Deep learning for pneumothorax diagnosis: a systematic review and meta-analysis
Background Deep learning (DL), a subset of artificial intelligence (AI), has been applied to pneumothorax diagnosis to aid physician diagnosis, but no meta-analysis has been performed. Methods A search of multiple electronic databases through September 2022 was performed to identify studies that app...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
European Respiratory Society
2023-06-01
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| Series: | European Respiratory Review |
| Online Access: | http://err.ersjournals.com/content/32/168/220259.full |
| Summary: | Background
Deep learning (DL), a subset of artificial intelligence (AI), has been applied to pneumothorax diagnosis to aid physician diagnosis, but no meta-analysis has been performed.
Methods
A search of multiple electronic databases through September 2022 was performed to identify studies that applied DL for pneumothorax diagnosis using imaging. Meta-analysis via a hierarchical model to calculate the summary area under the curve (AUC) and pooled sensitivity and specificity for both DL and physicians was performed. Risk of bias was assessed using a modified Prediction Model Study Risk of Bias Assessment Tool.
Results
In 56 of the 63 primary studies, pneumothorax was identified from chest radiography. The total AUC was 0.97 (95% CI 0.96–0.98) for both DL and physicians. The total pooled sensitivity was 84% (95% CI 79–89%) for DL and 85% (95% CI 73–92%) for physicians and the pooled specificity was 96% (95% CI 94–98%) for DL and 98% (95% CI 95–99%) for physicians. More than half of the original studies (57%) had a high risk of bias.
Conclusions
Our review found the diagnostic performance of DL models was similar to that of physicians, although the majority of studies had a high risk of bias. Further pneumothorax AI research is needed. |
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| ISSN: | 0905-9180 1600-0617 |