Deep learning system for Meyerding classification and segmental motion measurement in diagnosis of lumbar spondylolisthesis

Nguyen, Thong; Chae, Dong-Sik; Park, Sung-Jun; Kang, Kyung-Yil; Yoon, Jonghun

doi:10.1016/j.bspc.2020.102371

Cited by 12 publications

(11 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is particularly true for medical imaging. Deep learning has demonstrated remarkable progress in the analysis of medical imaging across a range of modalities including radiographs, computed tomographic (CT) scans, and magnetic resonance imaging (MRI) scans [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] . There is a growing body of evidence showing clinical utility for deep learning in musculoskeletal radiography (Table I), as evidenced by studies that use deep learning to achieve an expert or near-expert level of performance for the identification and localization of fractures on radiographs (Table II) 31,[36][37][38][39][40][41][42][43] .…”

Section: Continuedmentioning

confidence: 99%

Deep Learning and Imaging for the Orthopaedic Surgeon

Hill

Krogue

Jevsevar

et al. 2022

Journal of Bone and Joint Surgery

View full text Add to dashboard Cite

ä In the not-so-distant future, orthopaedic surgeons will be exposed to machines that begin to automatically "read" medical imaging studies using a technology called deep learning.ä Deep learning has demonstrated remarkable progress in the analysis of medical imaging across a range of modalities that are commonly used in orthopaedics, including radiographs, computed tomographic scans, and magnetic resonance imaging scans.ä There is a growing body of evidence showing clinical utility for deep learning in musculoskeletal radiography, as evidenced by studies that use deep learning to achieve an expert or near-expert level of performance for the identification and localization of fractures on radiographs.ä Deep learning is currently in the very early stages of entering the clinical setting, involving validation and proof-ofconcept studies for automated medical image interpretation.ä The success of deep learning in the analysis of medical imaging has been propelling the field forward so rapidly that now is the time for surgeons to pause and understand how this technology works at a conceptual level, before (not after) the technology ends up in front of us and our patients. That is the purpose of this article.Disclosure: The Disclosure of Potential Conflicts of Interest forms are provided with the online version of the article (http://links.lww.com/JBJS/H59).

show abstract

Section: Continuedmentioning

confidence: 99%

Deep Learning and Imaging for the Orthopaedic Surgeon

Hill

Krogue

Jevsevar

et al. 2022

Journal of Bone and Joint Surgery

View full text Add to dashboard Cite

show abstract

“…Therefore, we used a decentralized CNN model [17][18][19] previously proposed as a highaccuracy landmark detecting method for medical images. The advantage of this method is that it narrows the ROI after each order, which not only reduces the number of non-related features that can affect the results, but also increases the diversity of the training dataset.…”

Section: Decentralized Cnn To Evaluate Anterior Incisor Inclinationmentioning

confidence: 99%

Automated analysis of three-dimensional CBCT images taken in natural head position that combines facial profile processing and multiple deep-learning models

Ahn

Nguyen

Kim

et al. 2022

Computer Methods and Programs in Biomedicine

Self Cite

View full text Add to dashboard Cite

“…This methodology of processing X-ray images to extract disease features and using various classical (i.e., non-deep) machine learning algorithms (e.g., multilayer perceptron) and processing techniques (e.g., clustering) was taken by several related works [ 1 , 12 , 25 , 26 ]. However, such explicit extraction of measurements and features may complicate usability and can be error prone [ 27 ]. Liao et al [ 28 ] proposed automatic spondylolisthesis measurement using CT images as input.…”

Section: Introductionmentioning

confidence: 99%

“…Liao et al [ 28 ] proposed automatic spondylolisthesis measurement using CT images as input. The idea of such approaches is that computerized methods can achieve better accuracy in detecting vertebra edges, features, keypoints, or segmental motion angles [ 27 , 29 ] in a manner that spondylolisthesis can be accurately determined/graded. This literature suffers from the same aforementioned shortcomings it terms of accuracy, explicit processing, or multiple stages of diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Using deep transfer learning to detect scoliosis and spondylolisthesis from x-ray images

et al. 2022

View full text Add to dashboard Cite

Recent years have witnessed wider prevalence of vertebral column pathologies due to lifestyle changes, sedentary behaviors, or injuries. Spondylolisthesis and scoliosis are two of the most common ailments with an incidence of 5% and 3% in the United States population, respectively. Both of these abnormalities can affect children at a young age and, if left untreated, can progress into severe pain. Moreover, severe scoliosis can even lead to lung and heart problems. Thus, early diagnosis can make it easier to apply remedies/interventions and prevent further disease progression. Current diagnosis methods are based on visual inspection by physicians of radiographs and/or calculation of certain angles (e.g., Cobb angle). Traditional artificial intelligence-based diagnosis systems utilized these parameters to perform automated classification, which enabled fast and easy diagnosis supporting tools. However, they still require the specialists to perform error-prone tedious measurements. To this end, automated measurement tools were proposed based on processing techniques of X-ray images. In this paper, we utilize advances in deep transfer learning to diagnose spondylolisthesis and scoliosis from X-ray images without the need for any measurements. We collected raw data from real X-ray images of 338 subjects (i.e., 188 scoliosis, 79 spondylolisthesis, and 71 healthy). Deep transfer learning models were developed to perform three-class classification as well as pair-wise binary classifications among the three classes. The highest mean accuracy and maximum accuracy for three-class classification was 96.73% and 98.02%, respectively. Regarding pair-wise binary classification, high accuracy values were achieved for most of the models (i.e., > 98%). These results and other performance metrics reflect a robust ability to diagnose the subjects’ vertebral column disorders from standard X-ray images. The current study provides a supporting tool that can reasonably help the physicians make the correct early diagnosis with less effort and errors, and reduce the need for surgical interventions.

show abstract

Deep learning system for Meyerding classification and segmental motion measurement in diagnosis of lumbar spondylolisthesis

Cited by 12 publications

References 27 publications

Deep Learning and Imaging for the Orthopaedic Surgeon

Deep Learning and Imaging for the Orthopaedic Surgeon

Automated analysis of three-dimensional CBCT images taken in natural head position that combines facial profile processing and multiple deep-learning models

Using deep transfer learning to detect scoliosis and spondylolisthesis from x-ray images

Contact Info

Product

Resources

About