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Artificial intelligence helpful in stratifying COVID-19 risk based on chest x-rays: Study

MedhaBy MedhaPublished On 2022-01-22T09:00:47+05:30  |  Updated On 2022-01-22T11:20:06+05:30
Artificial intelligence helpful in stratifying COVID-19 risk based on chest x-rays: Study

USA: A modified commercially available deep learning algorithm (M-qXR) could be utilized in low-resource settings to risk-stratify patients with suspected COVID-19 infections by detecting abnormalities on chest X-rays, shows a recent study. The study was published in the journal Intelligence-Based Medicine on January 13, 2022. According to the study, the artificial intelligence...

USA: A modified commercially available deep learning algorithm (M-qXR) could be utilized in low-resource settings to risk-stratify patients with suspected COVID-19 infections by detecting abnormalities on chest X-rays, shows a recent study. The study was published in the journal Intelligence-Based Medicine on January 13, 2022. 

According to the study, the artificial intelligence (AI) algorithm had comparable accuracy to ground truth for detecting radiographic abnormalities on CXR suggestive of COVID-19 and thus could serve as a radiology decision tool to guide management of patients who are deemed at risk for COVID-19. 

Previous studies have shown that in resource-limited settings, deep learning-based radiological image analysis could facilitate the use of chest x-rays as a triaging tool for COVID-19 diagnosis. Diego A. Hipolito Canario, UNC School of Medicine, the University of North Carolina at Chapel Hill, Chapel Hill, NC, United States, and colleagues thus aimed to determine whether a modified commercially available deep learning algorithm (M-qXR) could risk-stratify patients with suspected COVID-19 infections. 

For this purpose, the researchers designed a dual-track clinical validation study to assess the clinical accuracy of M-qXR. All Chest-X-rays (CXRs) performed during the study period were evaluated for abnormal findings and assigned a COVID-19 risk score. Four independent radiologists served as radiological ground truth. 

The researchers then compared M-qXR algorithm output against radiological ground truth and calculated summary statistics for prediction accuracy. In a co-occurrence matrix, patients who underwent both PCR testing and CXR for suspected COVID-19 infection were included to assess the sensitivity and specificity of the M-qXR algorithm. 

The study revealed the following findings:

  • 625 CXRs were included in the clinical validation study. 98% of total interpretations made by M-qXR agreed with ground truth.
  • 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.
  • 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.

To conclude, M-qXR was shown to have comparable accuracy to radiological ground truth in detecting radiographic abnormalities on CXR suggestive of COVID-19.

Reference:

The study titled, "Using artificial intelligence to risk stratify COVID-19 patients based on chest X-ray findings," was published in the journal Intelligence-Based Medicine. 

DOI: https://doi.org/10.1016/j.ibmed.2022.100049

artificial intelligence COVID 19 Intelligence-Based Medicine 
Source : Intelligence-Based Medicine
Medha
Medha

    Medha Baranwal joined Medical Dialogues as an Editor in 2018 for Speciality Medical Dialogues. She covers several medical specialties including Cardiac Sciences, Dentistry, Diabetes and Endo, Diagnostics, ENT, Gastroenterology, Neurosciences, and Radiology. She has completed her Bachelors in Biomedical Sciences from DU and then pursued Masters in Biotechnology from Amity University. She has a working experience of 5 years in the field of medical research writing, scientific writing, content writing, and content management. She can be contacted at  editorial@medicaldialogues.in. Contact no. 011-43720751

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