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http://doi.org/10.25358/openscience-7651Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Jungmann, Florian | - |
| dc.contributor.author | Müller, Lukas | - |
| dc.contributor.author | Hahn, Felix | - |
| dc.contributor.author | Weustenfeld, Maximilian | - |
| dc.contributor.author | Dapper, Ann-Kathrin | - |
| dc.contributor.author | Mähringer-Kunz, Aline | - |
| dc.contributor.author | Graafen, Dirk | - |
| dc.contributor.author | Düber, Christoph | - |
| dc.contributor.author | Schafigh, Darius | - |
| dc.contributor.author | Pinto dos Santos, Daniel | - |
| dc.contributor.author | Mildenberger, Peter | - |
| dc.contributor.author | Kloeckner, Roman | - |
| dc.date.accessioned | 2022-09-01T08:30:43Z | - |
| dc.date.available | 2022-09-01T08:30:43Z | - |
| dc.date.issued | 2022 | - |
| dc.identifier.uri | https://openscience.ub.uni-mainz.de/handle/20.500.12030/7665 | - |
| dc.description.abstract | Objectives In response to the COVID-19 pandemic, many researchers have developed artificial intelligence (AI) tools to differentiate COVID-19 pneumonia from other conditions in chest CT. However, in many cases, performance has not been clinically validated. The aim of this study was to evaluate the performance of commercial AI solutions in differentiating COVID-19 pneumonia from other lung conditions. Methods Four commercial AI solutions were evaluated on a dual-center clinical dataset consisting of 500 CT studies; COVID-19 pneumonia was microbiologically proven in 50 of these. Sensitivity, specificity, positive and negative predictive values, and AUC were calculated. In a subgroup analysis, the performance of the AI solutions in differentiating COVID-19 pneumonia from other conditions was evaluated in CT studies with ground-glass opacities (GGOs). Results Sensitivity and specificity ranges were 62–96% and 31–80%, respectively. Negative and positive predictive values ranged between 82–99% and 19–25%, respectively. AUC was in the range 0.54–0.79. In CT studies with GGO, sensitivity remained unchanged. However, specificity was lower, and ranged between 15 and 53%. AUC for studies with GGO was in the range 0.54–0.69. Conclusions This study highlights the variable specificity and low positive predictive value of AI solutions in diagnosing COVID-19 pneumonia in chest CT. However, one solution yielded acceptable values for sensitivity. Thus, with further improvement, commercial AI solutions currently under development have the potential to be integrated as alert tools in clinical routine workflow. Randomized trials are needed to assess the true benefits and also potential harms of the use of AI in image analysis. Key Points • Commercial AI solutions achieved a sensitivity and specificity ranging from 62 to 96% and from 31 to 80%, respectively, in identifying patients suspicious for COVID-19 in a clinical dataset. • Sensitivity remained within the same range, while specificity was even lower in subgroup analysis of CT studies with ground-glass opacities, and interrater agreement between the commercial AI solutions was minimal to nonexistent. • Thus, commercial AI solutions have the potential to be integrated as alert tools for the detection of patients with lung changes suspicious for COVID-19 pneumonia in a clinical routine workflow, if further improvement is made. | en_GB |
| dc.language.iso | eng | de |
| dc.rights | CC BY | * |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | * |
| dc.subject.ddc | 610 Medizin | de_DE |
| dc.subject.ddc | 610 Medical sciences | en_GB |
| dc.title | Commercial AI solutions in detecting COVID-19 pneumonia in chest CT : not yet ready for clinical implementation? | en_GB |
| dc.type | Zeitschriftenaufsatz | de |
| dc.identifier.doi | http://doi.org/10.25358/openscience-7651 | - |
| jgu.type.dinitype | article | en_GB |
| jgu.type.version | Published version | de |
| jgu.type.resource | Text | de |
| jgu.organisation.department | FB 04 Medizin | de |
| jgu.organisation.number | 2700 | - |
| jgu.organisation.name | Johannes Gutenberg-Universität Mainz | - |
| jgu.rights.accessrights | openAccess | - |
| jgu.journal.title | European radiology | de |
| jgu.journal.volume | 32 | de |
| jgu.pages.start | 3152 | de |
| jgu.pages.end | 3160 | de |
| jgu.publisher.year | 2022 | - |
| jgu.publisher.name | Springer | de |
| jgu.publisher.issn | 1432-1084 | de |
| jgu.organisation.place | Mainz | - |
| jgu.subject.ddccode | 610 | de |
| jgu.publisher.doi | 10.1007/s00330-021-08409-4 | de |
| jgu.organisation.ror | https://ror.org/023b0x485 | - |
| Appears in collections: | JGU-Publikationen | |
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| File | Description | Size | Format | ||
|---|---|---|---|---|---|
 | commercial_ai_solutions_in_de-20220829141846580.pdf | 1.6 MB | Adobe PDF | View/Open |