Binomial Classification of Pediatric Elbow Fractures Using a Deep                     Learning Multiview Approach Emulating Radiologist Decision                     Making

Rayan, Jesse C.; Reddy, Neelima G.; Kan, J. Herman; Zhang, Wei; Annapragada, Ananth

doi:10.1148/ryai.2019180015

Cited by 75 publications

(56 citation statements)

References 18 publications

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“…In the field of radiology, different deep learning applications have employed many different image resolutions that can be compared with our nine selected image resolutions. For example, 3D U-Net liver volumetry has used 128 3 128 3 128 arrays (17), while pediatric elbow fraction classification has used 500 3 500-pixel inputs (18). Subtle musculoskeletal fraction detection is another case potentially similar to our pulmonary nodule label here, where increased image input resolution benefits performance owing to the size of the finding itself.…”

Section: Discussionmentioning

confidence: 99%

The Effect of Image Resolution on Deep Learning in Radiography

Sabottke

Spieler

2020

Radiology: Artificial Intelligence

210

109

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To examine variations of convolutional neural network (CNN) performance for multiple chest radiograph diagnoses and image resolutions. Materials and Methods: This retrospective study examined CNN performance using the publicly available National Institutes of Health chest radiograph dataset comprising 112 120 chest radiographic images from 30 805 patients. The network architectures examined included ResNet34 and DenseNet121. Image resolutions ranging from 32 3 32 to 600 3 600 pixels were investigated. Network training paradigms used 80% of samples for training and 20% for validation. CNN performance was evaluated based on area under the receiver operating characteristic curve (AUC) and label accuracy. Binary output networks were trained separately for each label or diagnosis under consideration. Results: Maximum AUCs were achieved at image resolutions between 256 3 256 and 448 3 448 pixels for binary decision networks targeting emphysema, cardiomegaly, hernias, edema, effusions, atelectasis, masses, and nodules. When comparing performance between networks that utilize lower resolution (64 3 64 pixels) versus higher (320 3 320 pixels) resolution inputs, emphysema, cardiomegaly, hernia, and pulmonary nodule detection had the highest fractional improvements in AUC at higher image resolutions. Specifically, pulmonary nodule detection had an AUC performance ratio of 80.7% 6 1.5 (standard deviation) (0.689 of 0.854) whereas thoracic mass detection had an AUC ratio of 86.7% 6 1.2 (0.767 of 0.886) for these image resolutions. Conclusion: Increasing image resolution for CNN training often has a trade-off with the maximum possible batch size, yet optimal selection of image resolution has the potential for further increasing neural network performance for various radiology-based machine learning tasks. Furthermore, identifying diagnosis-specific tasks that require relatively higher image resolution can potentially provide insight into the relative difficulty of identifying different radiology findings.

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Section: Discussionmentioning

confidence: 99%

The Effect of Image Resolution on Deep Learning in Radiography

Sabottke

Spieler

2020

Radiology: Artificial Intelligence

210

109

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“…For example, apparently subtle femoral neck fractures are often best seen on the lateral image, which was not included in our model. Rayan et al recently demonstrated excellent results from a novel system that used a CNN as a feature-extractor for images in a given radiographic study and then fed this output into a recurrent neural network to generate study-level predictions for pediatric elbow fractures (29). Such a system may help to improve our model's performance and represents an exciting area of research.…”

Section: Limitationsmentioning

confidence: 99%

Automatic Hip Fracture Identification and Functional Subclassification with Deep Learning

Krogue

Cheng

Hwang³

et al. 2020

Radiology: Artificial Intelligence

124

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Purpose: Hip fractures are a common cause of morbidity and mortality. Automatic identification and classification of hip fractures using deep learning may improve outcomes by reducing diagnostic errors and decreasing time to operation. Methods: Hip and pelvic radiographs from 1118 studies were reviewed and 3034 hips were labeled via bounding boxes and classified as normal, displaced femoral neck fracture, nondisplaced femoral neck fracture, intertrochanteric fracture, previous ORIF, or previous arthroplasty. A deep learning-based object detection model was trained to automate the placement of the bounding boxes. A Densely Connected Convolutional Neural Network (DenseNet) was trained on a subset of the bounding box images, and its performance evaluated on a held out test set and by comparison on a 100-image subset to two groups of human observers: fellowshiptrained radiologists and orthopaedists, and senior residents in emergency medicine, radiology, and orthopaedics. Results: The binary accuracy for fracture of our model was 93.8% (95% CI, 91.3-95.8%), with sensitivity of 92.7% (95% CI, 88.7-95.6%), and specificity 95.0% (95% CI, 91.5-97.3%). Multiclass classification accuracy was 90.4% (95% CI, 87.4-92.9%). When compared to human observers, our model achieved at least expert-level classification under all conditions. Additionally, when the model was used as an aid, human performance improved, with aided resident performance approximating unaided fellowship-trained expert performance. Conclusions: Our deep learning model identified and classified hip fractures with at least expert-level accuracy, and when used as an aid improved human performance, with aided resident performance approximating that of unaided fellowship-trained attendings.

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“…In this study, we implemented DL method to automatically detect the design of THR implants from plain film AP radiographs. Other studies have applied DL methods on plain film radiographs for various orthopedic applications [7][8][9][10][11][12][13][14][15][16] . However, to the best of our knowledge, our pilot study identifying three types of THR femoral implant designs 18 , and this study identifying nine types of THR femoral implant designs are the first applications of DL method to automatically detect THR femoral implant designs.…”

Section: Discussionmentioning

confidence: 99%

“…We hypothesized that deep learning (DL) based artificial intelligence algorithms could be trained to automatically identify hip implant designs from radiographic images. In recent years, DL methods have been applied to the interpretation of plain film radiographs with high degrees of success for identification and classification of orthopaedic fractures, staging knee osteoarthritis (OA) severity, and detection of aseptic loosening, to name a few [7][8][9][10][11][12][13][14][15][16][17] . In a previous pilot study, we successfully trained a DL method for the first time to classify a given THR radiograph into one of three possible femoral component designs 18 .…”

Section: Introductionmentioning

confidence: 99%

Comparing performance of deep convolutional neural network with orthopaedic surgeons on identification of total hip prosthesis design from plain radiographs

Borjali

Chen

Bedair

et al. 2020

Preprint

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A crucial step in preoperative planning for a revision total hip replacement (THR) surgery is accurate identification of failed implant design, especially if one or more well-fixed/functioning components are to be retained. Manual identification of the implant design from preoperative radiographic images can be time-consuming and inaccurate, which can ultimately lead to increased operating room time, more complex surgery, and increased healthcare costs. No automated system has been developed to accurately and efficiently identify THR implant designs. In this study, we present a novel, fully automatic and interpretable approach to identify the design of nine different THR femoral implants from plain radiographs using deep convolutional neural network (CNN). We also compared the CNN performance with three board-certified and fellowship trained orthopaedic surgeons. The CNN achieved on-par accuracy with the orthopaedic surgeons while being significantly faster. The need for additional training data for less distinct designs was also highlighted. Such CNN can be used to automatically identify the design of a failed THR femoral implant preoperatively in just a fraction of a second, saving time and improving identification accuracy.

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Binomial Classification of Pediatric Elbow Fractures Using a Deep Learning Multiview Approach Emulating Radiologist Decision Making

Cited by 75 publications

References 18 publications

The Effect of Image Resolution on Deep Learning in Radiography

The Effect of Image Resolution on Deep Learning in Radiography

Automatic Hip Fracture Identification and Functional Subclassification with Deep Learning

Comparing performance of deep convolutional neural network with orthopaedic surgeons on identification of total hip prosthesis design from plain radiographs

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