ArticleMedical image analysis2021
Automatic annotation of cervical vertebrae in videofluoroscopy images via deep learning.
Article in Medical image analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 18 citations in OpenAlex.
- Multimodal framework for swallow detection in video-fluoroscopic swallow studies using manometric pressure distributions from dysphagic patients.International journal of computer assisted radiology and surgery · 2026Article
- Advancing modified barium swallow pre-sorting with deep learning: a new paradigm for the first step analysis in X-ray swallowing study.International journal of computer assisted radiology and surgery · 2026Article
- Artificial Intelligence in Videofluoroscopy Swallow Study Analysis: A Comprehensive Review.Dysphagia · 2025Review
- Non-invasive integrated swallowing kinematic analysis framework leveraging transformer-based multi-task neural networks.Computers in biology and medicine · 2025Article
- Article
- Optimizing Modified Barium Swallow Exam Workflow: Automating Pre-Analysis Video Sorting in Swallowing Function Assessment.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2024Article
- Elevating Patient Care With Deep Learning: High-Resolution Cervical Auscultation Signals for Swallowing Kinematic Analysis in Nasogastric Tube Patients.IEEE journal of translational engineering in health and medicine · 2024Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Judging swallowing kinematic impairments via videofluoroscopy represents the gold standard for the detection and evaluation of swallowing disorders. However, the efficiency and accuracy of such a biomechanical kinematic analysis vary significantly among human judges affected mainly by their training and experience. Here, we showed that a novel machine learning algorithm can with high accuracy automatically detect key anatomical points needed for a routine swallowing assessment in real-time. We trained a novel two-stage convolutional neural network to localize and measure the vertebral bodies using 1518 swallowing videofluoroscopies from 265 patients. Our network model yielded high accuracy as the mean distance between predicted points and annotations was 4.20 ± 5.54 pixels. In comparison, human inter-rater error was 4.35 ± 3.12 pixels. Furthermore, 93% of predicted points were less than five pixels from annotated pixels when tested on an independent dataset from 70 subjects. Our model offers more choices for speech language pathologists in their routine clinical swallowing assessments as it provides an efficient and accurate method for anatomic landmark localization in real-time, a task previously accomplished using an off-line time-sinking procedure.
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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.