Prevalence of avascular necrosis in idiopathic inflammatory myositis: a single center experience.

OBJECTIVES: To assess the prevalence of avascular necrosis (AVN) in a large cohort of patients with idiopathic inflammatory myopathies (IIM) and define the major associated risk factors. METHODS: We retrospectively reviewed the electronic medical records of all patients with a definitive diagnosis of IIM enrolled in our registry between 2003-2017 and followed until 2020. Pertinent demographic, clinical, serologic and imaging data were collected. A matched group of patients without AVN was then selected for comparison. RESULTS: 1680 patients were diagnosed with IIM. Fifty-one patients developed AVN, with an overall prevalence of 3%. Musculoskeletal magnetic resonance imaging (MSK MRI) was available for 1085 patients and AVN was present in 46 patients (43 lower extremities and 3 upper extremities MRI studies), with a relative prevalence of 4.2%. Most patients with AVN were Caucasian females (57%) with a mean age at diagnosis of 44.5 ± 12.4 years. 61% had dermatomyositis (DM) and 29% had polymyositis (PM). The median time from onset of IIM to diagnosis of AVN was 46 months. The hip joint was most commonly involved in 76% of cases, followed by the knee joint in 15% and shoulder joint in 9%. 81% of patients were asymptomatic. Established risk factors for AVN were not found to be associated with the development of AVN in IIM patients. CONCLUSION: Although mostly asymptomatic and incidental, the overall prevalence of AVN in IIM was 3% and the prevalence by MRI was 4.2%. None of the established risk factors were found to be associated with AVN development

Diagnostic Value of Muscle Ultrasound for Myopathies and Myositis

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Automated diagnosis of myositis from muscle ultrasound: Exploring the use of machine learning and deep learning methods.

To evaluate the use of ultrasound coupled with machine learning (ML) and deep learning (DL) techniques for automated or semi-automated classification of myositis.Eighty subjects comprised of 19 with inclusion body myositis (IBM), 14 with polymyositis (PM), 14 with dermatomyositis (DM), and 33 normal (N) subjects were included in this study, where 3214 muscle ultrasound images of 7 muscles (observed bilaterally) were acquired. We considered three problems of classification including (A) normal vs. affected (DM, PM, IBM); (B) normal vs. IBM patients; and (C) IBM vs. other types of myositis (DM or PM). We studied the use of an automated DL method using deep convolutional neural networks (DL-DCNNs) for diagnostic classification and compared it with a semi-automated conventional ML method based on random forests (ML-RF) and "engineered" features. We used the known clinical diagnosis as the gold standard for evaluating performance of muscle classification.The performance of the DL-DCNN method resulted in accuracies ± standard deviation of 76.2% ± 3.1% for problem (A), 86.6% ± 2.4% for (B) and 74.8% ± 3.9% for (C), while the ML-RF method led to accuracies of 72.3% ± 3.3% for problem (A), 84.3% ± 2.3% for (B) and 68.9% ± 2.5% for (C).This study demonstrates the application of machine learning methods for automatically or semi-automatically classifying inflammatory muscle disease using muscle ultrasound. Compared to the conventional random forest machine learning method used here, which has the drawback of requiring manual delineation of muscle/fat boundaries, DCNN-based classification by and large improved the accuracies in all classification problems while providing a fully automated approach to classification

Problems studied.

<p>Problems studied.</p

DCNN architecture.

<p>This figure depicts the architecture of the AlexNet DCNN used in this study. The muscle images are input at left and the final class probabilities for categorization are output at right. Layers C1-C5 are convolutional layers, followed by fully connected layers (FC6 and FC7), and finally by the Softmax layer outputting the probabilities of the image corresponding to each disease. (For further architectural details, see the original AlexNet paper by Krizhevsky [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0184059#pone.0184059.ref044" target="_blank">44</a>]).</p

Example ultrasound images.

<p>Examples of ultrasound images for both healthy and affected individuals are shown for each muscle group studied. Each row represents one muscle group. The first column contains images of healthy individuals, whereas the second column contains images of patients suffering from myositis. The third and fourth columns show the manual segmentations of muscle and fat tissues corresponding to these images as red (for muscle) and green (for subcutaneous fat) overlays. The muscle group/disease type represented by each row are as follows. A: biceps/DM. B: deltoid/PM. C: FCR/IBM. D: FDP/IBM. E: gastrocnemius/PM. F: rectus femoris/PM. G: tibialis anterior/IBM.</p

Classification performance and standard deviation (parenthesized) for each problem.

<p>Classification performance and standard deviation (parenthesized) for each problem.</p

Demographics and subject characteristics table: Mean and standard deviation (parenthesized) are provided.

<p>Duration of weakness is expressed in units of months. N/A indicates that duration of weakness and CPK was not collected for normal subjects. For the associated antibodies rubric, the parenthesized values indicate the number of patients falling in the category. Also the abbreviations are as described next. C5N1A: cytosolic 5'-nucleotidase 1A; SRP: signal recognition particle; HMGCR: 3-hydroxy-3-methyl-glutaryl-CoA reductase; TIF1gamma: transcriptional intermediary factor 1 gamma.</p

Ultrasound can differentiate inclusion body myositis from disease mimics

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