scholarly journals Automated measurement of hip–knee–ankle angle on the unilateral lower limb X-rays using deep learning

2020 ◽  
Author(s):  
Yun Pei ◽  
Wenzhuo Yang ◽  
Shangqing Wei ◽  
Rui Cai ◽  
Jialin Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Lower Limb ◽  
X Rays ◽  
Automated Measurement ◽  
Ankle Angle ◽  
Hip Knee Ankle Angle

3D Image Volumes From 2D Digitally Reconstructed X‐Rays: A Deep Learning Approach In Lower Limb CT‐Scans

2021 ◽  
Author(s):  
Diogo F. Almeida ◽  
Patricio Astudillo ◽  
Dirk Vandermeulen
Keyword(s):  
Deep Learning ◽  
Lower Limb ◽  
Ct Scans ◽  
Learning Approach ◽  
X Rays ◽  
3D Image

scholarly journals Comparison of manual versus automated measurement of Cobb angle in idiopathic scoliosis based on a deep learning keypoint detection technology

2021 ◽  
Author(s):  
Yu Sun ◽  
Yaozhong Xing ◽  
Zian Zhao ◽  
Xianglong Meng ◽  
Gang Xu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Idiopathic Scoliosis ◽  
Cobb Angle ◽  
Correlation Coefficient ◽  
X Rays ◽  
Automated Measurement ◽  
Keypoint Detection ◽  
Automated Method ◽  
Detection Technology ◽  
Short Period

Abstract Purpose The present study compared manual and automated measurement of Cobb angle in idiopathic scoliosis based on deep learning keypoint detection technology. Methods A total of 181 anterior–posterior spinal X-rays were included in this study, including 165 cases of idiopathic scoliosis and 16 normal adult cases without scoliosis. We labeled all images and randomly chose 145 as the training set and 36 as the test set. Two state-of-the-art deep learning object detection models based on convolutional neural networks were used in sequence to segment each vertebra and locate the vertebral corners. Cobb angles measured from the output of the models were compared to manual measurements performed by orthopedic experts. Results The mean Cobb angle in test cases was 27.4° ± 19.2° (range 0.00–91.00°) with manual measurements and 26.4° ± 18.9° (range 0.00–88.00°) with automated measurements. The automated method needed 4.45 s on average to measure each radiograph. The intra-class correlation coefficient (ICC) for the reliability of the automated measurement of the Cobb angle was 0.994. The Pearson correlation coefficient and mean absolute error between automated positioning and expert annotation were 0.990 and 2.2° ± 2.0°, respectively. The analytical result for the Spearman rank-order correlation was 0.984 (p < 0.001). Conclusion The automated measurement results agreed with the experts’ annotation and had a high degree of reliability when the Cobb angle did not exceed 90° and could locate multiple curves in the same scoliosis case simultaneously in a short period of time. Our results need to be verified in more cases in the future.

scholarly journals Can deep learning classify stroke subtypes from chest X-rays?

EBioMedicine ◽  
2021 ◽  
Vol 70 ◽  
pp. 103517
Author(s):  
Vineet K. Raghu ◽  
Michael T. Lu
Keyword(s):  
Deep Learning ◽  
X Rays ◽  
Stroke Subtypes

scholarly journals Accessory pathway analysis using a multimodal deep learning model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Makoto Nishimori ◽  
Kunihiko Kiuchi ◽  
Kunihiro Nishimura ◽  
Kengo Kusano ◽  
Akihiro Yoshida ◽  
...  
Keyword(s):  
Deep Learning ◽  
Electrophysiological Study ◽  
Accessory Pathway ◽  
Learning Model ◽  
X Rays ◽  
X Ray ◽  
Wpw Syndrome ◽  
Chest X Ray ◽  
Deep Learning Model ◽  
Ecg Data

AbstractCardiac accessory pathways (APs) in Wolff–Parkinson–White (WPW) syndrome are conventionally diagnosed with decision tree algorithms; however, there are problems with clinical usage. We assessed the efficacy of the artificial intelligence model using electrocardiography (ECG) and chest X-rays to identify the location of APs. We retrospectively used ECG and chest X-rays to analyse 206 patients with WPW syndrome. Each AP location was defined by an electrophysiological study and divided into four classifications. We developed a deep learning model to classify AP locations and compared the accuracy with that of conventional algorithms. Moreover, 1519 chest X-ray samples from other datasets were used for prior learning, and the combined chest X-ray image and ECG data were put into the previous model to evaluate whether the accuracy improved. The convolutional neural network (CNN) model using ECG data was significantly more accurate than the conventional tree algorithm. In the multimodal model, which implemented input from the combined ECG and chest X-ray data, the accuracy was significantly improved. Deep learning with a combination of ECG and chest X-ray data could effectively identify the AP location, which may be a novel deep learning model for a multimodal model.

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 Detecting the Absence of Lung Sliding in Lung Ultrasounds Using Deep Learning

2021 ◽  
Vol 11 (15) ◽  
pp. 6976
Author(s):  
Miroslav Jaščur ◽  
Marek Bundzel ◽  
Marek Malík ◽  
Anton Dzian ◽  
Norbert Ferenčík ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Lung Ultrasound ◽  
Video Recording ◽  
X Rays ◽  
Surgery Complications ◽  
Temporal Dimension ◽  
Sliding Motion ◽  
Post Surgery ◽  
Lung Sliding

Certain post-thoracic surgery complications are monitored in a standard manner using methods that employ ionising radiation. A need to automatise the diagnostic procedure has now arisen following the clinical trial of a novel lung ultrasound examination procedure that can replace X-rays. Deep learning was used as a powerful tool for lung ultrasound analysis. We present a novel deep-learning method, automated M-mode classification, to detect the absence of lung sliding motion in lung ultrasound. Automated M-mode classification leverages semantic segmentation to select 2D slices across the temporal dimension of the video recording. These 2D slices are the input for a convolutional neural network, and the output of the neural network indicates the presence or absence of lung sliding in the given time slot. We aggregate the partial predictions over the entire video recording to determine whether the subject has developed post-surgery complications. With a 64-frame version of this architecture, we detected lung sliding on average with a balanced accuracy of 89%, sensitivity of 82%, and specificity of 92%. Automated M-mode classification is suitable for lung sliding detection from clinical lung ultrasound videos. Furthermore, in lung ultrasound videos, we recommend using time windows between 0.53 and 2.13 s for the classification of lung sliding motion followed by aggregation.

Computer Animation of Gait

1978 ◽  
Vol 7 (2) ◽  
pp. 111-113 ◽  
Author(s):  
D. R. Broome
Keyword(s):  
Computer Graphics ◽  
Computer Animation ◽  
Fourier Coefficients ◽  
Two Dimensional ◽  
Ankle Angle ◽  
Hip Knee Ankle Angle ◽  
Displacement Information ◽  
Leg Movements

A program has been written which enables two dimensional visualization of leg movements on a computer graphics display. Hip, knee, ankle angle, and pelvic displacement information can be input and processed to obtain Fourier coefficients characterizing these motions. The thigh, shank and foot are then displayed at, for example, two hundred points within each walking cycle, at natural speed or as slowly as desired.

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