Question: A 62-year-old woman presented with sudden abdominal pain in the left lower quadrant for 2 days. The needle-like abdominal pain was aggravated when changing body position and during defecation, but remitted when lying flat. She had a history of cerebral infarction and had been accepting acupuncture therapy at the right Huantiao (GB30) acupoint, one-third of the distance from the femoral greater trochanter peak to the sacral hiatus. Physical examination revealed left lower abdominal tenderness without other symptoms. Laboratory investigation was unremarkable. An abdominal computed tomography (CT) scan revealed a metallic density shadow in the left lower quadrant of the abdomen (Figure A). The patient made a full recovery postoperatively. What is the diagnosis? Look on page 1842 for the answer and see the Gastroenterology web site (www.gastrojournal. org) for more information on submitting your favorite image to Clinical Challenges and Images in GI.
BackgroundSolitary cecal ulcer is a rare disease. Its etiology is unknown and there are no pathognomonic symptoms. There are rare reports mimicking carcinoma as seen in this case.Case PresentationA 64 year-old woman presented with a history of intermittent right lower abdominal pain for 20 years and worsening for 1 year. Colonoscopy revealed an enormous cecal ulcer. The PET-CT showed increased metabolism of the lesion. She underwent a right hemicolectomy. Histopathological examination revealed chronic non-specific inflammation. A rare diagnosis of the solitary cecal ulcer was ultimately made.ConclusionSolitary cecal ulcer is a rare, idiopathic entity. It mimics inflammatory bowel disease, malignancy, infection, etc. The comprehensive images of this case describe the characteristics of the disease.
Background: Several scalp EEG epilepsy detection methods based on machine learning have achieved good detection accuracy. However, in clinical applications, different EEG acquisition equipment and experience of neurologists make the quality and style of EEG signals different, which makes previous epilepsy detection models cannot be widely used. The establishment of epilepsy detection model for a certain hospital usually depends on a large number of EEG samples, but there are usually few EEG samples from a certain hospital. Methods: To solve this problem, we proposed a small sample epilepsy detection method based on convolutional prototype learning (CPL) in this paper. CPL consists of convolutional neural network (CNN) and prototype learning. CNN is used as an adaptive feature extraction algorithm, and prototype learning is used as a small sample classification algorithm. Results: In the experiment, we select 20, 40, 60, 80, 100 and 120 samples to train and save 6 CPL-based detection models. The 6 models are used to classify the test samples, and the accuracy are 75.97%, 83.24%, 85.67%, 88.27%, 91.09% and 94.43% respectively. Conclusions: The CPL can realize automatic feature extraction of EEG signals, and solve the problem of insufficient training samples in epilepsy detection.
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