A comparison of CXR-CAD software to radiologists in identifying COVID-19 in individuals evaluated for Sars CoV-2 infection in Malawi and Zambia
-д хадгалсан:
| -д хэвлэсэн: | PLOS Digital Health vol. 4, no. 1 (Jan 2025), p. e0000535 |
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| Үндсэн зохиолч: | |
| Бусад зохиолчид: | , , , , , , , , , , , , |
| Хэвлэсэн: |
Public Library of Science
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| Нөхцлүүд: | |
| Онлайн хандалт: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Шошгууд: |
Шошго байхгүй, Энэхүү баримтыг шошголох эхний хүн болох!
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MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3252725941 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2767-3170 | ||
| 024 | 7 | |a 10.1371/journal.pdig.0000535 |2 doi | |
| 035 | |a 3252725941 | ||
| 045 | 2 | |b d20250101 |b d20250131 | |
| 100 | 1 | |a Linsen, Sam | |
| 245 | 1 | |a A comparison of CXR-CAD software to radiologists in identifying COVID-19 in individuals evaluated for Sars CoV-2 infection in Malawi and Zambia | |
| 260 | |b Public Library of Science |c Jan 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a AI based software, including computer aided detection software for chest radiographs (CXR-CAD), was developed during the pandemic to improve COVID-19 case finding and triage. In high burden TB countries, the use of highly portable CXR and computer aided detection software has been adopted more broadly to improve the screening and triage of individuals for TB, but there is little evidence in these settings regarding COVID-19 CAD performance. We performed a multicenter, retrospective cross-over study evaluating CXRs from individuals at risk for COVID-19. We evaluated performance of CAD software and radiologists in comparison to COVID-19 laboratory results in 671 individuals evaluated for COVID-19 at sites in Zambia and Malawi between January 2021 and June 2022. All CXRs were interpreted by an expert radiologist and two commercially available COVID-19 CXR-CAD software. Radiologists interpreted CXRs for COVID-19 with a sensitivity of 73% (95% CI: 69%- 76%) and specificity of 49% (95% CI: 40%-58%). One CAD software (CAD2) showed performance in diagnosing COVID-19 that was comparable to that of radiologists, (AUC-ROC of 0.70 (95% CI: 0.65–0.75)), while a second (CAD1) showed inferior performance (AUC-ROC of 0.57 (95% CI: 0.52–0.63)). Agreement between CAD software and radiologists was moderate for diagnosing COVID-19, and agreement was very good in differentiating normal and abnormal CXRs in this high prevalent population. The study highlights the potential of CXR-CAD as a tool to support effective triage of individuals in Malawi and Zambia during the pandemic, particularly for distinguishing normal from abnormal CXRs. These findings suggest that while current AI-based diagnostics like CXR-CAD show promise, their effectiveness varies significantly. In order to better prepare for future pandemics, there is a need for representative training data to optimize performance in key populations, and ongoing data collection to maintain diagnostic accuracy, especially as new disease strains emerge. | |
| 651 | 4 | |a Malawi | |
| 651 | 4 | |a Zambia | |
| 653 | |a Pandemics | ||
| 653 | |a Patients | ||
| 653 | |a Software | ||
| 653 | |a Teaching hospitals | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Data collection | ||
| 653 | |a Algorithms | ||
| 653 | |a Privacy | ||
| 653 | |a COVID-19 diagnostic tests | ||
| 653 | |a Medical imaging | ||
| 653 | |a Respiratory diseases | ||
| 653 | |a COVID-19 | ||
| 653 | |a Coronaviruses | ||
| 653 | |a Tuberculosis | ||
| 653 | |a Social | ||
| 700 | 1 | |a Kamoun, Aurélie | |
| 700 | 1 | |a Andrews, Gunda | |
| 700 | 1 | |a Mwenifumbo, Tamara | |
| 700 | 1 | |a Chancy Chavula | |
| 700 | 1 | |a Nchimunya, Lindiwe | |
| 700 | 1 | |a Tsai, Yucheng | |
| 700 | 1 | |a Mulenga, Namwaka | |
| 700 | 1 | |a Godfrey Kadewele | |
| 700 | 1 | |a Kajombo, Eunice Nahache | |
| 700 | 1 | |a Sunkutu, Veronica | |
| 700 | 1 | |a Shawa, Jane | |
| 700 | 1 | |a Kadam, Rigveda | |
| 700 | 1 | |a Arentz, Matthew | |
| 773 | 0 | |t PLOS Digital Health |g vol. 4, no. 1 (Jan 2025), p. e0000535 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3252725941/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3252725941/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3252725941/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |