ArticleMedical image analysis2021
Real-time medical phase recognition using long-term video understanding and progress gate method.
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 3 papers.
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
3 citing papers in PubMed.
- Human intention recognition for trauma resuscitation: An interpretable deep learning approach for medical process data.Journal of biomedical informatics · 2025Article
- Real-time Context-Aware Multimodal Network for Activity and Activity-Stage Recognition from Team Communication in Dynamic Clinical Settings.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2023Article
- Surgical Gesture Recognition in Laparoscopic Tasks Based on the Transformer Network and Self-Supervised Learning.Bioengineering (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
We introduce a real-time system for recognizing five phases of the trauma resuscitation process, the initial management of injured patients in the emergency department. We used depth videos as input to preserve the privacy of the patients and providers. The depth videos were recorded using a Kinect-v2 mounted on the sidewall of the room. Our dataset consisted of 183 depth videos of trauma resuscitations. The model was trained on 150 cases with more than 30 minutes each and tested on the remaining 33 cases. We introduced a reduced long-term operation (RLO) method for extracting features from long segments of video and combined it with the regular model having short-term information only. The model with RLO outperformed the regular short-term model by 5% using the accuracy score. We also introduced a progress gate (PG) method to distinguish visually similar phases using video progress. The final system achieved 91% accuracy and significantly outperformed previous systems for phase recognition in this setting.
Indexed as
Identifiers
What 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.