An Automated Deep Learning Method for Tile AO/OTA Pelvic Fracture Severity Grading from Trauma whole-Body CT

Dreizin, David; Goldmann, Florian; LeBedis, Christina A.; Boscak, Alexis R.; Dattwyler, Matthew; Bodanapally, Uttam K.; Li, Guang; Anderson, Stephan W.; Maier, Andreas; Unberath, Mathias

doi:10.1007/s10278-020-00399-x

Cited by 22 publications

(16 citation statements)

References 45 publications

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“…In this work, by leveraging the respective strengths of deep learning for feature detection and a BM for causal inference, we achieve automatic and interpretable Tile grading of pelvic fractures from CT images. Using the proposed pipeline, Tile grade prediction had an AUC of 83.3% and 85.1% for translational and rotational instability comparable to the previous black-box method [12] but with added interpretability and potential for human-machine teaming.…”

Section: Discussionmentioning

confidence: 79%

“…We obtained approval for use of the dataset initially described in [12], that consists of 373 admission CT scans of adult patients from two level I trauma centers with bleeding pelvic fractures and varying degrees of instability with IRBapproved waiver of consent. Imaging was performed with intravenous contrast in the arterial phase on 40-, 64-, and dual source 128-row CT scanners and reconstructed with 0.7 -5 mm slice thickness.…”

Section: Dataset and Metricsmentioning

confidence: 99%

“…An objective, automated approach to Tile AO/OTA grading of pelvic fractures could harmonize the accuracy of Tile grading across readers while accelerating the interpretation of whole-body CT to extract pelvic fracture-related risk scores, thus addressing an important unmet clinical need [27]. Previous work using automated deep learning methods to evaluate pelvic fracture severity relied on black-box models that predict severity from trauma imagery [12,21] but do not provide a transparent, interpretable association between Tile AO/OTA grade and detectable fractures. Plain radiographs have been used for pelvic fracture detection [8], but not classification tasks, and are severely hampered by bone and soft tissue overlap.…”

Section: Introductionmentioning

confidence: 99%

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An Interpretable Approach to Automated Severity Scoring in Pelvic Trauma

Zapaishchykova¹,

Dreizin²,

Li³

et al. 2021

Preprint

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Pelvic ring disruptions result from blunt injury mechanisms and are often found in patients with multi-system trauma. To grade pelvic fracture severity in trauma victims based on whole-body CT, the Tile AO/OTA classification is frequently used. Due to the high volume of whole-body trauma CTs generated in busy trauma centers, an automated approach to Tile classification would provide substantial value, e. g., to prioritize the reading queue of the attending trauma radiologist. In such scenario, an automated method should perform grading based on a transparent process and based on interpretable features to enable interaction with human readers and lower their workload by offering insights from a first automated read of the scan. This paper introduces an automated yet interpretable pelvic trauma decision support system to assist radiologists in fracture detection and Tile grade classification. The method operates similarly to human interpretation of CT scans and first detects distinct pelvic fractures on CT with high specificity using a Faster-RCNN model that are then interpreted using a structural causal model based on clinical best practices to infer an initial Tile grade. The Bayesian causal model and finally, the object detector are then queried for likely co-occurring fractures that may have been rejected initially due to the highly specific operating point of the detector, resulting in an updated list of detected fractures and corresponding final Tile grade. Our method is transparent in that it provides finding location and type using the object detector, as well as information on important counterfactuals that would invalidate the system's recommendation and achieves an AUC of 83.3%/85.1% for translational/rotational instability. Despite being designed for human-machine teaming, our approach does not compromise on performance compared to previous black-box approaches.

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Section: Discussionmentioning

confidence: 79%

Section: Dataset and Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Interpretable Approach to Automated Severity Scoring in Pelvic Trauma

Zapaishchykova¹,

Dreizin²,

Li³

et al. 2021

Preprint

Self Cite

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“…It is the harmonic mean of the previous two measures: precision and sensitivity. F1 score [36] is defined as Two other indicators-Matthews correlation coefficient (MCC) [37] and Fowlkes-Mallows index (FMI)-are defined below:…”

Section: Measures and Indicatorsmentioning

confidence: 99%

Secondary Pulmonary Tuberculosis Recognition by 4-Direction Varying-Distance GLCM and Fuzzy SVM

et al. 2022

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Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis . Our study investigates the recognition of secondary pulmonary (SPTB). A novel F3 model is proposed. The first F means using a four-direction varying-distance gray-level co-occurrence matrix (FDVDGLCM) to analyze the chest CT images; the second F means a five-property feature set (FPFS) from the FDVDGLCM results; the third F means fuzzy support vector machine (FSVM). Besides, a slight adaption of multiple-way data augmentation is used to boost the training set. The 10 runs of 10-fold cross-validation demonstrate that this F3 model achieves a sensitivity of 93.68% ± 1.75%, a specificity of 94.17% ± 1.68%, a precision of 94.17% ± 1.55%, an accuracy of 93.92% ± 1.05%, an F1 score of 93.91% ± 1.07%, an MCC of 87.88% ± 2.09%, and an FMI of 93.92% ± 1.06%. The AUC is 0.9624. The FSVM can give better performance than ordinary SVM. The proposed F3 model is superior to six state-of-the-art SPTB recognition models.

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“…The first four measures are sensitivity, specificity, precision and accuracy, common in most pattern recognition papers. The last three measures are F1 score, Matthews correlation coefficient (MCC) [38], and Fowlkes-Mallows index (FMI) [39]. They are defined as:…”

Section: Figure F-fold Cross Validationmentioning

confidence: 99%

Pseudo Zernike Moment and Deep Stacked Sparse Autoencoder for COVID-19 Diagnosis

Zhang¹,

Khan²,

Zhu³

et al. 2021

Computers, Materials &Amp; Continua

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Aim) COVID-19 is an ongoing infectious disease. It has caused more than 107.45 m confirmed cases and 2.35 m deaths till 11/Feb/2021. Traditional computer vision methods have achieved promising results on the automatic smart diagnosis. (Method) This study aims to propose a novel deep learning method that can obtain better performance. We use the pseudo-Zernike moment (PZM), derived from Zernike moment, as the extracted features. Two settings are introducing: (i) image plane over unit circle; and (ii) image plane inside the unit circle. Afterward, we use a deep-stacked sparse autoencoder (DSSAE) as the classifier. Besides, multiple-way data augmentation is chosen to overcome overfitting. The multiple-way data augmentation is based on Gaussian noise, salt-and-pepper noise, speckle noise, horizontal and vertical shear, rotation, Gamma correction, random translation and scaling. (Results) 10 runs of 10-fold cross validation shows that our PZM-DSSAE method achieves a sensitivity of 92.06% ± 1.54%, a specificity of 92.56% ± 1.06%, a precision of 92.53% ± 1.03%, and an accuracy of 92.31% ± 1.08%. Its F1 score, MCC, and FMI arrive at 92.29% ±1.10%, 84.64% ± 2.15%, and 92.29% ± 1.10%, respectively. The AUC of our model is 0.9576. (Conclusion) We demonstrate "image plane over unit circle" can get better results than "image plane inside a unit circle." Besides, this proposed PZM-DSSAE model is better than eight state-of-the-art approaches.

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An Automated Deep Learning Method for Tile AO/OTA Pelvic Fracture Severity Grading from Trauma whole-Body CT

Cited by 22 publications

References 45 publications

An Interpretable Approach to Automated Severity Scoring in Pelvic Trauma

An Interpretable Approach to Automated Severity Scoring in Pelvic Trauma

Secondary Pulmonary Tuberculosis Recognition by 4-Direction Varying-Distance GLCM and Fuzzy SVM

Pseudo Zernike Moment and Deep Stacked Sparse Autoencoder for COVID-19 Diagnosis

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