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

Phung tran huy Nhat, Nguyen Van hao, 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 Schultz, Reza Razavi, Andrew King, Louise Thwaites, Nguyen Van vinh chau, Sophie Yacoub, Dang phuong Thao, Dang trung Kien, Doan bui xuan ThyDong huu khanh Trinh, Du hong Duc, Ronald 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, Luu phuoc An, Angela Mcbride, Nguyen lam Vuong, Nguyen quang Huy, Nguyen than ha Quyen, Nguyen thanh Ngoc, Nguyen thi Giang, Nguyen thi diem Trinh, Nguyen thi Le thanh, Nguyen thi phuong Dung, Nguyen thi phuong Thao, Ninh thi thanh Van, Pham tieu Kieu, Phan nguyen quoc Khanh, Phung khanh Lam, Phung tran huy Nhat, Guy Thwaites, Louise Thwaites, Tran minh Duc, Trinh manh Hung, Hugo 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, Le ngoc minh Thu, Le thi mai Thao, Luong thi hue Tai, Nguyen hoan Phu, Nguyen quoc Viet, Nguyen thanh Dung, Nguyen thanh Nguyen, Nguyen thanh Phong, Nguyen thi kim Anh, Nguyen Van hao, Nguyen Van thanh duoc, Pham kieu nguyet Oanh, Phan thi hong Van, Phan tu Qui, Phan vinh Tho, Truong thi phuong Thao, Natasha Ali, David Clifton, Mike English, Jannis Hagenah, Ping Lu, Jacob Mcknight, Chris Paton, Tingting Zhu, Pantelis Georgiou, Bernard hernandez Perez, Kerri Hill-Cawthorne, Alison Holmes, Stefan Karolcik, Damien Ming, Nicolas Moser, Jesus rodriguez Manzano, Liane Canas, Alberto Gomez, Hamideh Kerdegari, Andrew King, Marc Modat, Reza Razavi, Miguel Xochicale, Walter Karlen, Linda Denehy, Thomas Rollinson, Luigi Pisani, Marcus Schultz, Alberto Gomez

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Background: 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. Methods: 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. Results: 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. Conclusions: 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.

Original languageEnglish
Article number257
JournalCRITICAL CARE
Volume27
Issue number1
DOIs
Publication statusPublished - 1 Jul 2023

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