Benefitsmentioning

confidence: 99%

Section: Drawbacksmentioning

confidence: 99%

Section: Benefitsmentioning

confidence: 99%

See 2 more Smart Citations

Assessment of the Educational and Training Modalities in Point-of-Care Ultrasound (POCUS) for Anesthesiologists

Mozingo,

Neely

2024

International Anesthesiology Clinics

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“…POCUS device manufacturers have begun to employ artificial intelligence (AI) to aid in image acquisition and interpretation, which may help address the current training barriers faced by clinicians. 11 This technology could become complementary to traditional teaching methods by offering augmented guidance of probe placement to improve a user's manual dexterity and labeled images to aid in the rapid identification of pathology. 12 Previous investigations have shown that AI-assisted ultrasounds can aid novices in obtaining high quality cardiac images, accurately assessing ventricular function, and identifying non-trivial pericardial effusions.…”

Section: Introductionmentioning

confidence: 99%

Use of Artificial Intelligence for Acquisition of Limited Echocardiograms: A Randomized Controlled Trial for Educational Outcomes

Baum

Megha

Ren

et al. 2023

Preprint

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Background Point-of-care ultrasound (POCUS) machines may utilize artificial intelligence (AI) to enhance image interpretation and acquisition. This study investigates whether AI-enabled devices improve competency among POCUS novices. Methods We conducted a randomized controlled trial at a single academic institution from 2021-2022. Internal medicine trainees (N=43) with limited POCUS experience were randomized to receive a POCUS device with (Echonous, N=22) or without (Butterfly, N=21) AI-functionality for two weeks while on an inpatient rotation. The AI-device provided automatic labeling of cardiac structures, guidance for optimal probe placement to acquire cardiac views, and ejection fraction estimations. Participants were allowed to use the devices at their discretion for patient-related care. The primary outcome was the time to acquire an apical 4-chamber (A4C) image. Secondary outcomes included A4C image quality using the modified Rapid Assessment for Competency in Echocardiography (RACE) scale, correct identification of pathology, and participant attitudes. Measurements were performed at the time of randomization and at two-week follow-up. All scanning assessments were performed on the same standardized patient. Results Both AI and non-AI groups had similar scan times and image quality scores at baseline. At follow-up, the AI group had faster scan times (72 seconds [IQR 38-85] vs. 85 seconds [IQR 54-166]; p=0.01), higher image quality scores (4.5 [IQR 2-5.5] vs. 2 [IQR 1-3]; p<0.01) and correctly identified reduced systolic function more often (85% vs 50%; p=0.02) compared to the non-AI group. Trust in the AI features did not differ between the groups pre- or post-intervention. The AI group did not report increased confidence in their abilities to obtain or interpret cardiac images. Conclusions POCUS devices with AI features may improve image acquisition and interpretation by novices. Future studies are needed to determine the extent that AI impacts POCUS learning.

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Point-of-Care-Ultraschall in Gynäkologie und Geburtshilfe

Recker

2023

Gynäkologie

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ZusammenfassungDer Point-of-Care-Ultraschall (POCUS) hat sich in der medizinischen Diagnostik und Patientenversorgung als unverzichtbare Technik etabliert. Diese innovative Bildgebung ermöglicht es medizinischem Fachpersonal, Ultraschallaufnahmen direkt am Patientenbett oder in dessen unmittelbarer Nähe durchzuführen. Besonders in Akutsituationen beschleunigt dies die Diagnosestellung und erleichtert Therapieüberwachung wie Verlaufskontrollen. POCUS bietet hochwertige Echtzeit-Ultraschallbilder und ist weltweit ein essenzielles Instrument für Ärzte. In der Geburtshilfe und Gynäkologie ist POCUS ein besonders wichtiges Werkzeug. Es verbessert die Diagnosesicherheit, ermöglicht gezielte Therapien und stärkt die Arzt-Patienten-Beziehung. Insbesondere in Notsituationen kann der Einsatz von POCUS lebensrettend werden. Darüber hinaus bietet POCUS die Chance, in unterversorgten Regionen den Zugang zur Bildgebung zu erleichtern. Technologische Fortschritte bringen kompaktere, tragbare Geräte mit Bildqualität. Die Integration in die medizinische Ausbildung wird dabei die POCUS-Fähigkeiten der anwendenden Fachkräfte stärken. Auch in der pränatalen Versorgung und bei gynäkologischen Untersuchungen wird POCUS eine Rolle spielen, wobei telemedizinische Ansätze die Fernbehandlung von Patientinnen verbessern werden. Zusammenfassend revolutioniert POCUS die medizinische Diagnostik und Patientenversorgung. Mit fortschreitender Technologie, gezielter Ausbildung und zunehmender Integration wird es die Patientenversorgung in der Gynäkologie und Geburtshilfe weiter optimieren.

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Artificial Intelligence Application on Point-of-Care Ultrasound

Cited by 15 publications

References 12 publications

Assessment of the Educational and Training Modalities in Point-of-Care Ultrasound (POCUS) for Anesthesiologists

Assessment of the Educational and Training Modalities in Point-of-Care Ultrasound (POCUS) for Anesthesiologists

Use of Artificial Intelligence for Acquisition of Limited Echocardiograms: A Randomized Controlled Trial for Educational Outcomes

Point-of-Care-Ultraschall in Gynäkologie und Geburtshilfe

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