Clinical evaluation of AI-assisted muscle ultrasound for monitoring muscle wasting in ICU patients
Authors: Nhat Phung Tran Huy , Van Hao Nguyen , Yen Lam Minh , Anh Nguyen Hoang , Khiem Dong Phu , Kerdegari Hamideh , Phuong Le Thanh , Hoang Vo Tan , Ngoc Nguyen Thanh , Thu Le Ngoc Minh , Trung Truong Ngoc , Pisani Luigi ,   VITAL Consortium, Canas Liane , Gomez Alberto , Kerdegari Hamideh , King Andrew , Modat Marc , Razavi Reza , Xochicale Miguel , Thao Dang Phuong , Kien Dang Trung , Thy Doan Bui Xuan , Trinh Dong Huu Khanh , Duc Du Hong , Geskus Ronald , Hai Ho Bich , Chanh Ho Quang , Van Hien Ho , Trieu Huynh Trung , Kestelyn Evelyne , Van Khoa Le Dinh , Khanh Le Thuy Thuy , Tran Luu Hoai Bao , An Luu Phuoc , Mcbride Angela , Vuong Nguyen Lam , Huy Nguyen Quang , Quyen Nguyen Than Ha , Giang Nguyen Thi , Trinh Nguyen Thi Diem , Le Thanh Nguyen Thi , Dung Nguyen Thi Phuong , Thao Nguyen Thi Phuong , Van Ninh Thi Thanh , Kieu Pham Tieu , Khanh Phan Nguyen Quoc , Lam Phung Khanh , Thwaites Guy , Thwaites Louise , Duc Tran Minh , Hung Trinh Manh , Turner Hugo , Van Nuil Jennifer Ilo , Huyen Vu Ngo Thanh , Yacoub Sophie , Tam Cao Thi , Thuy Duong Bich , Duong Ha Thi Hai , Nghia Ho Dang Trung , Chau Le Buu , Toan Le Mau , Thu Le Ngoc Minh , Thao Le Thi Mai , Tai Luong Thi Hue , Phu Nguyen Hoan , Viet Nguyen Quoc , Dung Nguyen Thanh , Nguyen Nguyen Thanh , Phong Nguyen Thanh , Anh Nguyen Thi Kim , Van Hao Nguyen , Van Thanh Duoc Nguyen , Oanh Pham Kieu Nguyet , Van Phan Thi Hong , Qui Phan Tu , Tho Phan Vinh , Thao Truong Thi Phuong , Pisani Luigi , Schultz Marcus , Ali Natasha , Clifton David , English Mike , Hagenah Jannis , Lu Ping , McKnight Jacob , Paton Chris , Zhu Tingting , Denehy Linda , Rollinson Thomas , Georgiou Pantelis , Perez Bernard Hernandez , Hill-Cawthorne Kerri , Holmes Alison , Karolcik Stefan , Ming Damien , Moser Nicolas , Manzano Jesus Rodriguez , Karlen Walter , Razavi Reza , Yacoub Sophie , Van Vinh Chau Nguyen , King Andrew P. , Thwaites Louise , Denehy Linda , Gomez Alberto 
Published: June 2024

AbstractMuscle 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 < 0.001). AI-assisted muscle ultrasound removes the need for manual tracing, increases reproducibility and saves time. This system may aid monitoring muscle size in ICU patients assisting rehabilitation programmes.