ArticleHealthcare technology letters
Real-Time Tool Detection in Laparoscopic Datasets for Surgical Training in Low-Resource Settings.
Article in Healthcare technology letters. 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
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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.
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Who cites it
1 citing paper in PubMed.
- Real-Time Tool Detection in Laparoscopic Datasets for Surgical Training in Low-Resource Settings.Healthcare technology lettersArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In low-resource settings, there is a critical need for skilled surgeons. Alternative training processes that include computer-assisted surgical skill evaluation are essential to address this gap. Using tool detection, surgical videos can be leveraged to derive insights into surgical skill assessment. However, state-of-the-art laparoscopic tool detection methods usually have more complex architectures tailored for in vivo data, which suffer from challenges such as smoke, occlusion, bleeding, etc., which are absent from in vitro training contexts. Thus, this paper tests multiple anchor-based and anchor-free, convolution- and transformer-based, traditional (non-surgical domain-specific) computer vision deep learning state-of-the-art models. With various hardware configurations on a newly curated in-house laparoscopic box-trainer dataset, we emphasise real-time performance on low-cost embedded devices. Overall, the anchor-free YOLOv8-X model was the most accurate, achieving
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Registered trials
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