scholarly journals The Appropriateness Criteria of Abdominal Fat Measurement at the Level of the L1-L2 Intervertebral Disc in Patients With Obesity

2021 ◽  
Vol 12 ◽  
Author(s):  
Jing Sun ◽  
Han Lv ◽  
Meng Zhang ◽  
Mengyi Li ◽  
Lei Zhao ◽  
...  
Keyword(s):  
Adipose Tissue ◽  
Intervertebral Disc ◽  
Intraclass Correlation ◽  
Correlation Coefficients ◽  
Abdominal Fat ◽  
Training Data ◽  
Mr Images ◽  
Appropriateness Criteria ◽  
Lumbar Intervertebral Disc ◽  
Disc Level

BackgroundIn this study, we proposed to use MR images at L1-L2 (lumbar) intervertebral disc level to measure abdominal fat area in patients with obesity. The quantitative results would provide evidence for the individualized assessment of the severity of obesity.MethodsAll patients in the IRB-approved database of Beijing Friendship Hospital who underwent bariatric surgery between November 2017 and November 2019 were recruited. We retrospectively reviewed upper abdominal magnetic resonance (MR) data before surgery. We analyzed the correlation and consistency of the area of abdominal subcutaneous adipose tissue (ASAT) and visceral adipose tissue (VAT) measured at the L1-L2 and L2-L3 levels on MR images. We randomly distributed the cases into prediction model training data and testing data at a ratio of 7:3.ResultsTwo hundred and forty-five subjects were included. The ASAT and VAT results within the L1-L2 and L2-L3 levels were very similar and highly correlated (maleASAT: r=0.98, femaleASAT: r=0.93; maleVAT: r=0.91, femaleVAT: r=0.88). There was no substantial systematic deviation among the results at the two levels, except for the ASAT results in males. The intraclass correlation coefficients (ICCs) were 0.91 and 0.93 for maleASAT and femaleASAT, and 0.88 and 0.87 for maleVAT and femaleVAT, respectively. The ASAT/VAT area at the L2-L3 level was well predicted. The coefficient β of linear regression that predicted L2-L3 ASAT from L1-L2 ASAT was 1.11 for males and 0.99 for females. The R-squares were 0.97 and 0.91, respectively. For VAT prediction, the coefficient β was 1.02 for males and 0.96 for females. The R-squares were 0.82 and 0.77, respectively.ConclusionFor patients with obesity, the L1-L2 intervertebral disc level can be used as the substitution of L2-L3 level in abdominal fat measurement.

Magnetic Resonance Imaging of Cerebral Central Sulci: a Study of Monozygotic Twins

1998 ◽  
Vol 47 (2) ◽  
pp. 89-100 ◽  
Author(s):  
I. Bonan ◽  
A.M. Argenti ◽  
M. Duyme ◽  
D. Hasboun ◽  
A. Dorion ◽  
...  
Keyword(s):  
Intraclass Correlation ◽  
Monozygotic Twins ◽  
Correlation Coefficients ◽  
Mean Value ◽  
Resonance Imaging ◽  
Mr Images ◽  
Central Sulcus ◽  
Intraclass Correlation Coefficients ◽  
Left Right Asymmetry ◽  
The Brain

AbstractThe cerebral central sulci, seat of the sensorimotor cortex, vary anatomically in form, length and depth among individuals and present a left/right asymmetry. The purpose of this work was to measure central sulcus's lengths, at the surface and in-depth, in each hemisphere of monozygotic twins in order to evaluate the influence of environmental factors on the morphometry and asymmetry of this structure. A measurement technique on MR images of the brains using 3 D software was developed. Two operators applied this technique to measure central sulcus lengths at the surface of the brain and in-depth in each hemisphere. Besides the fact that the technique developed gave high Intraclass Correlation Coefficients (ICC) for the surface lengths (mean value 0.94), and slightly less high for the in-depth length (mean value 0.87), we found a weak (from 0.57 to 0.73 for raw data) but significant ICC between homologous sulci in pairs of twins. In addition, the ICC for asymmetry indices were not significant. Hence, if central sulcus morphometry is in part genetically influenced, these results show that nongenetic factors are nonetheless important in their development.

scholarly journals Computer-Aided Cobb Measurement Based on Automatic Detection of Vertebral Slopes Using Deep Neural Network

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Junhua Zhang ◽  
Hongjian Li ◽  
Liang Lv ◽  
Yufeng Zhang
Keyword(s):  
Neural Network ◽  
Cobb Angle ◽  
Deep Neural Network ◽  
Intraclass Correlation ◽  
Correlation Coefficients ◽  
Angle Measurement ◽  
Training Data ◽  
Manual Measurement ◽  
Computer Aided

Objective. To develop a computer-aided method that reduces the variability of Cobb angle measurement for scoliosis assessment. Methods. A deep neural network (DNN) was trained with vertebral patches extracted from spinal model radiographs. The Cobb angle of the spinal curve was calculated automatically from the vertebral slopes predicted by the DNN. Sixty-five in vivo radiographs and 40 model radiographs were analyzed. An experienced surgeon performed manual measurements on the aforementioned radiographs. Two examiners used both the proposed and the manual measurement methods to analyze the aforementioned radiographs. Results. For model radiographs, the intraclass correlation coefficients were greater than 0.98, and the mean absolute differences were less than 3°. This indicates that the proposed system showed high repeatability for measurements of model radiographs. For the in vivo radiographs, the reliabilities were lower than those from the model radiographs, and the differences between the computer-aided measurement and the manual measurement by the surgeon were higher than 5°. Conclusion. The variability of Cobb angle measurements can be reduced if the DNN system is trained with enough vertebral patches. Training data of in vivo radiographs must be included to improve the performance of DNN. Significance. Vertebral slopes can be predicted by DNN. The computer-aided system can be used to perform automatic measurements of Cobb angle, which is used to make reliable and objective assessments of scoliosis.

scholarly journals Efficient segmentation of lumbar intervertebral disc from MR images

2020 ◽  
Vol 14 (13) ◽  
pp. 3076-3083
Author(s):  
Leena Silvoster M ◽  
Retnaswami Mathusoothana S. Kumar
Keyword(s):  
Intervertebral Disc ◽  
Mr Images ◽  
Lumbar Intervertebral Disc

scholarly journals Intervertebral Disc and Adipokine Leptin—Loves Me, Loves Me Not

2020 ◽  
Vol 22 (1) ◽  
pp. 375
Author(s):  
Goran Curic
Keyword(s):  
Adipose Tissue ◽  
Intervertebral Disc ◽  
Nutrient Deficiency ◽  
Protective Role ◽  
Lumbar Intervertebral Disc ◽  
Metabolic Role ◽  
Ivd Degeneration ◽  
Negative Connotation

Leptin—the most famous adipose tissue-secreted hormone—in the human body is mostly observed in a negative connotation, as the hormone level increases with the accumulation of body fat. Nowadays, fatness is becoming another normal body shape. Fatness is burdened with numerous illnesses—including low back pain and degenerative disease of lumbar intervertebral disc (IVD). IVD degeneration and IVD inflammation are two indiscerptible phenomena. Irrespective of the underlying pathophysiological background (trauma, obesity, nutrient deficiency), the inflammation is crucial in triggering IVD degeneration. Leptin is usually depicted as a proinflammatory adipokine. Many studies aimed at explaining the role of leptin in IVD degeneration, though mostly in in vitro and on animal models, confirmed leptin’s “bad reputation”. However, several studies found that leptin might have protective role in IVD metabolism. This review examines the current literature on the metabolic role of different depots of adipose tissue, with focus on leptin, in pathogenesis of IVD degeneration.

scholarly journals Using Deep Learning to Detect Spinal Cord Diseases on Thoracolumbar Magnetic Resonance Images of Dogs

2021 ◽  
Vol 8 ◽  
Author(s):  
Anika Biercher ◽  
Sebastian Meller ◽  
Jakob Wendt ◽  
Norman Caspari ◽  
Johannes Schmidt-Mosig ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Intervertebral Disc ◽  
Magnetic Resonance Images ◽  
Training Data ◽  
Mr Images ◽  
Disc Protrusion ◽  
Fibrocartilaginous Embolism ◽  
T2 Weighted Images

Deep Learning based Convolutional Neural Networks (CNNs) are the state-of-the-art machine learning technique with medical image data. They have the ability to process large amounts of data and learn image features directly from the raw data. Based on their training, these networks are ultimately able to classify unknown data and make predictions. Magnetic resonance imaging (MRI) is the imaging modality of choice for many spinal cord disorders. Proper interpretation requires time and expertise from radiologists, so there is great interest in using artificial intelligence to more quickly interpret and diagnose medical imaging data. In this study, a CNN was trained and tested using thoracolumbar MR images from 500 dogs. T1- and T2-weighted MR images in sagittal and transverse planes were used. The network was trained with unremarkable images as well as with images showing the following spinal cord pathologies: intervertebral disc extrusion (IVDE), intervertebral disc protrusion (IVDP), fibrocartilaginous embolism (FCE)/acute non-compressive nucleus pulposus extrusion (ANNPE), syringomyelia and neoplasia. 2,693 MR images from 375 dogs were used for network training. The network was tested using 7,695 MR images from 125 dogs. The network performed best in detecting IVDPs on sagittal T1-weighted images, with a sensitivity of 100% and specificity of 95.1%. The network also performed very well in detecting IVDEs, especially on sagittal T2-weighted images, with a sensitivity of 90.8% and specificity of 98.98%. The network detected FCEs and ANNPEs with a sensitivity of 62.22% and a specificity of 97.90% on sagittal T2-weighted images and with a sensitivity of 91% and a specificity of 90% on transverse T2-weighted images. In detecting neoplasms and syringomyelia, the CNN did not perform well because of insufficient training data or because the network had problems differentiating different hyperintensities on T2-weighted images and thus made incorrect predictions. This study has shown that it is possible to train a CNN in terms of recognizing and differentiating various spinal cord pathologies on canine MR images. CNNs therefore have great potential to act as a “second eye” for imagers in the future, providing a faster focus on the altered image area and thus increasing workflow in radiology.

scholarly journals Deep learning algorithms to segment and quantify the choroidal thickness and vasculature in swept-source optical coherence tomography images

2020 ◽  
pp. 2140002
Author(s):  
Gu Zheng ◽  
Yanfeng Jiang ◽  
Ce Shi ◽  
Hanpei Miao ◽  
Xiangle Yu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Choroidal Thickness ◽  
Automatic Segmentation ◽  
Learning Algorithms ◽  
Intraclass Correlation ◽  
Correlation Coefficients ◽  
Practical Method ◽  
Training Data ◽  
Validation Data ◽  
Swept Source

Accurate segmentation of choroidal thickness (CT) and vasculature is important to better analyze and understand the choroid-related ocular diseases. In this paper, we proposed and implemented a novel and practical method based on the deep learning algorithms, residual U-Net, to segment and quantify the CT and vasculature automatically. With limited training data and validation data, the residual U-Net was capable of identifying the choroidal boundaries as precise as the manual segmentation compared with an experienced operator. Then, the trained deep learning algorithms was applied to 217 images and six choroidal relevant parameters were extracted, we found high intraclass correlation coefficients (ICC) of more than 0.964 between manual and automatic segmentation methods. The automatic method also achieved great reproducibility with ICC greater than 0.913, indicating good consistency of the automatic segmentation method. Our results suggested the deep learning algorithms can accurately and efficiently segment choroid boundaries, which will be helpful to quantify the CT and vasculature.

Interobserver Reliability Using the Phonetic Level Evaluation With Severely and Profoundly Hearing-Impaired Children

1991 ◽  
Vol 34 (5) ◽  
pp. 989-999 ◽  
Author(s):  
Stephanie Shaw ◽  
Truman E. Coggins
Keyword(s):  
Interrater Reliability ◽  
Interobserver Reliability ◽  
Intraclass Correlation ◽  
Correlation Coefficients ◽  
Hearing Impaired ◽  
Intraclass Correlation Coefficients ◽  
Assessment Measure ◽  
Impaired Children ◽  
Speech Assessment ◽  
Hearing Impaired Children

This study examines whether observers reliably categorize selected speech production behaviors in hearing-impaired children. A group of experienced speech-language pathologists was trained to score the elicited imitations of 5 profoundly and 5 severely hearing-impaired subjects using the Phonetic Level Evaluation (Ling, 1976). Interrater reliability was calculated using intraclass correlation coefficients. Overall, the magnitude of the coefficients was found to be considerably below what would be accepted in published behavioral research. Failure to obtain acceptably high levels of reliability suggests that the Phonetic Level Evaluation may not yet be an accurate and objective speech assessment measure for hearing-impaired children.

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