Is there any coexistence of sacroiliac joints dysfunction with dysfunctions of the occipito-atlanto-axial complex? Part I: The sensorimotor function

Abstract Objective To investigate long-term effects of pregnancy/childbirth on bone marrow edema (BME) and subchondral sclerosis of sacroiliac joints (SIJ) in comparison to MRI changes caused by spondyloarthritis (SpA) and assess the influence of birth method and number of children on SIJ-MRI changes. Materials and methods This is a retrospective cohort study with 349 women (mean age 47 ± 14 years) suffering low back pain. Four subgroups were formed based on SpA diagnosis and childbirth (CB) history. Two musculoskeletal radiologists scored the presence of BME and sclerosis on SIJ-MRI using the Berlin method. Further, an 11-point “global assessment score” representing the overall confidence of SpA diagnosis based on MRI was evaluated in addition to the ASAS (Assessment of Spondyloarthritis International Society) criterion of “positive MRI” for sacroiliitis. Results CB did not correlate with BME score (p = 0.38), whereas SpA diagnosis was associated with a higher BME score (r = 0.31, p < 0.001). Both CB (r = 0.21, p < 0.001) and SpA diagnosis (r = 0.33, p < 0.001) were correlated with a higher sclerosis score. CB was not associated with a higher confidence level in diagnosing SpA based on MRI (p = 0.07), whereas SpA diagnosis was associated with a higher score (r = 0.61, p < 0.001). Both CB (phi = 0.13, p = 0.02) and SpA diagnosis (phi = 0.23, p < 0.001) were significantly associated with a positive ASAS criterion for sacroiliitis. In non-SpA patients with CB, number of children (p = 0.001) was an independent predictor of sclerosis score, while birth method yielded no significant effect (p = 0.75). Conclusion Pregnancy/CB has no impact on long-term BME on SIJ, however, may cause long-term subchondral sclerosis—similar to SpA-associated sclerosis. Number of children is positively correlated with SIJ sclerosis. Birth method yields no effect on SIJ sclerosis.

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The effect of prucalopride on gastric sensorimotor function and satiation in healthy volunteers

Neurogastroenterology & Motility ◽

10.1111/nmo.14083 ◽

2021 ◽

Author(s):

Florencia Carbone ◽

Tim Vanuytsel ◽

Jan Tack

Keyword(s):

Healthy Volunteers ◽

Sensorimotor Function

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Method for Diagnosing the Bone Marrow Edema of Sacroiliac Joint in Patients with Axial Spondyloarthritis Using Magnetic Resonance Image Analysis Based on Deep Learning

Diagnostics ◽

10.3390/diagnostics11071156 ◽

2021 ◽

Vol 11 (7) ◽

pp. 1156

Author(s):

Kang Hee Lee ◽

Sang Tae Choi ◽

Guen Young Lee ◽

You Jung Ha ◽

Sang-Il Choi

Keyword(s):

Bone Marrow ◽

Deep Learning ◽

Bone Marrow Edema ◽

Axial Spondyloarthritis ◽

Mr Images ◽

Sacroiliac Joints ◽

Learning Network ◽

Active Sacroiliitis ◽

Deep Learning Network ◽

Marrow Edema

Axial spondyloarthritis (axSpA) is a chronic inflammatory disease of the sacroiliac joints. In this study, we develop a method for detecting bone marrow edema by magnetic resonance (MR) imaging of the sacroiliac joints and a deep-learning network. A total of 815 MR images of the sacroiliac joints were obtained from 60 patients diagnosed with axSpA and 19 healthy subjects. Gadolinium-enhanced fat-suppressed T1-weighted oblique coronal images were used for deep learning. Active sacroiliitis was defined as bone marrow edema, and the following processes were performed: setting the region of interest (ROI) and normalizing it to a size suitable for input to a deep-learning network, determining bone marrow edema using a convolutional-neural-network-based deep-learning network for individual MR images, and determining sacroiliac arthritis in subject examinations based on the classification results of individual MR images. About 70% of the patients and normal subjects were randomly selected for the training dataset, and the remaining 30% formed the test dataset. This process was repeated five times to calculate the average classification rate of the five-fold sets. The gradient-weighted class activation mapping method was used to validate the classification results. In the performance analysis of the ResNet18-based classification network for individual MR images, use of the ROI showed excellent detection performance of bone marrow edema with 93.55 ± 2.19% accuracy, 92.87 ± 1.27% recall, and 94.69 ± 3.03% precision. The overall performance was additionally improved using a median filter to reflect the context information. Finally, active sacroiliitis was diagnosed in individual subjects with 96.06 ± 2.83% accuracy, 100% recall, and 94.84 ± 3.73% precision. This is a pilot study to diagnose bone marrow edema by deep learning based on MR images, and the results suggest that MR analysis using deep learning can be a useful complementary means for clinicians to diagnose bone marrow edema.

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