COVID-19 on Chest Radiographs : A Multireader Evaluation of an Artificial Intelligence System
Background Chest radiography may play an important role in triage for coronavirus disease 2019 (COVID-19), particularly in low-resource settings. Purpose To evaluate the performance of an artificial intelligence (AI) system for detection of COVID-19 pneumonia on chest radiographs. Materials and Methods An AI system (CAD4COVID-XRay) was trained on 24 678 chest radiographs, including 1540 used only for validation while training. The test set consisted of a set of continuously acquired chest radiographs (n = 454) obtained in patients suspected of having COVID-19 pneumonia between March 4 and April 6, 2020, at one center (223 patients with positive reverse transcription polymerase chain reaction [RT-PCR] results, 231 with negative RT-PCR results). Radiographs were independently analyzed by six readers and by the AI system. Diagnostic performance was analyzed with the receiver operating characteristic curve. Results For the test set, the mean age of patients was 67 years ± 14.4 (standard deviation) (56% male). With RT-PCR test results as the reference standard, the AI system correctly classified chest radiographs as COVID-19 pneumonia with an area under the receiver operating characteristic curve of 0.81. The system significantly outperformed each reader (P < .001 using the McNemar test) at their highest possible sensitivities. At their lowest sensitivities, only one reader significantly outperformed the AI system (P = .04). Conclusion The performance of an artificial intelligence system in the detection of coronavirus disease 2019 on chest radiographs was comparable with that of six independent readers. © RSNA, 2020.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2020 |
|---|---|
| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:296 |
|---|---|
| Enthalten in: |
Radiology - 296(2020), 3 vom: 18. Sept., Seite E166-E172 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Murphy, Keelin [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
|---|
| Anmerkungen: |
Date Completed 27.08.2020 Date Revised 12.11.2023 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1148/radiol.2020201874 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM309672732 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM309672732 | ||
| 003 | DE-627 | ||
| 005 | 20231225134430.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231225s2020 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1148/radiol.2020201874 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1032.xml |
| 035 | |a (DE-627)NLM309672732 | ||
| 035 | |a (NLM)32384019 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Murphy, Keelin |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a COVID-19 on Chest Radiographs |b A Multireader Evaluation of an Artificial Intelligence System |
| 264 | 1 | |c 2020 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 27.08.2020 | ||
| 500 | |a Date Revised 12.11.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Background Chest radiography may play an important role in triage for coronavirus disease 2019 (COVID-19), particularly in low-resource settings. Purpose To evaluate the performance of an artificial intelligence (AI) system for detection of COVID-19 pneumonia on chest radiographs. Materials and Methods An AI system (CAD4COVID-XRay) was trained on 24 678 chest radiographs, including 1540 used only for validation while training. The test set consisted of a set of continuously acquired chest radiographs (n = 454) obtained in patients suspected of having COVID-19 pneumonia between March 4 and April 6, 2020, at one center (223 patients with positive reverse transcription polymerase chain reaction [RT-PCR] results, 231 with negative RT-PCR results). Radiographs were independently analyzed by six readers and by the AI system. Diagnostic performance was analyzed with the receiver operating characteristic curve. Results For the test set, the mean age of patients was 67 years ± 14.4 (standard deviation) (56% male). With RT-PCR test results as the reference standard, the AI system correctly classified chest radiographs as COVID-19 pneumonia with an area under the receiver operating characteristic curve of 0.81. The system significantly outperformed each reader (P < .001 using the McNemar test) at their highest possible sensitivities. At their lowest sensitivities, only one reader significantly outperformed the AI system (P = .04). Conclusion The performance of an artificial intelligence system in the detection of coronavirus disease 2019 on chest radiographs was comparable with that of six independent readers. © RSNA, 2020 | ||
| 650 | 4 | |a Journal Article | |
| 700 | 1 | |a Smits, Henk |e verfasserin |4 aut | |
| 700 | 1 | |a Knoops, Arnoud J G |e verfasserin |4 aut | |
| 700 | 1 | |a Korst, Michael B J M |e verfasserin |4 aut | |
| 700 | 1 | |a Samson, Tijs |e verfasserin |4 aut | |
| 700 | 1 | |a Scholten, Ernst T |e verfasserin |4 aut | |
| 700 | 1 | |a Schalekamp, Steven |e verfasserin |4 aut | |
| 700 | 1 | |a Schaefer-Prokop, Cornelia M |e verfasserin |4 aut | |
| 700 | 1 | |a Philipsen, Rick H H M |e verfasserin |4 aut | |
| 700 | 1 | |a Meijers, Annet |e verfasserin |4 aut | |
| 700 | 1 | |a Melendez, Jaime |e verfasserin |4 aut | |
| 700 | 1 | |a van Ginneken, Bram |e verfasserin |4 aut | |
| 700 | 1 | |a Rutten, Matthieu |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Radiology |d 1945 |g 296(2020), 3 vom: 18. Sept., Seite E166-E172 |w (DE-627)NLM000054860 |x 1527-1315 |7 nnns |
| 773 | 1 | 8 | |g volume:296 |g year:2020 |g number:3 |g day:18 |g month:09 |g pages:E166-E172 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1148/radiol.2020201874 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 296 |j 2020 |e 3 |b 18 |c 09 |h E166-E172 | ||