396 research outputs found
Precise Proximal Femur Fracture Classification for Interactive Training and Surgical Planning
We demonstrate the feasibility of a fully automatic computer-aided diagnosis
(CAD) tool, based on deep learning, that localizes and classifies proximal
femur fractures on X-ray images according to the AO classification. The
proposed framework aims to improve patient treatment planning and provide
support for the training of trauma surgeon residents. A database of 1347
clinical radiographic studies was collected. Radiologists and trauma surgeons
annotated all fractures with bounding boxes, and provided a classification
according to the AO standard. The proposed CAD tool for the classification of
radiographs into types "A", "B" and "not-fractured", reaches a F1-score of 87%
and AUC of 0.95, when classifying fractures versus not-fractured cases it
improves up to 94% and 0.98. Prior localization of the fracture results in an
improvement with respect to full image classification. 100% of the predicted
centers of the region of interest are contained in the manually provided
bounding boxes. The system retrieves on average 9 relevant images (from the
same class) out of 10 cases. Our CAD scheme localizes, detects and further
classifies proximal femur fractures achieving results comparable to
expert-level and state-of-the-art performance. Our auxiliary localization model
was highly accurate predicting the region of interest in the radiograph. We
further investigated several strategies of verification for its adoption into
the daily clinical routine. A sensitivity analysis of the size of the ROI and
image retrieval as a clinical use case were presented.Comment: Accepted at IPCAI 2020 and IJCAR
Automatic Hip Fracture Identification and Functional Subclassification with Deep Learning
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: fellowship-trained 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.Comment: Presented at Orthopaedic Research Society, Austin, TX, Feb 2, 2019,
currently in submission for publicatio
Detecting mechanical loosening of total hip replacement implant from plain radiograph using deep convolutional neural network
Plain radiography is widely used to detect mechanical loosening of total hip
replacement (THR) implants. Currently, radiographs are assessed manually by
medical professionals, which may be prone to poor inter and intra observer
reliability and low accuracy. Furthermore, manual detection of mechanical
loosening of THR implants requires experienced clinicians who might not always
be readily available, potentially resulting in delayed diagnosis. In this
study, we present a novel, fully automatic and interpretable approach to detect
mechanical loosening of THR implants from plain radiographs using deep
convolutional neural network (CNN). We trained a CNN on 40 patients
anteroposterior hip x rays using five fold cross validation and compared its
performance with a high volume board certified orthopaedic surgeon (AFC). To
increase the confidence in the machine outcome, we also implemented saliency
maps to visualize where the CNN looked at to make a diagnosis. CNN outperformed
the orthopaedic surgeon in diagnosing mechanical loosening of THR implants
achieving significantly higher sensitively (0.94) than the orthopaedic surgeon
(0.53) with the same specificity (0.96). The saliency maps showed that the CNN
looked at clinically relevant features to make a diagnosis. Such CNNs can be
used for automatic radiologic assessment of mechanical loosening of THR
implants to supplement the practitioners decision making process, increasing
their diagnostic accuracy, and freeing them to engage in more patient centric
care
Deep learning for accurately recognizing common causes of shoulder pain on radiographs
Objective: Training a convolutional neural network (CNN) to detect the most common causes of shoulder pain on plain radiographs and to assess its potential value in serving as an assistive device to physicians.
Materials and methods: We used a CNN of the ResNet-50 architecture which was trained on 2700 shoulder radiographs from clinical practice of multiple institutions. All radiographs were reviewed and labeled for six findings: proximal humeral fractures, joint dislocation, periarticular calcification, osteoarthritis, osteosynthesis, and joint endoprosthesis. The trained model was then evaluated on a separate test dataset, which was previously annotated by three independent expert radiologists. Both the training and the test datasets included radiographs of highly variable image quality to reflect the clinical situation and to foster robustness of the CNN. Performance of the model was evaluated using receiver operating characteristic (ROC) curves, the thereof derived AUC as well as sensitivity and specificity.
Results: The developed CNN demonstrated a high accuracy with an area under the curve (AUC) of 0.871 for detecting fractures, 0.896 for joint dislocation, 0.945 for osteoarthritis, and 0.800 for periarticular calcifications. It also detected osteosynthesis and endoprosthesis with near perfect accuracy (AUC 0.998 and 1.0, respectively). Sensitivity and specificity were 0.75 and 0.86 for fractures, 0.95 and 0.65 for joint dislocation, 0.90 and 0.86 for osteoarthrosis, and 0.60 and 0.89 for calcification.
Conclusion: CNNs have the potential to serve as an assistive device by providing clinicians a means to prioritize worklists or providing additional safety in situations of increased workload
X-Ray Bone Fracture Classification Using Deep Learning: A Baseline for Designing a Reliable Approach
Computer-Aided Diagnosis System for Bone Fracture Detection and Classification: A Review on Deep Learning Techniques
Bone fracture detection and classification was a large discussed topic over the last
few years and many researchers proposed different technological solutions to tackle
this task. Despite this, a universal approach able to support the classification of
fractures in the human body still does not exist today. We aim to provide a first
discussion concerning a selection of research works done in the technological domain,
with a specific focus on Deep Learning. The objective was to underline a picture on
the most promising studies for stimulating a knowledge improvement in the specific
focus of bone fracture classification, necessary to start the development of an optimal
shared framework. The evaluation has been made involving a first qualitative
assessment based on strengths and weaknesses, providing a usage scenario evaluation.
This could support the development of a helpful Computer Aided Diagnosis (CAD)
system able to drive doctors in diagnosis tasks reducing diagnosis time, especially in
the most complex tasks, and supporting the reduction of wrong diagnosis issues,
especially during stressful working conditions, as what frequently happens in many
emergency departments
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