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Clinical Implication and Prognostic Value of Artificial-Intelligence-Based Results of Chest Radiographs for Assessing Clinical Outcomes of COVID-19 Patients

Authors
 Hyun Joo Shin  ;  Min Hyung Kim  ;  Nak-Hoon Son  ;  Kyunghwa Han  ;  Eun-Kyung Kim  ;  Yong Chan Kim  ;  Yoon Soo Park  ;  Eun Hye Lee  ;  Taeyoung Kyong 
Citation
 DIAGNOSTICS, Vol.13(12) : 2090, 2023-06 
Journal Title
DIAGNOSTICS
Issue Date
2023-06
Keywords
COVID-19 ; artificial intelligence ; lung diseases ; prognosis ; software
Abstract
This study aimed to investigate the clinical implications and prognostic value of artificial intelligence (AI)-based results for chest radiographs (CXR) in coronavirus disease 2019 (COVID-19) patients. Patients who were admitted due to COVID-19 from September 2021 to March 2022 were retrospectively included. A commercial AI-based software was used to assess CXR data for consolidation and pleural effusion scores. Clinical data, including laboratory results, were analyzed for possible prognostic factors. Total O2 supply period, the last SpO2 result, and deterioration were evaluated as prognostic indicators of treatment outcome. Generalized linear mixed model and regression tests were used to examine the prognostic value of CXR results. Among a total of 228 patients (mean 59.9 ± 18.8 years old), consolidation scores had a significant association with erythrocyte sedimentation rate and C-reactive protein changes, and initial consolidation scores were associated with the last SpO2 result (estimate −0.018, p = 0.024). All consolidation scores during admission showed significant association with the total O2 supply period and the last SpO2 result. Early changing degree of consolidation score showed an association with deterioration (odds ratio 1.017, 95% confidence interval 1.005–1.03). In conclusion, AI-based CXR results for consolidation have potential prognostic value for predicting treatment outcomes in COVID-13 patients.
Files in This Item:
T202303507.pdf Download
DOI
10.3390/diagnostics13122090
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Hospital Medicine (입원의학과) > 1. Journal Papers
1. College of Medicine (의과대학) > Research Institute (부설연구소) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Kyong, Tae Young(경태영) ORCID logo https://orcid.org/0000-0001-5846-7808
Kim, Min Hyung(김민형)
Kim, Yong Chan(김용찬)
Kim, Eun-Kyung(김은경) ORCID logo https://orcid.org/0000-0002-3368-5013
Park, Yoon Soo(박윤수)
Shin, Hyun Joo(신현주) ORCID logo https://orcid.org/0000-0002-7462-2609
Lee, Eun Hye(이은혜) ORCID logo https://orcid.org/0000-0003-2570-3442
Han, Kyung Hwa(한경화)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/195527
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