ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2020
Automated Assessment System for Neonatal Endotracheal Intubation Using Dilated Convolutional Neural Network.
Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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
8 citing papers in PubMed, 10 citations in OpenAlex.
- Deep learning for video-based assessment of endotracheal intubation skills.Communications medicine · 2025Article
- Role of AI in Competency-Based Medical Education: Using EPA as the Magicbox.Archives of Iranian medicine · 2024Article
- Explainable artificial intelligence (XAI) for predicting the need for intubation in methanol-poisoned patients: a study comparing deep and machine learning models.Scientific reports · 2024Article
- Neurosonographic Classification in Premature Infants Receiving Omega-3 Supplementation Using Convolutional Neural Networks.Diagnostics (Basel, Switzerland) · 2024Article
- On the Horizon: Specific Applications of Automation and Artificial Intelligence in Anesthesiology.Current anesthesiology reports · 2023Article
- An Intelligent Augmented Reality Training Framework for Neonatal Endotracheal Intubation.International Symposium on Mixed and Augmented Reality : (ISMAR) [proceedings]. IEEE and ACM International Symposium on Mixed and Augmented Reality · 2020Article
- Automated Assessment of Neonatal Endotracheal Intubation Measured by a Virtual Reality Simulation System.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2020Article
- Mapping artificial intelligence models in emergency medicine: A scoping review on artificial intelligence performance in emergency care and education.Turkish journal of emergency medicineReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Neonatal endotracheal intubation (ETI) is an important, complex resuscitation skill, which requires a significant amount of practice to master. Current ETI practice is conducted on the physical manikin and relies on the expert instructors' assessment. Since the training opportunities are limited by the availability of expert instructors, an automatic assessment model is highly desirable. However, automating ETI assessment is challenging due to the complexity of identifying crucial features, providing accurate evaluations and offering valuable feedback to trainees. In this paper, we propose a dilated Convolutional Neural Network (CNN) based ETI assessment model, which can automatically provide an overall score and performance feedback to pediatric trainees. The proposed assessment model takes the captured kinematic multivariate time-series (MTS) data from the manikin-based augmented ETI system that we developed, automatically extracts the crucial features of captured data, and eventually provides an overall score as output. Furthermore, the visualization based on the class activation mapping (CAM) can automatically identify the motions that have significant impact on the overall score, thus providing useful feedback to trainees. Our model can achieve 92.2% average classification accuracy using the Leave-One-Out-Cross-Validation (LOOCV).
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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.