ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2024
Toward Autonomous Marker Localization for Lumbar Epidural Steroid Injection Robot.
Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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
Abstract
This study compares the effectiveness of the traditional minimum circle detection strategy, i.e. Welzl's algorithm, and the state-of-the-art nnU-Net in the localization of lumbar Epidural Steroid Injection (ESI) robot markers across different imaging modalities (MRI and CT). Fiducial frames and markers of identical design were used in both settings. To adjust for human errors in the benchmarking process, experiments were conducted to compare computational and manual marking results. Due to the complexity of the CT dataset, the accuracy and sensitivity of 3D nnU-Net were significantly superior to Welzl's algorithm. However, in the relatively simple MRI datasets, Welzl's algorithm outperformed the learning-based method. Subsequent experiments were conducted to validate that the localization accuracy meets the contingency requirements. This finding informs potential improvement in the current clinical workflow and the registration process of surgical robots in general.
Indexed as
Identifiers
What Socratic 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 Socratic graph.