ArticleSkeletal radiology2022
Comparison of AI-powered 3D automated ultrasound tomography with standard handheld ultrasound for the visualization of the hands-clinical proof of concept.
Article in Skeletal radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it, 2 citations in OpenAlex.
- The use of artificial intelligence in musculoskeletal ultrasound: a systematic review of the literature.La Radiologia medica · 2024Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
objectiveTo 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
methodsTwelve 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.
resultsMean 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.
conclusionThe 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.