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Clinical benefit of AI-assisted lung ultrasound in a resource-limited intensive care unit

2023; BioMed Central; Volume: 27; Issue: 1 Linguagem: Inglês

10.1186/s13054-023-04548-w

1466-609X

Phung Tran Huy Nhat, Nguyễn Văn Hảo, Phan Vinh Tho, Hamideh Kerdegari, Luigi Pisani, Le Ngoc Minh Thu, Le Thanh Phuong, Ha Thi Hai Duong, Duong Bich Thuy, Angela McBride, Miguel Xochicale, Marcus J. Schultz, Reza Razavi, Andrew P. King, Louise Thwaites, Nguyễn Văn Vĩnh Châu, Sophie Yacoub, Dang Phuong Thao, Trung Kien Dang, Doan Bui Xuan Thy, Dong Huu Khanh Trinh, Du Hong Duc, Ronald B. Geskus, Ho Bich Hai, Ho Quang Chanh, Ho Van Hien, Huynh Trung Trieu, Evelyne Kestelyn, Lam Minh Yen, Le Dinh Van Khoa, Le Thanh Phuong, Le Thuy Thuy Khanh, Luu Hoai Bao Tran, An Phuoc Luu, Angela McBride, Nguyen Lam Vuong, Nguyễn Quang Huy, Nguyen Than Ha Quyen, Nguyễn Thanh Ngọc, Nguyen Thi Giang, Nguyen Thi Diem Trinh, Nguyen Thi Le Thanh, Nguyễn Thị Phương Dung, Nguyễn Thị Phương Thảo, Ninh Thi Thanh Van, Pham Tieu Kieu, Phan Nguyen Quoc Khanh, Phung Khanh Lam, Phung Tran Huy Nhat, Louise Thwaites, Louise Thwaites, Duc Minh Tran, Trinh Manh Hung, Hugo C. Turner, Jennifer Ilo Van Nuil, Vo Tan Hoang, Vu Ngo Thanh Huyen, Sophie Yacoub, Cao Thi Tam, Duong Bich Thuy, Ha Thi Hai Duong, Ho Dang Trung Nghia, Le Buu Chau, Le Mau Toan, Lê Ngọc Minh Thư, Le Thi Mai Thao, Luong Thi Hue Tai, Nguyen Hoan Phu, Nguyễn Quốc Việt, Nguyen Thanh Dung, Nguyen Thanh Nguyen, Nguyễn Thanh Phong, Nguyễn Thị Kim Ánh, Nguyễn Văn Hảo, Nguyen Van Thanh Duoc, Pham Kieu Nguyet Oanh, Phan Thi Van, Phan Tu Qui, Phan Vinh Tho, Truong Thi Phuong Thao, Natasha Ali, David A. Clifton, Mike English, Jannis Hagenah, Ping Lü, Jacob McKnight, Chris Paton, Tingting Zhu, Pantelis Georgiou, Bernard Hernandez Perez, Kerri Hill-Cawthorne, Alison Holmes, Štefan Karolčík, Damien Ming, Nicolas Moser, Jesús Rodríguez-Manzano, Liane S. Canas, Alberto Gómez, Hamideh Kerdegari, Andrew King, Marc Modat, Reza Razavi, Miguel Xochicale, Walter Karlen, Linda Denehy, Thomas Rollinson, Luigi Pisani, Marcus J. Schultz, Alberto Gómez,

Radiation Dose and Imaging

Interpreting point-of-care lung ultrasound (LUS) images from intensive care unit (ICU) patients can be challenging, especially in low- and middle- income countries (LMICs) where there is limited training available. Despite recent advances in the use of Artificial Intelligence (AI) to automate many ultrasound imaging analysis tasks, no AI-enabled LUS solutions have been proven to be clinically useful in ICUs, and specifically in LMICs. Therefore, we developed an AI solution that assists LUS practitioners and assessed its usefulness in a low resource ICU.This was a three-phase prospective study. In the first phase, the performance of four different clinical user groups in interpreting LUS clips was assessed. In the second phase, the performance of 57 non-expert clinicians with and without the aid of a bespoke AI tool for LUS interpretation was assessed in retrospective offline clips. In the third phase, we conducted a prospective study in the ICU where 14 clinicians were asked to carry out LUS examinations in 7 patients with and without our AI tool and we interviewed the clinicians regarding the usability of the AI tool.The average accuracy of beginners' LUS interpretation was 68.7% [95% CI 66.8-70.7%] compared to 72.2% [95% CI 70.0-75.6%] in intermediate, and 73.4% [95% CI 62.2-87.8%] in advanced users. Experts had an average accuracy of 95.0% [95% CI 88.2-100.0%], which was significantly better than beginners, intermediate and advanced users (p < 0.001). When supported by our AI tool for interpreting retrospectively acquired clips, the non-expert clinicians improved their performance from an average of 68.9% [95% CI 65.6-73.9%] to 82.9% [95% CI 79.1-86.7%], (p < 0.001). In prospective real-time testing, non-expert clinicians improved their baseline performance from 68.1% [95% CI 57.9-78.2%] to 93.4% [95% CI 89.0-97.8%], (p < 0.001) when using our AI tool. The time-to-interpret clips improved from a median of 12.1 s (IQR 8.5-20.6) to 5.0 s (IQR 3.5-8.8), (p < 0.001) and clinicians' median confidence level improved from 3 out of 4 to 4 out of 4 when using our AI tool.AI-assisted LUS can help non-expert clinicians in an LMIC ICU improve their performance in interpreting LUS features more accurately, more quickly and more confidently.