Background Intraneural ganglion cysts are rare. They affect the peripheral nerves. According to the most widely accepted theory (articular/synovial theory), the cysts are formed from a capsular defect of an adjacent joint, so that synovial fluid spreads along the epineurium of a nerve branch. This leads to diverse neurological symptoms. We will illustrate this disease based on three of our own cases. Methods Patients were examined between 2011 and 2018 using lower limb MRI. MRI scans were also performed for the follow-up examinations. Case studies and Discussion The patients had many symptoms. We were able to accurately detect the intraneural ganglion cysts on MRI and provide the treating surgeons with the basis for the operation to be performed. The success of surgical therapy depends on the resection of the nerve endings supplying the joint as the only way to treat the origin of the disease and prevent recurrence. Based on our case studies, we can support the commonly favored articular/synovial theory. Key Points: Citation Format
Osteoporosis is a complex multifactorial skeletal disease and has become a major socioeconomic issue, causing tremendous hospitalization and rehabilitation costs. In addition to age, premature menopause or the use of oral corticosteroids, vertebral fractures are regarded as main risk factors for developing osteoporosis and associated fractures. In this work, we adapt an existing automated pipeline to classify fracture grades of individual vertebrae and the spine as a whole: First, vertebral body centers were identified on CT images by a hierarchical neural network. Next, the fracture grades of individual vertebrae were processed by a multi-head, feed-forward convolutional neural network. The sum of the classified grades was then evaluated according to the German radiology guidelines. A hyperparameter search on validation data showed the most promising results for an output configuration based on three sequentially applied binary classification outputs trained using binary cross-entropy: Grade 0-1 vs 2-3, and 0 vs 1-3, and 0-2 vs 3. In a cross-validation setting on 159 low-dose CT images, our pipeline accurately classified patients to have sum-scores ≥ 2 with sensitivities and specificities of 90 % ± 5.0 % and 87 % ± 2.7 %, respectively. As our method was based on classifying individual vertebrae, we were able to provide both the fracture position and severity to enhance transparency, interpretability and usability.
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