ArticleNPJ digital medicine2019
A computer vision system for deep learning-based detection of patient mobilization activities in the ICU.
Article in NPJ digital medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed, 117 citations in OpenAlex.
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- Harnessing the Power of Technology to Transform Delirium Severity Measurement in the Intensive Care Unit: Protocol for a Prospective Cohort Study.JMIR research protocols · 2025Article
- Efficient quality control of platelet-rich plasma preparation using computer vision and deep learning.Journal of biomedical optics · 2025Article
- Early detection of Parkinson's disease using a multi area graph convolutional network.Scientific reports · 2025Article
- Developing ICU Clinical Behavioral Atlas Using Ambient Intelligence and Computer Vision.NEJM AI · 2025Article
- Hospitalization prediction from the emergency department using computer vision AI with short patient video clips.NPJ digital medicine · 2024Article
- Artificial Intelligence in IVF Laboratories: Elevating Outcomes Through Precision and Efficiency.Biology · 2024Review
- Depth over RGB: automatic evaluation of open surgery skills using depth camera.International journal of computer assisted radiology and surgery · 2024Article
- Deep learning to quantify care manipulation activities in neonatal intensive care units.NPJ digital medicine · 2024Article
- Applied Artificial Intelligence in Healthcare: A Review of Computer Vision Technology Application in Hospital Settings.Journal of imaging · 2024Review
- Automation of Functional Mobility Assessments at Home Using a Multimodal Sensor System Integrating Inertial Measurement Units and Computer Vision (IMU-Vision).Physical therapy · 2024Article
- Leveraging physiology and artificial intelligence to deliver advancements in health care.Physiological reviews · 2023Review
- Self-supervised contrastive learning using CT images for PD-1/PD-L1 expression prediction in hepatocellular carcinoma.Frontiers in oncology · 2023Article
- Novel deep transfer learning model for COVID-19 patient detection using X-ray chest images.Journal of ambient intelligence and humanized computing · 2023Article
- Improving disease prevention, diagnosis, and treatment using novel bionic technologies.Bioengineering & translational medicine · 2023Review
- Research Hotspots and Trends of Deep Learning in Critical Care Medicine: A Bibliometric and Visualized Study.Journal of multidisciplinary healthcare · 2023Article
Corrections and comments
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Authors and funding
15 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early and frequent patient mobilization substantially mitigates risk for post-intensive care syndrome and long-term functional impairment. We developed and tested computer vision algorithms to detect patient mobilization activities occurring in an adult ICU. Mobility activities were defined as moving the patient into and out of bed, and moving the patient into and out of a chair. A data set of privacy-safe-depth-video images was collected in the Intermountain LDS Hospital ICU, comprising 563 instances of mobility activities and 98,801 total frames of video data from seven wall-mounted depth sensors. In all, 67% of the mobility activity instances were used to train algorithms to detect mobility activity occurrence and duration, and the number of healthcare personnel involved in each activity. The remaining 33% of the mobility instances were used for algorithm evaluation. The algorithm for detecting mobility activities attained a mean specificity of 89.2% and sensitivity of 87.2% over the four activities; the algorithm for quantifying the number of personnel involved attained a mean accuracy of 68.8%.
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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.