A Generalizable Artificial Intelligence Model for COVID-19 Classification Task Using Chest X-ray Radiographs: Evaluated Over Four Clinical Datasets with 15,097 Patients

• URL: http://arxiv.org/abs/2210.02189v1
• Date: Tue, 4 Oct 2022 04:12:13 GMT
• Title: A Generalizable Artificial Intelligence Model for COVID-19 Classification Task Using Chest X-ray Radiographs: Evaluated Over Four Clinical Datasets with 15,097 Patients
• Authors: Ran Zhang, Xin Tie, John W. Garrett, Dalton Griner, Zhihua Qi, Nicholas B. Bevins, Scott B. Reeder and Guang-Hong Chen
• Abstract summary: The generalizability of the trained model was retrospectively evaluated using four different real-world clinical datasets. The AI model trained using a single-source clinical dataset achieved an AUC of 0.82 when applied to the internal temporal test set. An AUC of 0.79 was achieved when applied to a multi-institutional COVID-19 dataset collected by the Medical Imaging and Data Resource Center.
• Score: 6.209420804714487
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Purpose: To answer the long-standing question of whether a model trained from a single clinical site can be generalized to external sites. Materials and Methods: 17,537 chest x-ray radiographs (CXRs) from 3,264 COVID-19-positive patients and 4,802 COVID-19-negative patients were collected from a single site for AI model development. The generalizability of the trained model was retrospectively evaluated using four different real-world clinical datasets with a total of 26,633 CXRs from 15,097 patients (3,277 COVID-19-positive patients). The area under the receiver operating characteristic curve (AUC) was used to assess diagnostic performance. Results: The AI model trained using a single-source clinical dataset achieved an AUC of 0.82 (95% CI: 0.80, 0.84) when applied to the internal temporal test set. When applied to datasets from two external clinical sites, an AUC of 0.81 (95% CI: 0.80, 0.82) and 0.82 (95% CI: 0.80, 0.84) were achieved. An AUC of 0.79 (95% CI: 0.77, 0.81) was achieved when applied to a multi-institutional COVID-19 dataset collected by the Medical Imaging and Data Resource Center (MIDRC). A power-law dependence, N^(k )(k is empirically found to be -0.21 to -0.25), indicates a relatively weak performance dependence on the training data sizes. Conclusion: COVID-19 classification AI model trained using well-curated data from a single clinical site is generalizable to external clinical sites without a significant drop in performance.

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