Muscle Pressure Correlates With Pain Levels in Fibromyalgia Patients.

BACKGROUND/OBJECTIVE: We evaluated patients with fibromyalgia syndrome (FMS) to determine whether there is a correlation between pain scores based on a 0- to 10-point visual analog scale (VAS) and muscle pressure. METHODS: One hundred forty-two patients who satisfied the American College of Rheumatology classification criteria for FMS and 38 non-FMS controls comprised the study groups. Muscle pressure was measured in mm Hg using a pressure gauge attached to a no. 22 needle inserted into the midportion of the trapezius muscle. The muscle pressure was then correlated with the VAS pain score of 0 to 10, some with an increment of 0.5. A second muscle pressure was obtained from 19 patients at a subsequent visit, which was compared with their pain scores. RESULTS: The mean (SD) pain score for 142 patients with FMS was 6.6 (SD, 1.84) on a 0- to 10-point VAS. The mean pain score in the non-FMS subjects was 0.7 (SD, 1.26). The mean muscle pressure in the FMS group was 32.9 (SD, 6.57) mm Hg. The mean muscle pressure in the non-FMS subjects was 10.6 (SD, 3.85) mm Hg. The calculated Pearson correlation coefficient for muscle pressure versus pain score was 0.8312 ( p < 0.0001). This indicates a highly significant association between subjects' muscle pressure and pain scores. For the repeat muscle pressures, the change in muscle pressure was correlated with the change in pain score, and the resulting Pearson correlation coefficient was 0.9255 ( p < 0.0001). These results again indicate a highly significant association between subjects' muscle pressure and pain scores. CONCLUSION: The results show that increased muscle pressure may be a significant cause of pain in FMS, and the etiology of the pain may have a large peripheral component in addition to a centralized origin of the pain.

Automatic Ventriculomegaly Detection in Fetal Brain MRI: A Step-by-Step Deep Learning Model for Novel 2D-3D Linear Measurements.

In this study, we developed an automated workflow using a deep learning model (DL) to measure the lateral ventricle linearly in fetal brain MRI, which are subsequently classified into normal or ventriculomegaly, defined as a diameter wider than 10 mm at the level of the thalamus and choroid plexus. To accomplish this, we first trained a UNet-based deep learning model to segment the brain of a fetus into seven different tissue categories using a public dataset (FeTA 2022) consisting of fetal T2-weighted images. Then, an automatic workflow was developed to perform lateral ventricle measurement at the level of the thalamus and choroid plexus. The test dataset included 22 cases of normal and abnormal T2-weighted fetal brain MRIs. Measurements performed by our AI model were compared with manual measurements performed by a general radiologist and a neuroradiologist. The AI model correctly classified 95% of fetal brain MRI cases into normal or ventriculomegaly. It could measure the lateral ventricle diameter in 95% of cases with less than a 1.7 mm error. The average difference between measurements was 0.90 mm in AI vs. general radiologists and 0.82 mm in AI vs. neuroradiologists, which are comparable to the difference between the two radiologists, 0.51 mm. In addition, the AI model also enabled the researchers to create 3D-reconstructed images, which better represent real anatomy than 2D images. When a manual measurement is performed, it could also provide both the right and left ventricles in just one cut, instead of two. The measurement difference between the general radiologist and the algorithm (p = 0.9827), and between the neuroradiologist and the algorithm (p = 0.2378), was not statistically significant. In contrast, the difference between general radiologists vs. neuroradiologists was statistically significant (p = 0.0043). To the best of our knowledge, this is the first study that performs 2D linear measurement of ventriculomegaly with a 3D model based on an artificial intelligence approach. The paper presents a step-by-step approach for designing an AI model based on several radiological criteria. Overall, this study showed that AI can automatically calculate the lateral ventricle in fetal brain MRIs and accurately classify them as abnormal or normal.