Data Enhancement for Deep Learning-Based Wrist Fracture Detection

2021 ◽  
pp. 1182-1193
Author(s):  
Weijie Huang ◽  
Fuqiang Sun ◽  
Menghua Zhang ◽  
Yongfeng Zhang ◽  
Changhui Ma
Keyword(s):  
Deep Learning ◽  
Wrist Fracture ◽  
Fracture Detection

scholarly journals How Can a Deep Learning Algorithm Improve Fracture Detection on X-rays in the Emergency Room?

2021 ◽  
Vol 7 (7) ◽  
pp. 105
Author(s):  
Guillaume Reichert ◽  
Ali Bellamine ◽  
Matthieu Fontaine ◽  
Beatrice Naipeanu ◽  
Adrien Altar ◽  
...  
Keyword(s):  
Deep Learning ◽  
Emergency Room ◽  
Learning Algorithm ◽  
Traumatic Injury ◽  
Diagnostic Tools ◽  
Appendicular Skeleton ◽  
Trauma Patients ◽  
X Rays ◽  
Fracture Detection ◽  
Local Data

The growing need for emergency imaging has greatly increased the number of conventional X-rays, particularly for traumatic injury. Deep learning (DL) algorithms could improve fracture screening by radiologists and emergency room (ER) physicians. We used an algorithm developed for the detection of appendicular skeleton fractures and evaluated its performance for detecting traumatic fractures on conventional X-rays in the ER, without the need for training on local data. This algorithm was tested on all patients (N = 125) consulting at the Louis Mourier ER in May 2019 for limb trauma. Patients were selected by two emergency physicians from the clinical database used in the ER. Their X-rays were exported and analyzed by a radiologist. The prediction made by the algorithm and the annotation made by the radiologist were compared. For the 125 patients included, 25 patients with a fracture were identified by the clinicians, 24 of whom were identified by the algorithm (sensitivity of 96%). The algorithm incorrectly predicted a fracture in 14 of the 100 patients without fractures (specificity of 86%). The negative predictive value was 98.85%. This study shows that DL algorithms are potentially valuable diagnostic tools for detecting fractures in the ER and could be used in the training of junior radiologists.

scholarly journals Rib fracture detection system based on deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Liding Yao ◽  
Xiaojun Guan ◽  
Xiaowei Song ◽  
Yanbin Tan ◽  
Chun Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Detection System ◽  
Rib Fracture ◽  
Diagnostic Efficiency ◽  
Fracture Detection ◽  
Deep Learning Algorithm ◽  
Experienced Radiologist ◽  
Comparable Performance ◽  
Detection And Diagnosis

AbstractRib fracture detection is time-consuming and demanding work for radiologists. This study aimed to introduce a novel rib fracture detection system based on deep learning which can help radiologists to diagnose rib fractures in chest computer tomography (CT) images conveniently and accurately. A total of 1707 patients were included in this study from a single center. We developed a novel rib fracture detection system on chest CT using a three-step algorithm. According to the examination time, 1507, 100 and 100 patients were allocated to the training set, the validation set and the testing set, respectively. Free Response ROC analysis was performed to evaluate the sensitivity and false positivity of the deep learning algorithm. Precision, recall, F1-score, negative predictive value (NPV) and detection and diagnosis were selected as evaluation metrics to compare the diagnostic efficiency of this system with radiologists. The radiologist-only study was used as a benchmark and the radiologist-model collaboration study was evaluated to assess the model’s clinical applicability. A total of 50,170,399 blocks (fracture blocks, 91,574; normal blocks, 50,078,825) were labelled for training. The F1-score of the Rib Fracture Detection System was 0.890 and the precision, recall and NPV values were 0.869, 0.913 and 0.969, respectively. By interacting with this detection system, the F1-score of the junior and the experienced radiologists had improved from 0.796 to 0.925 and 0.889 to 0.970, respectively; the recall scores had increased from 0.693 to 0.920 and 0.853 to 0.972, respectively. On average, the diagnosis time of radiologist assisted with this detection system was reduced by 65.3 s. The constructed Rib Fracture Detection System has a comparable performance with the experienced radiologist and is readily available to automatically detect rib fracture in the clinical setting with high efficacy, which could reduce diagnosis time and radiologists’ workload in the clinical practice.

scholarly journals Advanced Deep Learning Techniques Applied to Automated Femoral Neck Fracture Detection and Classification

2020 ◽  
Vol 33 (5) ◽  
pp. 1209-1217
Author(s):  
Simukayi Mutasa ◽  
Sowmya Varada ◽  
Akshay Goel ◽  
Tony T. Wong ◽  
Michael J. Rasiej
Keyword(s):  
Deep Learning ◽  
Femoral Neck ◽  
Femoral Neck Fracture ◽  
Fracture Detection ◽  
Learning Techniques ◽  
Neck Fracture

Deep learning-based visual ensemble method for high-speed railway catenary clevis fracture detection

2020 ◽  
Vol 396 ◽  
pp. 556-568 ◽  
Author(s):  
Ye Han ◽  
Zhigang Liu ◽  
Yang Lyu ◽  
Kai Liu ◽  
Changjiang Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Speed ◽  
Ensemble Method ◽  
Fracture Detection ◽  
High Speed Railway

scholarly journals A Human-Algorithm Integration System for Hip Fracture Detection on Plain Radiography: System Development and Validation Study (Preprint)

2020 ◽  
Author(s):  
Chi-Tung Cheng ◽  
Chih-Chi Chen ◽  
Fu-Jen Cheng ◽  
Huan-Wu Chen ◽  
Yi-Siang Su ◽  
...  
Keyword(s):  
Deep Learning ◽  
Hip Fracture ◽  
Emergency Departments ◽  
Diagnostic Performance ◽  
Learning Algorithm ◽  
Fracture Detection ◽  
Testing Dataset ◽  
Fracture Diagnosis ◽  
Sensitivity Specificity ◽  
Independent Testing Dataset

BACKGROUND Hip fracture is the most common type of fracture in elderly individuals. Numerous deep learning (DL) algorithms for plain pelvic radiographs (PXRs) have been applied to improve the accuracy of hip fracture diagnosis. However, their efficacy is still undetermined. OBJECTIVE The objective of this study is to develop and validate a human-algorithm integration (HAI) system to improve the accuracy of hip fracture diagnosis in a real clinical environment. METHODS The HAI system with hip fracture detection ability was developed using a deep learning algorithm trained on trauma registry data and 3605 PXRs from August 2008 to December 2016. To compare their diagnostic performance before and after HAI system assistance using an independent testing dataset, 34 physicians were recruited. We analyzed the physicians’ accuracy, sensitivity, specificity, and agreement with the algorithm; we also performed subgroup analyses according to physician specialty and experience. Furthermore, we applied the HAI system in the emergency departments of different hospitals to validate its value in the real world. RESULTS With the support of the algorithm, which achieved 91% accuracy, the diagnostic performance of physicians was significantly improved in the independent testing dataset, as was revealed by the sensitivity (physician alone, median 95%; HAI, median 99%; <i>P</i>&lt;.001), specificity (physician alone, median 90%; HAI, median 95%; <i>P</i>&lt;.001), accuracy (physician alone, median 90%; HAI, median 96%; <i>P</i>&lt;.001), and human-algorithm agreement [physician alone κ, median 0.69 (IQR 0.63-0.74); HAI κ, median 0.80 (IQR 0.76-0.82); <i>P</i>&lt;.001. With the help of the HAI system, the primary physicians showed significant improvement in their diagnostic performance to levels comparable to those of consulting physicians, and both the experienced and less-experienced physicians benefited from the HAI system. After the HAI system had been applied in 3 departments for 5 months, 587 images were examined. The sensitivity, specificity, and accuracy of the HAI system for detecting hip fractures were 97%, 95.7%, and 96.08%, respectively. CONCLUSIONS HAI currently impacts health care, and integrating this technology into emergency departments is feasible. The developed HAI system can enhance physicians’ hip fracture diagnostic performance.

scholarly journals ARTIFICIAL INTELLIGENCE ACCURATELY DETECTS TRAUMATIC THORACOLUMBAR FRACTURES ON SAGITTAL RADIOGRAPHS

2021 ◽  
Author(s):  
Guillermo F Sanchez Rosenberg ◽  
Andrea Cina ◽  
Giuseppe Rosario Schiro ◽  
Pietro Domenico Giorgi ◽  
Boyko Gueorguiev ◽  
...  
Keyword(s):  
Deep Learning ◽  
Vertebral Fractures ◽  
Fracture Zone ◽  
Imaging Modality ◽  
Financial Burden ◽  
Correct Identification ◽  
Clinical Implementation ◽  
Fracture Detection ◽  
Emergency Rooms ◽  
Mri Scans

Background context Traumatic thoracolumbar (TL) fractures are frequently encountered in emergency rooms. Sagittal and anteroposterior radiographs are the first step in the trauma routine imaging. Up to 30% of TL fractures are missed in this imaging modality, thus requiring a CT and/or MRI to confirm the diagnosis. A delay in treatment leads to increased morbidity, mortality, exposure to ionizing radiation and financial burden. Fracture detection with Machine Learning models has achieved expert level performance in previous studies. Reliably detecting vertebral fractures in simple radiographic projections would have a significant clinical and financial impact. Purpose To develop a deep learning model that detects traumatic fractures on sagittal radiographs of the TL spine. Study design/setting Retrospective Cohort study. Methods We collected sagittal radiographs, CT and MRI scans of the TL spine of 362 patients exhibiting traumatic vertebral fractures. Cases were excluded when CT and/or MRI where not available. The reference standard was set by an expert group of three spine surgeons who conjointly annotated the sagittal radiographs of 171 cases. CT and/or MRI were reviewed to confirm the presence and type of the fracture in all cases. 302 cropped vertebral images were labelled -fracture- and 328 -no fracture-. After augmentation, this dataset was then used to train, validate, and test deep learning classifiers based on ResNet18 and VGG16 architectures. To ensure that the prediction of the model was based on the correct identification of the fracture zone, an Activation Map analysis was conducted. Results Vertebras T12 to L2 were the most frequently involved, accounting for 48% of the fractures. A4, A3 and A1 were the most frequent AO Spine fracture types. Accuracies of 88% and 84% were obtained with ResNet18 and VGG16 respectively. The sensitivity was 89% with both architectures but ResNet18 showed a higher specificity (88%) compared to VGG16 (79%). The fracture zone was precisely identified in 81% of the heatmaps. Conclusions Our AI model can accurately identify anomalies suggestive of vertebral fractures in sagittal radiographs by precisely identifying the fracture zone within the vertebral body. Clinical significance Clinical implementation of a diagnosis aid tool specifically trained for TL fracture identification is anticipated to reduce the rate of missed vertebral fractures in emergency rooms.

scholarly journals A Human-Algorithm Integration System for Hip Fracture Detection on Plain Radiography: System Development and Validation Study

10.2196/19416 ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. e19416
Author(s):  
Chi-Tung Cheng ◽  
Chih-Chi Chen ◽  
Fu-Jen Cheng ◽  
Huan-Wu Chen ◽  
Yi-Siang Su ◽  
...  
Keyword(s):  
Deep Learning ◽  
Hip Fracture ◽  
Emergency Departments ◽  
Diagnostic Performance ◽  
Learning Algorithm ◽  
Fracture Detection ◽  
Testing Dataset ◽  
Fracture Diagnosis ◽  
Sensitivity Specificity ◽  
Independent Testing Dataset

Background Hip fracture is the most common type of fracture in elderly individuals. Numerous deep learning (DL) algorithms for plain pelvic radiographs (PXRs) have been applied to improve the accuracy of hip fracture diagnosis. However, their efficacy is still undetermined. Objective The objective of this study is to develop and validate a human-algorithm integration (HAI) system to improve the accuracy of hip fracture diagnosis in a real clinical environment. Methods The HAI system with hip fracture detection ability was developed using a deep learning algorithm trained on trauma registry data and 3605 PXRs from August 2008 to December 2016. To compare their diagnostic performance before and after HAI system assistance using an independent testing dataset, 34 physicians were recruited. We analyzed the physicians’ accuracy, sensitivity, specificity, and agreement with the algorithm; we also performed subgroup analyses according to physician specialty and experience. Furthermore, we applied the HAI system in the emergency departments of different hospitals to validate its value in the real world. Results With the support of the algorithm, which achieved 91% accuracy, the diagnostic performance of physicians was significantly improved in the independent testing dataset, as was revealed by the sensitivity (physician alone, median 95%; HAI, median 99%; P<.001), specificity (physician alone, median 90%; HAI, median 95%; P<.001), accuracy (physician alone, median 90%; HAI, median 96%; P<.001), and human-algorithm agreement [physician alone κ, median 0.69 (IQR 0.63-0.74); HAI κ, median 0.80 (IQR 0.76-0.82); P<.001. With the help of the HAI system, the primary physicians showed significant improvement in their diagnostic performance to levels comparable to those of consulting physicians, and both the experienced and less-experienced physicians benefited from the HAI system. After the HAI system had been applied in 3 departments for 5 months, 587 images were examined. The sensitivity, specificity, and accuracy of the HAI system for detecting hip fractures were 97%, 95.7%, and 96.08%, respectively. Conclusions HAI currently impacts health care, and integrating this technology into emergency departments is feasible. The developed HAI system can enhance physicians’ hip fracture diagnostic performance.

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