A deep learning pipeline for automatized assessment of spinal MRI

Balzer, Irina; Mühlemann, Cand. Med. Malin; Jokeit, Moritz; Rawal, Ishaan Singh; Snedeker, Jess G.; Farshad, Prof. Dr. Med. Mazda; Widmer, Jonas

doi:10.1016/j.cmpbup.2022.100081

Cited by 2 publications

(2 citation statements)

References 27 publications

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“…Lumbar Intervertebral Discs (LID) are the joints of the lumbar spine that bolster spinal motion, exibility, and stability (Wu et al, 2016). Fundamentally, LID consists of three important parts including inner nucleus pulposus (INP), outer annulus brosus (OAF), and endplates (EP) (Balzer et al, 2022;Cihangiroglu et al,2004). Degenerative changes in the LID can occur as early as in the rst decade of life and commonly are associated with symptoms including severe/chronic lower back pain, sciatica, and disability that are in connection with reduced quality of life (Rohlmann et al, 2006;Wu et al, 2016;Jiao et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…For patients experiencing simultaneously two or more of such health conditions (i.e., comorbidity of two or more symptoms), the disease may advance to a more severe or more complicated stage, causing generally a situation where differentiated health care or urgent intervention/treatment is needed (Tang et al, 2022;Hussain et al, 2023). Statistics data indicate that globally approximately more than two-fths of individuals experience lower back pain in connection with the lumbar spine within their lifetime (Miskin et al, 2020;Miskin et al, 2022;Hussain et al, 2023;Jordan et al, 2015;Balzer et al, 2022). Degeneration of lumbar spine usually is associated with alterations to the structure of LID, damage to the ligaments, and impairment in the vertebras (Wu et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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VIRD: An ensemble transfer learning to improve automatic stratification of types of lumbar intervertebral disc degeneration

Nakamoto,

Chen,

Wang

et al. 2023

Preprint

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The degeneration of the intervertebral discs in the lumbar spine is the common cause of neurological and physical dysfunctions and chronic disability of patients, which can be stratified into single- (e.g., disc herniation, disc prolapse, or disc bulge) and comorbidity-type degeneration (i.e., simultaneous presence of two or more conditions of disc herniation, prolapse, and bulge in a patient) respectively. The degenerative changes of the lumbar spine differentiate in the level of severity and type. A sample of lumbar magnetic resonance images from multiple clinical hospitals in China was collected and used in the proposal assessment. Theoretically, we devised a novel transfer learning framework VIRD by ensembling four pre-trained models including Densenet169, ResNet101, InceptionResNetV2, and VGG16. Thereafter, the proposed approach was applied to the clinical data and achieved 99% accuracy versus 94%, 96%, 96%, 96%, and 98% for compositional benchmark models of VGG16, InceptionResNetV2, DenseNet169, ResNet101, and other ensemble deep learning respectively. Furthermore, improved performance was observed as well for the metric of the area under the curve, producing a 1% increase relative to other ensemble learning, a 2% increase relative to most-studied models, and a 3% increase relative to the baseline models. The novel methodology can be used as a guide in the initial and efficient screening of complex degeneration of lumbar intervertebral discs and assist in the early-stage selection of clinically differentiated treatment options.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

VIRD: An ensemble transfer learning to improve automatic stratification of types of lumbar intervertebral disc degeneration

Nakamoto,

Chen,

Wang

et al. 2023

Preprint

View full text Add to dashboard Cite

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A deep neural network for MRI spinal inflammation in axial spondyloarthritis

Lin,

Chan,

Chung

et al. 2024

Eur Spine J

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Objective To develop a deep neural network for the detection of inflammatory spine in short tau inversion recovery (STIR) sequence of magnetic resonance imaging (MRI) on patients with axial spondyloarthritis (axSpA). Methods A total 330 patients with axSpA were recruited. STIR MRI of the whole spine and clinical data were obtained. Regions of interests (ROIs) were drawn outlining the active inflammatory lesion consisting of bone marrow edema (BME). Spinal inflammation was defined by the presence of an active inflammatory lesion on the STIR sequence. The 'fake-color' images were constructed. Images from 270 and 60 patients were randomly separated into the training/validation and testing sets, respectively. Deep neural network was developed using attention UNet. The neural network performance was compared to the image interpretation by a radiologist blinded to the ground truth. Results Active inflammatory lesions were identified in 2891 MR images and were absent in 14,590 MR images. The sensitivity and specificity of the derived deep neural network were 0.80 ± 0.03 and 0.88 ± 0.02, respectively. The Dice coefficient of the true positive lesions was 0.55 ± 0.02. The area under the curve of the receiver operating characteristic (AUC-ROC) curve of the deep neural network was 0.87 ± 0.02. The performance of the developed deep neural network was comparable to the interpretation of a radiologist with similar sensitivity and specificity. Conclusion The developed deep neural network showed similar sensitivity and specificity to a radiologist with four years of experience. The results indicated that the network can provide a reliable and straightforward way of interpreting spinal MRI. The use of this deep neural network has the potential to expand the use of spinal MRI in managing axSpA.

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A deep learning pipeline for automatized assessment of spinal MRI

Cited by 2 publications

References 27 publications

VIRD: An ensemble transfer learning to improve automatic stratification of types of lumbar intervertebral disc degeneration

VIRD: An ensemble transfer learning to improve automatic stratification of types of lumbar intervertebral disc degeneration

A deep neural network for MRI spinal inflammation in axial spondyloarthritis

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