Using artificial intelligence to risk stratify COVID-19 patients based on chest X-ray findings

Abstract

Background Deep learning-based radiological image analysis could facilitate use of chest x-rays as a triaging tool for COVID-19 diagnosis in resource-limited settings. This study sought to determine whether a modified commercially available deep learning algorithm (M-qXR) could risk stratify patients with suspected COVID-19 infections. Methods A dual track clinical validation study was designed to assess the clinical accuracy of M-qXR. The algorithm evaluated all Chest-X-rays (CXRs) performed during the study period for abnormal findings and assigned a COVID-19 risk score. Four independent radiologists served as radiological ground truth. The M-qXR algorithm output was compared against radiological ground truth and summary statistics for prediction accuracy were calculated. In addition, patients who underwent both PCR testing and CXR for suspected COVID-19 infection were included in a co-occurrence matrix to assess the sensitivity and specificity of the M-qXR algorithm. Results 625 CXRs were included in the clinical validation study. 98% of total interpretations made by M-qXR agreed with ground truth (p = 0.25). M-qXR correctly identified the presence or absence of pulmonary opacities in 94% of CXR interpretations. M-qXR's sensitivity, specificity, PPV, and NPV for detecting pulmonary opacities were 94%, 95%, 99%, and 88% respectively. M-qXR correctly identified the presence or absence of pulmonary consolidation in 88% of CXR interpretations (p = 0.48). M-qXR's sensitivity, specificity, PPV, and NPV for detecting pulmonary consolidation were 91%, 84%, 89%, and 86% respectively. Furthermore, 113 PCR-confirmed COVID-19 cases were used to create a co-occurrence matrix between M-qXR's COVID-19 risk score and COVID-19 PCR test results. The PPV and NPV of a medium to high COVID-19 risk score assigned by M-qXR yielding a positive COVID-19 PCR test result was estimated to be 89.7% and 80.4% respectively. Conclusion M-qXR was found to have comparable accuracy to radiological ground truth in detecting radiographic abnormalities on CXR suggestive of COVID-19.

Research progress of artificial intelligence technology in COVID-19

“…Numerous researchers have employed diverse predictive indicators in their investigation of COVID-19 to evaluate the prognosis of the disease's ultimate clinical outcome. These predictive indicators comprise blood samples, electrocardiograms, imaging data (CT, XCR), respiratory parameters, clinical symptoms, and other relevant information [28][29][30][31][32][33][34][35] . The concluding indicators of the study were also varied, with a primary focus on the severity of the illness, the risk of mortality, the actual mortality rate, the length of hospitalization, and other relevant factors.…”

Using Artificial Intelligence to Stratify Normal versus Abnormal Chest X-rays: External Validation of a Deep Learning Algorithm at East Kent Hospitals University NHS Foundation Trust

“…Deep learning systems for analyzing chest X-rays have been developed for automating the detection of radiological signs of tuberculosis [10][11][12], pneumonia [13], COVID-19 [14,15], pneumothorax, and lung nodules [16,17]. The WHO has recently endorsed computer-aided detection (CAD) technologies for tuberculosis (TB) diagnosis in individuals aged 15 and above as a replacement for human interpreters in the assessment of digital chest radiographs for TB screening and triage [18].…”

“…Multiple clinical applications of qXR have undergone evaluation in prior studies, studying its generalizability across various healthcare settings and geographic locations. Globally, qXR has been employed in diagnostic pathways for tuberculosis (TB) [10][11][12]20,21], lung nodules [13,16], and COVID-19 [14], as well as in the classification of CXRs as normal or abnormal [22][23][24]. A study by Govindarajan et al [22] found that qXR improved the diagnostic accuracy and turnaround time for reporting abnormal CXRs in a prospective setting.…”