Automatically Scoring Lung Ultrasound Videos of COVID-19 and post-COVID-19 Patients

被引：0

作者：

Mento, Federico ^{[1
]}

Di Sabatino, Antonio ^{[2
]}

Fiengo, Anna ^{[2
]}

Sabatini, Umberto ^{[2
]}

Macioce, Veronica Narvena ^{[3
,4
]}

Tursi, Francesco ^{[3
]}

Sofia, Carmelo ^{[5
]}

Di Cienzo, Chiara ^{[5
]}

Smargiassi, Andrea ^{[5
]}

Inchingolo, Riccardo ^{[5
]}

Perrone, Tiziano ^{[2
,6
]}

Demi, Libertario ^{[1
]}

机构：

[1] Univ Trento, Dept Informat Engn & Comp Sci, Ultrasound Lab Trento, Trento, Italy

[2] IRCCS San Matteo, Dept Internal Med, Pavia, Italy

[3] Asst Lodi, UOS Pneumol Codogno, Lodi, Italy

[4] ATS Val Padana, Mantua, Italy

[5] Fdn Policlin Univ A Gemelli IRCCS, Dept Med & Surg Sci, UOC Pneumol, Rome, Italy

[6] Humanitas Gavazzeni, Emergency Dept, Bergamo, Italy

来源：

2022 IEEE INTERNATIONAL ULTRASONICS SYMPOSIUM (IEEE IUS) | 2022年

关键词：

Artificial intelligence; COVID-19; deep learning; lung ultrasound; post-COVID-19;

D O I：

10.1109/IUS54386.2022.9958500

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

Lung ultrasound (LUS) imaging is playing an important role in the current pandemic, allowing the evaluation of patients affected by COVID-19 pneumonia. However, LUS is limited to the visual inspection of ultrasound data, which negatively affects the reproducibility and reliability of the findings. For these reasons, we were the first to propose a standardized imaging protocol and a scoring system, from which we developed the first artificial intelligence (AI) models able to evaluate LUS videos. Furthermore, we demonstrated prognostic value of our approach and its utility for patients' stratification. In this study, we report on the level of agreement between AI and LUS clinical experts (MD) on LUS data acquired from both COVID-19 patients and post-COVID-19 patients.

引用

页数：4

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