Clinical utilization of artificial intelligence-based COVID-19 pneumonia quantification using chest computed tomography - a multicenter retrospective cohort study in Japan

被引：5

作者：

Tanaka, Hiromu ^{[1
]}

Maetani, Tomoki ^{[2
]}

Chubachi, Shotaro ^{[1
]}

Tanabe, Naoya ^{[2
]}

Shiraishi, Yusuke ^{[2
]}

Asakura, Takanori ^{[1
,3
,4
]}

Namkoong, Ho ^{[5
]}

Shimada, Takashi ^{[1
]}

Azekawa, Shuhei ^{[1
]}

Otake, Shiro ^{[1
]}

Nakagawara, Kensuke ^{[1
]}

Fukushima, Takahiro ^{[1
]}

Watase, Mayuko ^{[1
]}

Terai, Hideki ^{[1
]}

Sasaki, Mamoru ^{[6
]}

Ueda, Soichiro ^{[6
]}

Kato, Yukari ^{[7
,8
]}

Harada, Norihiro ^{[7
,8
]}

Suzuki, Shoji ^{[9
]}

Yoshida, Shuichi ^{[9
]}

Tateno, Hiroki ^{[9
]}

Yamada, Yoshitake ^{[10
]}

Jinzaki, Masahiro ^{[10
]}

Hirai, Toyohiro ^{[2
]}

Okada, Yukinori ^{[11
,12
,13
]}

Koike, Ryuji ^{[14
]}

Ishii, Makoto ^{[1
,15
]}

Hasegawa, Naoki ^{[5
]}

Kimura, Akinori ^{[16
]}

Imoto, Seiya ^{[17
]}

Miyano, Satoru ^{[18
]}

Ogawa, Seishi ^{[19
]}

Kanai, Takanori ^{[20
]}

Fukunaga, Koichi ^{[1
]}

机构：

[1] Keio Univ, Sch Med, Dept Internal Med, Div Pulm Med, 35 Shinanomachi,Shinjuku Ku, Tokyo 1608582, Japan

[2] Kyoto Univ, Grad Sch Med, Dept Resp Med, 54 Kawahara Cho,Sakyo Ku, Kyoto 6068507, Japan

[3] Kitasato Univ, Sch Pharm, Dept Clin Med, Lab Bioregulatory Med, Tokyo, Japan

[4] Kitasato Univ, Kitasato Inst Hosp, Dept Resp Med, Tokyo, Japan

[5] Keio Univ, Sch Med, Dept Infect Dis, Tokyo, Japan

[6] JCHO Japan Community Hlth care Org, Saitama Med Ctr, Dept Resp Med, Saitama, Japan

[7] Juntendo Univ, Fac Med, Dept Resp Med, Tokyo, Japan

[8] Grad Sch Med, Tokyo, Japan

[9] Saitama City Hosp, Dept Pulm Med, Saitama, Japan

[10] Keio Univ, Sch Med, Dept Radiol, Tokyo, Japan

[11] Osaka Univ, Grad Sch Med, Dept Stat Genet, Suita, Japan

[12] Univ Tokyo, Grad Sch Med, Dept Genome Informat, Tokyo, Japan

[13] RIKEN, Ctr Integrat Med Sci, Lab Syst Genet, Yokohama, Kanagawa, Japan

[14] Tokyo Med & Dent Univ, Hlth Sci Res & Dev Ctr HeRD, Tokyo, Japan

[15] Nagoya Univ, Grad Sch Med, Dept Resp Med, Nagoya, Japan

[16] Tokyo Med & Dent Univ TMDU, Inst Res, Tokyo, Japan

[17] Univ Tokyo, Inst Med Sci, Human Genome Ctr, Div Hlth Med Intelligence, Tokyo, Japan

[18] Tokyo Med & Dent Univ, M&D Data Sci Ctr, Tokyo, Japan

[19] Kyoto Univ, Dept Pathol & Tumor Biol, Kyoto, Japan

[20] Keio Univ, Sch Med, Dept Internal Med, Div Gastroenterol & Hepatol, Tokyo, Japan

来源：

RESPIRATORY RESEARCH | 2023年 / 24卷 / 01期

关键词：

Artificial intelligence (AI)-based analysis; Computer Vision System; Pneumonia; Post-acute COVID-19 syndrome; SARS-CoV-2; infection; CT; DIAGNOSIS; SEVERITY;

D O I：

10.1186/s12931-023-02530-2

中图分类号：

R56 [呼吸系及胸部疾病];

学科分类号：

摘要：

BackgroundComputed tomography (CT) imaging and artificial intelligence (AI)-based analyses have aided in the diagnosis and prediction of the severity of COVID-19. However, the potential of AI-based CT quantification of pneumonia in assessing patients with COVID-19 has not yet been fully explored. This study aimed to investigate the potential of AI-based CT quantification of COVID-19 pneumonia to predict the critical outcomes and clinical characteristics of patients with residual lung lesions.MethodsThis retrospective cohort study included 1,200 hospitalized patients with COVID-19 from four hospitals. The incidence of critical outcomes (requiring the support of high-flow oxygen or invasive mechanical ventilation or death) and complications during hospitalization (bacterial infection, renal failure, heart failure, thromboembolism, and liver dysfunction) was compared between the groups of pneumonia with high/low-percentage lung lesions, based on AI-based CT quantification. Additionally, 198 patients underwent CT scans 3 months after admission to analyze prognostic factors for residual lung lesions.ResultsThe pneumonia group with a high percentage of lung lesions (N = 400) had a higher incidence of critical outcomes and complications during hospitalization than the low percentage group (N = 800). Multivariable analysis demonstrated that AI-based CT quantification of pneumonia was independently associated with critical outcomes (adjusted odds ratio [aOR] 10.5, 95% confidence interval [CI] 5.59-19.7), as well as with oxygen requirement (aOR 6.35, 95% CI 4.60-8.76), IMV requirement (aOR 7.73, 95% CI 2.52-23.7), and mortality rate (aOR 6.46, 95% CI 1.87-22.3). Among patients with follow-up CT scans (N = 198), the multivariable analysis revealed that the pneumonia group with a high percentage of lung lesions on admission (aOR 4.74, 95% CI 2.36-9.52), older age (aOR 2.53, 95% CI 1.16-5.51), female sex (aOR 2.41, 95% CI 1.13-5.11), and medical history of hypertension (aOR 2.22, 95% CI 1.09-4.50) independently predicted persistent residual lung lesions.ConclusionsAI-based CT quantification of pneumonia provides valuable information beyond qualitative evaluation by physicians, enabling the prediction of critical outcomes and residual lung lesions in patients with COVID-19.

引用

页数：11

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