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 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 Performance of a deep learning CNN model for the automated detection of 13 common conditions on Chest X-rays

2021 ◽  
Author(s):  
Tirupathi Karthik ◽  
Vijayalakshmi Kasiraman ◽  
Bhavani Paski ◽  
Kashyap Gurram ◽  
Amit Talwar ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Delayed Diagnosis ◽  
Cost Effective ◽  
Radiology Workflow ◽  
X Rays ◽  
Term Care ◽  
X Ray ◽  
Intelligence System ◽  
Deep Learning Algorithm

Background and aims: Chest X-rays are widely used, non-invasive, cost effective imaging tests. However, the complexity of interpretation and global shortage of radiologists have led to reporting backlogs, delayed diagnosis and a compromised quality of care. A fully automated, reliable artificial intelligence system that can quickly triage abnormal images for urgent radiologist review would be invaluable in the clinical setting. The aim was to develop and validate a deep learning Convoluted Neural Network algorithm to automate the detection of 13 common abnormalities found on Chest X-rays. Method: In this retrospective study, a VGG 16 deep learning model was trained on images from the Chest-ray 14, a large publicly available Chest X-ray dataset, containing over 112,120 images with annotations. Images were split into training, validation and testing sets and trained to identify 13 specific abnormalities. The primary performance measures were accuracy and precision. Results: The model demonstrated an overall accuracy of 88% in the identification of abnormal X-rays and 87% in the detection of 13 common chest conditions with no model bias. Conclusion: This study demonstrates that a well-trained deep learning algorithm can accurately identify multiple abnormalities on X-ray images. As such models get further refined, they can be used to ease radiology workflow bottlenecks and improve reporting efficiency. Napier Healthcare’s team that developed this model consists of medical IT professionals who specialize in AI and its practical application in acute & long-term care settings. This is currently being piloted in a few hospitals and diagnostic labs on a commercial basis.

scholarly journals TRACKING AND PREDICTING COVID-19 RADIOLOGICAL TRAJECTORY USING DEEP LEARNING ON CHEST X-RAYS: INITIAL ACCURACY TESTING

2020 ◽  
Author(s):  
S. Duchesne ◽  
D. Gourdeau ◽  
P. Archambault ◽  
C. Chartrand-Lefebvre ◽  
L. Dieumegarde ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Roc Curves ◽  
Quantitative Information ◽  
Lung Lesion ◽  
Imaging Features ◽  
X Rays ◽  
Disease Trajectory ◽  
Deep Learning Algorithm ◽  
Deep Learning Features

ABSTRACTBackgroundDecision scores and ethically mindful algorithms are being established to adjudicate mechanical ventilation in the context of potential resources shortage due to the current onslaught of COVID-19 cases. There is a need for a reproducible and objective method to provide quantitative information for those scores.PurposeTowards this goal, we present a retrospective study testing the ability of a deep learning algorithm at extracting features from chest x-rays (CXR) to track and predict radiological evolution.Materials and MethodsWe trained a repurposed deep learning algorithm on the CheXnet open dataset (224,316 chest X-ray images of 65,240 unique patients) to extract features that mapped to radiological labels. We collected CXRs of COVID-19-positive patients from two open-source datasets (last accessed on April 9, 2020)(Italian Society for Medical and Interventional Radiology and MILA). Data collected form 60 pairs of sequential CXRs from 40 COVID patients (mean age ± standard deviation: 56 ± 13 years; 23 men, 10 women, seven not reported) and were categorized in three categories: “Worse”, “Stable”, or “Improved” on the basis of radiological evolution ascertained from images and reports. Receiver operating characteristic analyses, Mann-Whitney tests were performed.ResultsOn patients from the CheXnet dataset, the area under ROC curves ranged from 0.71 to 0.93 for seven imaging features and one diagnosis. Deep learning features between “Worse” and “Improved” outcome categories were significantly different for three radiological signs and one diagnostic (“Consolidation”, “Lung Lesion”, “Pleural effusion” and “Pneumonia”; all P < 0.05). Features from the first CXR of each pair could correctly predict the outcome category between “Worse” and “Improved” cases with 82.7% accuracy.ConclusionCXR deep learning features show promise for classifying the disease trajectory. Once validated in studies incorporating clinical data and with larger sample sizes, this information may be considered to inform triage decisions.

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.

scholarly journals Explainable COVID-19 Detection on Chest X-rays Using an End-to-End Deep Convolutional Neural Network Architecture

2021 ◽  
Vol 5 (4) ◽  
pp. 73
Author(s):  
Mohamed Chetoui ◽  
Moulay A. Akhloufi ◽  
Bardia Yousefi ◽  
El Mostafa Bouattane
Keyword(s):  
Deep Learning ◽  
Medical Imaging ◽  
Network Architecture ◽  
Learning Algorithm ◽  
Binary Classification ◽  
X Rays ◽  
Deep Learning Algorithm ◽  
Binary Model ◽  
Chest X Ray ◽  
Multi Class Classification

The coronavirus pandemic is spreading around the world. Medical imaging modalities such as radiography play an important role in the fight against COVID-19. Deep learning (DL) techniques have been able to improve medical imaging tools and help radiologists to make clinical decisions for the diagnosis, monitoring and prognosis of different diseases. Computer-Aided Diagnostic (CAD) systems can improve work efficiency by precisely delineating infections in chest X-ray (CXR) images, thus facilitating subsequent quantification. CAD can also help automate the scanning process and reshape the workflow with minimal patient contact, providing the best protection for imaging technicians. The objective of this study is to develop a deep learning algorithm to detect COVID-19, pneumonia and normal cases on CXR images. We propose two classifications problems, (i) a binary classification to classify COVID-19 and normal cases and (ii) a multiclass classification for COVID-19, pneumonia and normal. Nine datasets and more than 3200 COVID-19 CXR images are used to assess the efficiency of the proposed technique. The model is trained on a subset of the National Institute of Health (NIH) dataset using swish activation, thus improving the training accuracy to detect COVID-19 and other pneumonia. The models are tested on eight merged datasets and on individual test sets in order to confirm the degree of generalization of the proposed algorithms. An explainability algorithm is also developed to visually show the location of the lung-infected areas detected by the model. Moreover, we provide a detailed analysis of the misclassified images. The obtained results achieve high performances with an Area Under Curve (AUC) of 0.97 for multi-class classification (COVID-19 vs. other pneumonia vs. normal) and 0.98 for the binary model (COVID-19 vs. normal). The average sensitivity and specificity are 0.97 and 0.98, respectively. The sensitivity of the COVID-19 class achieves 0.99. The results outperformed the comparable state-of-the-art models for the detection of COVID-19 on CXR images. The explainability model shows that our model is able to efficiently identify the signs of COVID-19.

COVID-DETECT: A DEEP LEARNING APPROACH FOR CLASSIFICATION OF COVID-19 PNEUMONIA FROM LUNG SEGMENTED CHEST X-RAYS

2020 ◽  
pp. 2150010
Author(s):  
S. Rajkumar ◽  
P. V. Rajaraman ◽  
Haree Shankar Meganathan ◽  
V. Sapthagirivasan ◽  
K. Tejaswinee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Rapid Screening ◽  
X Rays ◽  
X Ray ◽  
Deep Learning Algorithm ◽  
X Ray Imaging ◽  
Before And After ◽  
Wide Availability ◽  
Novel Coronavirus

The novel coronavirus (COVID-19) was first reported in the Wuhan City of China in 2019 and became a pandemic. The outbreak has caused shocking effects to the people across the globe. It is important to screen a majority of the population in every country and for the respective governments to take appropriate action. There is a need for a rapid screening system to triage and recommend the patients for appropriate treatment. Chest X-ray imaging is one of the potential modalities, which has ample advantages such as wide availability even in the villages, portability, fast data sharing option from the point of capturing to the point of investigation, etc. The aim of the proposed work is to develop a deep learning algorithm for screening COVID-19 cases by leveraging the widely available X-ray imaging. We have built a deep learning Convolutional Neural Network model utilizing a combination of the public domain (open-source COVID-19) and private data (pneumonia and normal cases). The dataset was used before and after the segmentation of the lung region for training and testing. The outcome of the classification after lung segmentation resulted in significant superiority. The average accuracy achieved by the proposed system was 96%. The heat maps incorporated in the system were helpful for our radiologists to cross-verify whether the appropriate features are identified. This system (COVID-Detect) can be used in remote places in the countries affected by COVID-19 for mass screening of suspected cases and suggesting appropriate actions, such as recommending confirmatory tests.

scholarly journals Diagnosing heart failure from chest X-ray images using deep learning

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
T Matsumoto ◽  
S Kodera ◽  
H Shinohara ◽  
A Kiyosue ◽  
Y Higashikuni ◽  
...  
Keyword(s):  
Heart Failure ◽  
Deep Learning ◽  
Data Augmentation ◽  
Learning Algorithm ◽  
Funding Source ◽  
X Rays ◽  
X Ray ◽  
Cardiothoracic Ratio ◽  
Prediction Probability ◽  
Chest X Ray

Abstract   The development of deep learning technology has enabled machines to achieve high-level accuracy in interpreting medical images. While many previous studies have examined the detection of pulmonary nodules and cardiomegaly in chest X-rays using deep learning, the application of this technology to heart failure remains rare. In this study, we investigated the performance of a deep learning algorithm in terms of diagnosing heart failure using images obtained from chest X-rays. We used 952 chest X-ray images from a labeled database published by the National Institutes of Health. Two cardiologists respectively verified and relabeled these images, for a total of 260 “normal” and 378 “heart failure” images, and the remainder were discarded because they had been incorrectly labeled. In this study “heart failure” was defined as “cardiomegaly or congestion”, in a chest X-ray with cardiothoracic ratio (CTR) over 50% or radiographic presence of pulmonary edema. To enable the machine to extract a sufficient number of features from the images, we used the general machine learning approach called data augmentation and transfer learning. Owing mostly to this technique and the adequate relabeling process, we established a model to detect heart failure in chest X-ray by applying deep learning, and obtained an accuracy of 82%. Sensitivity and specificity to heart failure were 75% and 94.4%, respectively. Furthermore, heatmap imaging allowed us to visualize decisions made by the machine. The figure shows randomly selected examples of the prediction probabilities and heatmaps of the chest X-rays from the dataset. The original image is on the left and its heatmap is on the right, with its prediction probability written below. The red areas on the heatmaps show important regions, according to which the machine determined the classification. While some images with ambiguous radiolucency such as (e) and (f) were prone to be misdiagnosed by this model, most of the images like (a)–(d) were diagnosed correctly. Deep learning can thus help support the diagnosis of heart failure using chest X-ray images. Heatmaps and probabilities of prediction Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): JSPS KAKENHI

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