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Abstract

Background

A high false-positive rate remains a technical glitch hindering the broad spectrum of application of deep-learning-based diagnostic tools in routine radiological practice from assisting in diagnosing rib fractures.

Purpose

To examine the performance of two versions of deep-learning-based software tools in aiding radiologists in diagnosing rib fractures on chest computed tomography (CT) images.

Material and Methods

In total, 123 patients (708 rib fractures) were included in this retrospective study. Two groups of radiologists with different experience levels retrospectively reviewed images for rib fractures in the concurrent mode aided with RibFrac-High Sensitivity (HS) and RibFrac-High Precision (HP). We compared their diagnostic performance against the reference standard in terms of sensitivity and positive predictive value (PPV).

Results

On a per-patient basis, RibFrac-HS exhibited a higher sensitivity compared with RibFrac-HP (mean difference=0.051, 95% CI=0.012–0.090; P = 0.011), whereas the latter significantly outperformed the former in terms of the PPV (mean difference=0.273, 95% CI=0.238–0.308; P < 0.0001). The use of RibFrac-HP significantly improved the junior and the senior groups’ sensitivities respectively by 0.058 (95% CI=0.033–0.083; P < 0.0001) and 0.058 (95% CI=0.034–0.081; P < 0.0001), and decreased the diagnosis time by 206 s (95% CI=191–220; P < 0.0001) and 79 s (95% CI=67–92; P < 0.0001), respectively, when compared to no software assistance.

Conclusion

The sensitivity and efficiency of radiologists in identifying rib fractures can be improved by using RibFrac-HS and/or RibFrac-HP. With an added module for false-positive suppression, RibFrac-HP maintains the sensitivity and increases the PPV in fracture detection compared to Rib-Frac-HS.

Keywords

Rib fracture artificial intelligence computed tomography false positives computer-aided diagnosis

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Comparison and verification of two deep learning models for the detection of chest CT rib fractures

Abstract

Background

Purpose

Material and Methods

Results

Conclusion

Keywords

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References