Highlights
- •A novel CNN model can automatically detect and output the precise anatomical localization and classification of rib fractures.
- •The sensitivity of localization reached 94.87% and 97.11% in the right and left ribs, respectively.
- •The CNN model had a certain robustness verified by the external test, and it matched or surpassed the detection and classification performance of experienced radiologists.
Abstract
Objective
To develop a convolutional neural network (CNN) model for the detection, precise anatomical
localization (right 1-12th and left 1-12th) and classification (fresh, healing and
old fractures) of rib fractures automatically, and to compare the performance with
the experienced radiologists.
Materials and methods
A total of 640 rib fracture patients with 340,501 annotations were retrospectively
collected from three hospitals. They consisted of a classification training dataset
(n = 482), a localization training dataset (n = 30), an internal testing dataset (n = 90)
and an external testing dataset (n = 38). RetinaNet with rib localization postprocessing
and the result merging technique were employed to structure the CNN model. ROC curve,
free-response ROC curve, AUC, precision, recall, and F1-score were calculated to choose
the better option between model I (training classification and localization data together)
and model II (adding an additional classification model to model I).
Results
The detection and classification performance of rib fractures was better in model
II than in model I. The sensitivity of localization reached 97.11% and 94.87% on the
right and left ribs, respectively. In the external dataset with different CT scanner
and slice thickness, model II showed better diagnostic performance. Moreover, the
CNN model was superior in diagnosing fresh and healing fractures to 5 radiologists
and consumed shorter diagnosis time.
Conclusions
Our CNN model was capable of detection, precise anatomical localization, and classification
of rib fractures automatically.
Abbreviations:
CNN (convolutional neural network), DL (deep learning), ROC (receiver operating characteristic), fROC (free-response receiver operating characteristic), AUC (area under the curve), RPN (region proposal network), GT (ground truth)Keywords
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Article info
Publication history
Published online: September 21, 2021
Accepted:
September 13,
2021
Received in revised form:
September 9,
2021
Received:
May 9,
2021
Identification
Copyright
© 2021 Elsevier Inc. All rights reserved.
