Evidence map›Paper›PMID 41063098›Full record

ArticleBMC nursing2025

Design and evaluation of a non-contact AI system for monitoring unplanned device removal in neurocritical care.

Zhongjie Shi, Hualing Huang, Taotao Shi, Dongfeng Li, Chongfei Li, Yixia Chen, Zhangyu Li, Sifang Chen, Zhanxiang Wang

Abstract read
In one paragraph

Article in BMC nursing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

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.

2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Zhongjie ShiDepartment of Neurosurgery, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361003, China.
Hualing HuangDepartment of Neurosurgery, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361003, China. naicha1225@126.com.
Taotao ShiThe School of Clinical Medicine, Fujian Medical University, Fuzhou, 350122, China.
Dongfeng LiDepartment of Neurosurgery, Sanya Hospital of Traditional Chinese Medicine, Hainan, 572000, Sanya, China.
Chongfei LiThe School of Clinical Medicine, Fujian Medical University, Fuzhou, 350122, China.
Yixia ChenDepartment of Neurosurgery, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361003, China.
Zhangyu LiDepartment of Neurosurgery, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361003, China.
Sifang ChenDepartment of Neurosurgery, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361003, China.
Zhanxiang WangDepartment of Neurosurgery, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361003, China. wzxcn@foxmail.com.

Funding

National Natural Science Foundation of Fujian Province 2022J011355Young and Middle-aged Backbone Talent Training Project of Fujian Provincial Health Commission 2022GGB010
6 · The paper itself

Abstract

objectiveTo develop a non-contact monitoring system for unplanned device removal (UDR) and to preliminarily evaluate its clinical feasibility.

methodsWe developed a non-contact monitoring system for UDR featuring an interactive user interface. It operates reliably on CPU hardware, eliminating the need for GPU acceleration or cloud-based processing while supporting adjustable sensitivity and incorporating multiple mechanisms to mitigate alarm fatigue. We applied the system to neurocritical care patients, using unplanned removal of cranial drainage tubes as a representative scenario, and conducted a proof-of-concept, prospective, randomized controlled trial to determine the number of hand-to-head occurrences detected by the system vs. direct observation.

resultsThe system functioned continuously throughout the study period without any interruption. Individual monitoring durations ranged from 25.7 to 88.9 h, with a median of 45.5 (IQR: 31.0–55.8) hours. No crashes or abnormal terminations are observed. A total of 65 neurocritical care patients were enrolled, with 34 in the intervention group and 31 in the control group. The median number of hand-to-head contacts was 18.0 (IQR: 15.3–20.0) in the intervention group and 12.0 (IQR: 11.0–13.0) in the control group, with a significant difference between groups (p < 0.001). The incidence of UDR was 2.9% in the intervention group versus 16.1% in the control group (p = 0.10).

conclusionThis study preliminarily validated the clinical value of an AI-based system for monitoring UDR in neurocritical care patients. The system provides a non-contact, low-cost, and efficient monitoring solution, and demonstrates strong potential for broader clinical applications. With further optimization, it may be adapted for use with various critical devices in pediatric, respiratory, and other intensive care settings. CLINICAL TRAIL NUMBER: Not applicable.

Indexed as

Artificial intelligence (AI)Neurocritical careNon-contactUnplanned device removal (UDR)

Identifiers

PMID41063098
PMCPMC12506316

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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.