ArticleInternational journal of nursing sciences2026
Image recognition-based detection system for preventing accidental dislodgement of head-and-neck medical supplies in ICU patients: A feasibility randomized controlled trial.
Article in International journal of nursing sciences, 2026. 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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8 authors.
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Abstract
Objectives: This study aimed to design and evaluate a detection system for the accidental dislodgement of head-and-neck medical supplies through hand position recognition and tracking in Intensive Care Unit (ICU) patients. Methods: We conducted a single-center, prospective, parallel-group feasibility randomized controlled trial. We recruited 80 participants using convenience sampling from the ICU of a hospital in Ningbo City, Zhejiang Province, between March 2025 and June 2025, and they were randomly assigned to either the control group (routine care) or the intervention group (routine care plus image recognition-based detection system). The system continuously tracked patients' hand positions via bedside cameras and generated real-time alarms when hands entered predefined risk zones, notifying on-duty nurses to enable early intervention. System stability was assessed by continuous system uptime; system performance and clinical feasibility were evaluated by the frequencies of risk actions and accidental dislodgement of medical supplies (ADMS). Results: All 80 participants completed the intervention, with 40 patients in each group. The baseline characteristics and median observation time of the two groups were balanced (intervention group: 48 h/patient vs. control group: 49 h/patient). Compared with the control group, the intervention group showed fewer ADMS (2/40 vs. 9/40) and detected more risk actions per 100 h (36 vs. 25); all system-detected events had corroborating images with complete concordance on manual review, and all nurse-recorded hand-contact events were accurately captured. Conclusions: The study demonstrated that the image recognition-based detection system can function stably in clinical settings, providing accurate and continuous surveillance while supporting the early detection of risk actions. By reducing the observation burden and offering real-time cognitive support, the system complements routine nursing care and serves as an additional safety measure in ICU practice. With further optimization and larger multicenter validation, this approach could have the potential to make a significant contribution to the development of smart ICUs and the broader digital transformation of nursing care.
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