An automated COVID-19 triage pipeline using artificial intelligence based on chest radiographs and clinical data.
An automated COVID-19 triage pipeline using artificial intelligence based on chest radiographs and clinical data.
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3 型脊髓小脑共济失调中皮质回旋与白质完整性的关联
DOI：
10.1038/s41746-021-00546-w
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发表时间：
2022-01-14
影响因子：
15.2
通讯作者：
Bai HX
中科院分区：
文献类型：
--
作者：
Kim CK;Choi JW;Jiao Z;Wang D;Wu J;Yi TY;Halsey KC;Eweje F;Tran TML;Liu C;Wang R;Sollee J;Hsieh C;Chang K;Yang FX;Singh R;Ou JL;Huang RY;Feng C;Feldman MD;Liu T;Gong JS;Lu S;Eickhoff C;Feng X;Kamel I;Sebro R;Atalay MK;Healey T;Fan Y;Liao WH;Wang J;Bai HX
虽然存在COVID-19诊断和预后人工智能模型，但由于其高偏倚风险，很少能实现实际应用。我们的目标是开发一种诊断模型，以解决先前研究的显着缺陷，并将其整合到全自动分诊管道中，以检查胸部x线片是否存在、严重程度和COVID-19肺炎的进展。使用DICOM图像分析和存档系统收集扫描，该系统可与医院的图像存储库通信。作者收集了6500多张非公开胸部x光片，包括不同的COVID-19严重程度，以及放射学报告和RT-PCR数据。作者提供了一个内部测试集和两个外部测试集来评估模型的泛化性，并将其与传统放射科医生的解释进行比较。该管道在80张x线片的前瞻性队列中进行了评估，报告了95%的诊断准确率。该研究减轻了人工智能模型开发中的偏见，并展示了端到端COVID-19分诊平台的价值。
While COVID-19 diagnosis and prognosis artificial intelligence models exist, very few can be implemented for practical use given their high risk of bias. We aimed to develop a diagnosis model that addresses notable shortcomings of prior studies, integrating it into a fully automated triage pipeline that examines chest radiographs for the presence, severity, and progression of COVID-19 pneumonia. Scans were collected using the DICOM Image Analysis and Archive, a system that communicates with a hospital’s image repository. The authors collected over 6,500 non-public chest X-rays comprising diverse COVID-19 severities, along with radiology reports and RT-PCR data. The authors provisioned one internally held-out and two external test sets to assess model generalizability and compare performance to traditional radiologist interpretation. The pipeline was evaluated on a prospective cohort of 80 radiographs, reporting a 95% diagnostic accuracy. The study mitigates bias in AI model development and demonstrates the value of an end-to-end COVID-19 triage platform.
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影响因子：
4.6
作者：
Zargari Khuzani A;Heidari M;Shariati SA
通讯作者：
Shariati SA
影响因子：
19.5
作者：
Russakovsky, Olga;Deng, Jia;Fei-Fei, Li
通讯作者：
Fei-Fei, Li
影响因子：
1.8
作者：
Agresti, A;Coull, BA
通讯作者：
Coull, BA
DOI：
10.1016/j.inffus.2021.04.008
发表时间：
2021-12
期刊：
An international journal on information fusion
影响因子：
--
作者：
Maguolo G;Nanni L
通讯作者：
Nanni L
DOI：
10.1148/ryai.2020200079
发表时间：
2020-07-01
期刊：
RADIOLOGY-ARTIFICIAL INTELLIGENCE
影响因子：
--
作者：
Li, Matthew D.;Arun, Nishanth T.;Kalpathy-Cramer, Jayashree
通讯作者：
Kalpathy-Cramer, Jayashree