1. Clinical evaluation of AI-assisted muscle ultrasound for monitoring muscle wasting in ICU patients
- Author
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Phung Tran Huy Nhat, Nguyen Van Hao, Lam Minh Yen, Nguyen Hoang Anh, Dong Phu Khiem, Hamideh Kerdegari, Le Thanh Phuong, Vo Tan Hoang, Nguyen Thanh Ngoc, Le Ngoc Minh Thu, Truong Ngoc Trung, Luigi Pisani, VITAL Consortium, Reza Razavi, Sophie Yacoub, Nguyen Van Vinh Chau, Andrew P. King, Louise Thwaites, Linda Denehy, and Alberto Gomez
- Subjects
Muscle ultrasound ,Muscle wasting ,Intensive care unit ,Artificial intelligence ,Real-time ,Medicine ,Science - Abstract
Abstract Muscle ultrasound has been shown to be a valid and safe imaging modality to assess muscle wasting in critically ill patients in the intensive care unit (ICU). This typically involves manual delineation to measure the rectus femoris cross-sectional area (RFCSA), which is a subjective, time-consuming, and laborious task that requires significant expertise. We aimed to develop and evaluate an AI tool that performs automated recognition and measurement of RFCSA to support non-expert operators in measurement of the RFCSA using muscle ultrasound. Twenty patients were recruited between Feb 2023 and July 2023 and were randomized sequentially to operators using AI (n = 10) or non-AI (n = 10). Muscle loss during ICU stay was similar for both methods: 26 ± 15% for AI and 23 ± 11% for the non-AI, respectively (p = 0.13). In total 59 ultrasound examinations were carried out (30 without AI and 29 with AI). When assisted by our AI tool, the operators showed less variability between measurements with higher intraclass correlation coefficients (ICCs 0.999 95% CI 0.998–0.999 vs. 0.982 95% CI 0.962–0.993) and lower Bland Altman limits of agreement (± 1.9% vs. ± 6.6%) compared to not using the AI tool. The time spent on scans reduced significantly from a median of 19.6 min (IQR 16.9–21.7) to 9.4 min (IQR 7.2–11.7) compared to when using the AI tool (p
- Published
- 2024
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