Automated abnormality detection in lower extremity radiographs using deep learning

Varma, Maya; Lu, Mandy; Gardner, Rachel; Dunnmon, Jared; Khandwala, Nishith; Rajpurkar, Pranav; Jin, Long; Beaulieu, Christopher; Shpanskaya, Katie; Li, Feifei; Lungren, Matthew P.; Patel, Bhavik N.

doi:10.1038/s42256-019-0126-0

Cited by 66 publications

(40 citation statements)

References 25 publications

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“…Convolutional Neural Networks (CNN) have been the core technique to detect and localise disease manifestations and differentiate anomalies. These methods have been successfully employed for several radiographic applications [10][11][12]. These models utilise larger training samples to achieve better performance.…”

Section: Introductionmentioning

confidence: 99%

Differentiation of COVID-19 conditions in planar chest radiographs using optimized convolutional neural networks

Govindarajan

Swaminathan

2020

Appl Intell

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In this study, an attempt has been made to differentiate Novel Coronavirus-2019 (COVID-19) conditions from healthy subjects in Chest radiographs using a simplified end-to-end Convolutional Neural Network (CNN) model and occlusion sensitivity maps. Early detection and faster automated screening of the COVID-19 patients is essential. For this, the images are considered from publicly available datasets. Significant biomarkers representing critical image features are extracted from CNN by experimentally investigating on cross-validation methods and hyperparameter settings. The performance of the network is evaluated using standard metrics. Perturbation based occlusion sensitivity maps are employed on the features obtained from the classification model to visualise the localization of abnormal areas. Results demonstrate that the simplified CNN model with optimised parameters is able to extract significant features with a sensitivity of 97.35% and F-measure of 96.71% to detect COVID-19 images. The algorithm achieves an Area Under the Curve-Receiver Operating Characteristic score of 99.4% with Matthews correlation coefficient of 0.93. High value of Diagnostic odds ratio is also obtained. Occlusion sensitivity maps provide precise localization of abnormal regions by identifying COVID-19 conditions. As early detection through chest radiographic images are useful for automated screening of the disease, this method appears to be clinically relevant in providing a visual diagnostic solution using a simplified and efficient model.

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Section: Introductionmentioning

confidence: 99%

Differentiation of COVID-19 conditions in planar chest radiographs using optimized convolutional neural networks

Govindarajan

Swaminathan

2020

Appl Intell

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“…A variety of use cases for AI to aid in the interpretation of MSK radiographs have been proposed, ranging from automating measurements such as the femoral neckshaft angle or Insall-Salvati ratio, detecting fractures, assessing skeletal maturity (bone age), and providing a probability of a bone lesion being benign or malignant. 37 Determining bone age, detecting fractures, grading osteoarthritis, and automating measurements are some of the most prominent use cases explored in the literature 6,[38][39][40][41][42][43] and for which AI solutions have recently been commercialized.…”

Section: What Ai Applications For Msk Radiograph Analysis Are Currently Available and On The Horizon?mentioning

confidence: 99%

“…There have also been recent promising studies describing AI for fracture detection. 6,41,42,49 Lindsey et al 6 used a data set of 135 845 MSK radiographs from across anatomic regions with groundtruth defined by one or more orthopedic surgeons, and achieved an AUROC of 0.967. In a study which assessed how the algorithm may change fracture detection performance among emergency physicians interpreting posterior-anterior and lateral-view wrist radiographs, a statistically significant improvement in both sensitivity and specificity for detection of fractures was observed when emergency medicine physicians were aided by the algorithm compared to when they were unaided.…”

Section: What Ai Applications For Msk Radiograph Analysis Are Currently Available and On The Horizon?mentioning

confidence: 99%

Artificial Intelligence Solutions for Analysis of X-ray Images

et al. 2020

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Artificial intelligence (AI) presents a key opportunity for radiologists to improve quality of care and enhance the value of radiology in patient care and population health. The potential opportunity of AI to aid in triage and interpretation of conventional radiographs (X-ray images) is particularly significant, as radiographs are the most common imaging examinations performed in most radiology departments. Substantial progress has been made in the past few years in the development of AI algorithms for analysis of chest and musculoskeletal (MSK) radiographs, with deep learning now the dominant approach for image analysis. Large public and proprietary image data sets have been compiled and have aided the development of AI algorithms for analysis of radiographs, many of which demonstrate accuracy equivalent to radiologists for specific, focused tasks. This article describes (1) the basis for the development of AI solutions for radiograph analysis, (2) current AI solutions to aid in the triage and interpretation of chest radiographs and MSK radiographs, (3) opportunities for AI to aid in noninterpretive tasks related to radiographs, and (4) considerations for radiology practices selecting AI solutions for radiograph analysis and integrating them into existing IT systems. Although comprehensive AI solutions across modalities have yet to be developed, institutions can begin to select and integrate focused solutions which increase efficiency, increase quality and patient safety, and add value for their patients.

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“…The CNNs architectures have an input layer and an output layer, and there are also many convolutional layers, pooling layers, rectified linear unit layers, dense layers and dropout layers [ 17 , 18 ]. The CNN shows huge success in the analysis of radiography X-rays in the knee osteoarthritis automatically, as there is no need of image pre-processing [ 19 , 20 ]. However, X-rays have not been able to improve upon three classes of knee ACL detection, as compared to MR images.…”

Section: Introductionmentioning

confidence: 99%

Efficient Detection of Knee Anterior Cruciate Ligament from Magnetic Resonance Imaging Using Deep Learning Approach

et al. 2021

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The most commonly injured ligament in the human body is an anterior cruciate ligament (ACL). ACL injury is standard among the football, basketball and soccer players. The study aims to detect anterior cruciate ligament injury in an early stage via efficient and thorough automatic magnetic resonance imaging without involving radiologists, through a deep learning method. The proposed approach in this paper used a customized 14 layers ResNet-14 architecture of convolutional neural network (CNN) with six different directions by using class balancing and data augmentation. The performance was evaluated using accuracy, sensitivity, specificity, precision and F1 score of our customized ResNet-14 deep learning architecture with hybrid class balancing and real-time data augmentation after 5-fold cross-validation, with results of 0.920%, 0.916%, 0.946%, 0.916% and 0.923%, respectively. For our proposed ResNet-14 CNN the average area under curves (AUCs) for healthy tear, partial tear and fully ruptured tear had results of 0.980%, 0.970%, and 0.999%, respectively. The proposing diagnostic results indicated that our model could be used to detect automatically and evaluate ACL injuries in athletes using the proposed deep-learning approach.

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Automated abnormality detection in lower extremity radiographs using deep learning

Cited by 66 publications

References 25 publications

Differentiation of COVID-19 conditions in planar chest radiographs using optimized convolutional neural networks

Differentiation of COVID-19 conditions in planar chest radiographs using optimized convolutional neural networks

Artificial Intelligence Solutions for Analysis of X-ray Images

Efficient Detection of Knee Anterior Cruciate Ligament from Magnetic Resonance Imaging Using Deep Learning Approach

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