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Comparison of AI-powered 3D automated ultrasound tomography with standard handheld ultrasound for the visualization of the hands-clinical proof of concept


Getzmann, Jonas M; Kaniewska, Malwina; Rothenfluh, Esin; Borowka, Sophia; Guggenberger, Roman (2022). Comparison of AI-powered 3D automated ultrasound tomography with standard handheld ultrasound for the visualization of the hands-clinical proof of concept. Skeletal Radiology, 51(7):1415-1423.

Abstract

OBJECTIVE

To assess the ability of a newly developed AI-powered ultrasound 3D hand scanner to visualize joint structures in healthy hands and detect degenerative changes in cadaveric hands.

MATERIALS AND METHODS

Twelve individuals (6 males, 6 females, age 43.5 ± 17.8 years) underwent four scans with the 3D ultrasound tomograph (right and left hand, dorsal and palmar, respectively) as well as four sets of handheld ultrasound of predefined anatomic regions. The 3D ultrasound tomographic images and the standard handheld ultrasound images were assessed by two radiologists with regard to visibility of bone contour, joint capsule and space, and tendons. In addition, three cadaveric hands were scanned with the 3D ultrasound tomograph and CT.

RESULTS

Mean scan time for both hands was significantly faster with handheld ultrasound (10 min 30 s ± 95 s) compared to 3D ultrasound tomography (32 min 9 s ± 6 s; p < 0.001). Interreader and intermodality agreement was moderate (0.4 < κ ≤ 0.6) to substantial (0.6 < κ ≤ 0.8). Overall visibility of joint structures was comparable between the modalities at the level of the wrist (p = 0.408), and significantly better with handheld ultrasound at the level of the finger joints and the thumb (both p < 0.001). The 3D ultrasound tomograph was able to detect osteophytes in cadaveric hands which were confirmed by CT.

CONCLUSION

The AI-powered 3D ultrasound tomograph was able to visualize joint structures in healthy hands and singular osteophytes in cadaveric hands. Further technical improvements are necessary to shorten scan times and improve automated scanning of the finger joints and the thumb.

Abstract

OBJECTIVE

To assess the ability of a newly developed AI-powered ultrasound 3D hand scanner to visualize joint structures in healthy hands and detect degenerative changes in cadaveric hands.

MATERIALS AND METHODS

Twelve individuals (6 males, 6 females, age 43.5 ± 17.8 years) underwent four scans with the 3D ultrasound tomograph (right and left hand, dorsal and palmar, respectively) as well as four sets of handheld ultrasound of predefined anatomic regions. The 3D ultrasound tomographic images and the standard handheld ultrasound images were assessed by two radiologists with regard to visibility of bone contour, joint capsule and space, and tendons. In addition, three cadaveric hands were scanned with the 3D ultrasound tomograph and CT.

RESULTS

Mean scan time for both hands was significantly faster with handheld ultrasound (10 min 30 s ± 95 s) compared to 3D ultrasound tomography (32 min 9 s ± 6 s; p < 0.001). Interreader and intermodality agreement was moderate (0.4 < κ ≤ 0.6) to substantial (0.6 < κ ≤ 0.8). Overall visibility of joint structures was comparable between the modalities at the level of the wrist (p = 0.408), and significantly better with handheld ultrasound at the level of the finger joints and the thumb (both p < 0.001). The 3D ultrasound tomograph was able to detect osteophytes in cadaveric hands which were confirmed by CT.

CONCLUSION

The AI-powered 3D ultrasound tomograph was able to visualize joint structures in healthy hands and singular osteophytes in cadaveric hands. Further technical improvements are necessary to shorten scan times and improve automated scanning of the finger joints and the thumb.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Clinic for Reconstructive Surgery
04 Faculty of Medicine > University Hospital Zurich > Clinic for Diagnostic and Interventional Radiology
Dewey Decimal Classification:610 Medicine & health
Scopus Subject Areas:Health Sciences > Radiology, Nuclear Medicine and Imaging
Language:English
Date:1 July 2022
Deposited On:28 Jan 2022 16:00
Last Modified:26 Apr 2024 01:40
Publisher:Springer
ISSN:0364-2348
OA Status:Closed
Publisher DOI:https://doi.org/10.1007/s00256-021-03984-5
PubMed ID:34970704
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