ArticleInternational journal of computer assisted radiology and surgery2026
Automated image acquisition of apical four-chamber view with robotic echocardiography.
Article in International journal of computer assisted radiology and surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe purpose of this study is to propose a method for automatically estimating the position and angle of the acoustic window required to acquire the apical four-chamber view in echocardiography. The apical four-chamber view is clinically important, but it requires accurate probe placement near the cardiac apex. Acquisition of the apical four-chamber view is technically challenging because the acoustic window available for transthoracic ultrasound transmission is restricted by the intercostal space, and the cardiac apex and endocardial borders can be difficult to delineate due to rib shadowing and acoustic attenuation caused by the chest wall and subcutaneous tissue. Therefore, this study proposes a method for automatically acquiring cross sectional images without directly detecting the apex.
methodsThe proposed method estimates the apical four-chamber view through a three-step procedure. First, starting from the parasternal long-axis view, the probe is swept obliquely along the body surface while maintaining the mitral valve near the center of the ultrasound image and gradually rotating the probe toward the apical region. This maneuver is continued through the acoustic window where the cardiac apex can be visualized until the mitral valve leaflets are no longer visible. Second, the probe is rotated and angled to visualize the tricuspid valve, mitral valve, and interventricular septum in a single imaging plane. Finally, the probe is tilted and rocked to optimize the apical four-chamber view.
resultsProof-of-concept experiments demonstrated that the proposed framework could estimate probe positions and orientations for acquiring apical four-chamber views without direct apex detection. Across five subjects, the system achieved an image quality score of 68.0% ± 7.6% based on predefined scoring criteria. These preliminary findings suggest the potential feasibility of algorithm-assisted guidance for apical view acquisition in echocardiography.
conclusionThis study presented a three-step method to estimate the acoustic window for apical four-chamber acquisition without direct apex detection. A proof-of-concept evaluation in five subjects demonstrated that the proposed method could estimate the target acoustic window and suggested the potential of algorithm-assisted guidance for apical four-chamber view acquisition.
Indexed as
Identifiers
42545595What 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.