scholarly journals Deep learning pipeline for automated detection and classification of central canal, lateral recess and neural foraminal stenosis on lumbar spine MRI

2021 ◽  
Vol 1 ◽  
pp. 100240
Author(s):  
Jiong Hao Tan ◽  
Lei Zhu ◽  
Kaiyuan Yang ◽  
Hiroshi Yoshioka ◽  
Beng Chin Ooi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Lumbar Spine ◽  
Central Canal ◽  
Automated Detection ◽  
Foraminal Stenosis ◽  
Lateral Recess ◽  
Spine Mri

Deep Learning Model for Automated Detection and Classification of Central Canal, Lateral Recess, and Neural Foraminal Stenosis at Lumbar Spine MRI

Radiology ◽  
2021 ◽  
pp. 204289
Author(s):  
James Thomas Patrick Decourcy Hallinan ◽  
Lei Zhu ◽  
Kaiyuan Yang ◽  
Andrew Makmur ◽  
Diyaa Abdul Rauf Algazwi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Lumbar Spine ◽  
Learning Model ◽  
Central Canal ◽  
Automated Detection ◽  
Foraminal Stenosis ◽  
Lateral Recess ◽  
Spine Mri ◽  
Deep Learning Model

Simplified Universal Grading of Lumbar Spine MRI Degenerative Findings: Inter-Reader Agreement of Non-Radiologist Spine Experts

Pain Medicine ◽  
2021 ◽  
Author(s):  
Nityanand Miskin ◽  
Zacharia Isaac ◽  
Yi Lu ◽  
Melvin C Makhni ◽  
Danielle L Sarno ◽  
...  
Keyword(s):  
Lumbar Spine ◽  
Spinal Stenosis ◽  
Degenerative Changes ◽  
Grading System ◽  
Foraminal Stenosis ◽  
Substantial Agreement ◽  
Lateral Recess ◽  
Lateral Recess Stenosis ◽  
Facet Arthropathy ◽  
Spine Mri

Abstract Objective 1) To describe a simplified multidisciplinary grading system for the most clinically relevant lumbar spine degenerative changes. 2) To measure the inter-reader variability among non-radiologist spine experts in their use of the classification system for interpretation of a consecutive series of lumbar spine magnetic resonance imaging (MRI) examinations. Methods ATS multidisciplinary and collaborative standardized grading of spinal stenosis, foraminal stenosis, lateral recess stenosis, and facet arthropathy was developed. Our institution’s picture archiving and communication system was searched for 50 consecutive patients who underwent non-contrast MRI of the lumbar spine for chronic back pain, radiculopathy, or symptoms of spinal stenosis. Three fellowship-trained spine subspecialists from neurosurgery, orthopedic surgery, and physiatry interpreted the 50 exams using the classification at the L4–L5 and L5–S1 levels. Inter-reader agreement was assessed with Cohen’s kappa coefficient. Results For spinal stenosis, the readers demonstrated substantial agreement (κ = 0.702). For foraminal stenosis and facet arthropathy, the three readers demonstrated moderate agreement (κ = 0.544, and 0.557, respectively). For lateral recess stenosis, there was fair agreement (κ = 0.323). Conclusions A simplified universal grading system of lumbar spine MRI degenerative findings is newly described. Use of this multidisciplinary grading system in the assessment of clinically relevant degenerative changes revealed moderate to substantial agreement among non-radiologist spine physicians. This standardized grading system could serve as a foundation for interdisciplinary communication.

Automated detection and classification of shoulder arthroplasty models using deep learning

2020 ◽  
Vol 49 (10) ◽  
pp. 1623-1632
Author(s):  
Paul H. Yi ◽  
Tae Kyung Kim ◽  
Jinchi Wei ◽  
Xinning Li ◽  
Gregory D. Hager ◽  
...  
Keyword(s):  
Deep Learning ◽  
Shoulder Arthroplasty ◽  
Automated Detection

scholarly journals Spine MRI Patterns And Predictors of Disability In Patients With Lower Back Pain. A Prospective Cross-Sectional Study at University of Gondar Comprehensive Specialized Hospital, North West Ethiopia. 2020.

2021 ◽  
Author(s):  
Yonathan Gebrewold ◽  
Bati Tesfaye
Keyword(s):  
Back Pain ◽  
Lumbar Spine ◽  
Cross Sectional Study ◽  
Foraminal Stenosis ◽  
Mri Findings ◽  
Disc Disease ◽  
Cross Sectional ◽  
North West ◽  
Degree Of Disability ◽  
Spine Mri

Abstract Background: Low back pain (LBP) has become one of the most serious public health problems with substantial socioeconomic implication. Degenerative disc disease one of the commonest cause of LBP. Magnetic resonance imaging (MRI) is routinely utilized in evaluation patients with degenerative changes of the lumbar spine. However there are contradictors reports with regards to association of MRI findings of lumbar spine and patients’ symptoms.Objective: This study is aimed determine correlation of degree of disability with lumbar spine MRI findings in patients with LBP at University of Gondar comprehensive Specialized Hospital (UoGCSH), North West Ethiopia, 2020.Methods and Materials. A prospective cross-sectional study was conducted on 72 consecutively enrolled patients with lower back pain who underwent lumbar MRI scan. Degree of disability was measured using Oswestry disability Index (ODI) questionnaire translated to local language. Association between lumbar spine MRI parameters and ODI score and category was tested using Spearman’s rank correlation coefficient and Chi square tests. Results The mean age the study subjects was 43.81±1.88 years (range 22-83 years). 59.7% of the study population were Female. In terms of ODI category, most fell under minimal and moderate disability 33 (45.8%) and 25(34.7%) respectively. Disc bulge (81.9%) and foraminal stenosis were the most frequent MRI abnormalities detected. ODI score showed weak correlation with grade of spinal canal stenosis. Foraminal stenosis grade was not correlated ODI.Conclusion: The clinical relevance of MRI findings in patients with degenerative disc disease is limited and MRI should be sparingly ordered in evaluation of these patients particularly in resource constrained settings.

scholarly journals Automated detection and classification of fundus diabetic retinopathy images using synergic deep learning model

2020 ◽  
Vol 133 ◽  
pp. 210-216 ◽  
Author(s):  
K. Shankar ◽  
Abdul Rahaman Wahab Sait ◽  
Deepak Gupta ◽  
S.K. Lakshmanaprabu ◽  
Ashish Khanna ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Learning Model ◽  
Automated Detection ◽  
Deep Learning Model

scholarly journals Automated detection and classification of the proximal humerus fracture by using deep learning algorithm

2018 ◽  
Vol 89 (4) ◽  
pp. 468-473 ◽  
Author(s):  
Seok Won Chung ◽  
Seung Seog Han ◽  
Ji Whan Lee ◽  
Kyung-Soo Oh ◽  
Na Ra Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Proximal Humerus Fracture ◽  
Proximal Humerus ◽  
Learning Algorithm ◽  
Automated Detection ◽  
Humerus Fracture ◽  
Deep Learning Algorithm
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