The most common CT finding in patients with SLE and acute abdominal pain is ischemic bowel disease. CT is useful for detecting the primary cause of gastrointestinal symptoms, planning treatment, and monitoring for infarction or perforation.

The study was undertaken to evaluate clinical and laboratory characteristics of patients with lupus enteritis and to investigate its association with anti-endothelial cell antibodies (AECAs). Systemic lupus erythematosus (SLE) patients who were admitted to Kangnam St. Mary's Hospital with complaints of acute abdominal pain from January 1990 to July 2006 were reviewed retrospectively. The clinical features, laboratory data and prognosis of these patients were analyzed. Among the 706 SLE patients admitted during the study period, 87 were found to admit for acute abdominal pain. Among them, 41 patients were identified with lupus enteritis. The SLE disease activity index score at admission and the mean prednisolone dose administered during the last three months prior to admission were significantly higher in patients with lupus enteritis than those with other causes (P < 0.001, P = 0.036). Serum anti-endothelial cell antibody (AECA-IgG) titer was also significantly higher in patients with lupus enteritis than those with other manifestations or healthy controls (P = 0.040, P < 0.001). Four out of 13 recurrent patients had pre-existing anti-phospholipid syndrome (APS), whereas only one out of 28 non-recurrent patients had pre-existing APS (P = 0.028). Most of the patients with lupus enteritis showed good response to high-dose intravenous steroids and there was no death directly associated with lupus enteritis.

The interleukin-33 (IL-33)/ST2 pathway has emerged as an intercellular signaling system that participates in antigen-allergen response, autoimmunity and fibrosis. It has been suggested that IL-33/ST2 signaling has been involved in the pathogenesis of rheumatoid arthritis (RA), because IL-33 and its receptor have been specifically mapped to RA synovium. The aim of this study was to determine the levels of IL-33 and sST2 in sera and synovial fluids in patients with RA. The serum level of IL-33 was significantly higher in patients with RA (294.9 ± 464.0 pg/mL) than in healthy controls (96.0 ± 236.9 pg/mL, P = 0.002). The synovial fluid level of IL-33 was significantly higher in RA patients than in osteoarthritis patients. The level of serum sST2 was higher in RA patients than in healthy controls (P = 0.042). A significant relationship was found between the levels of IL-33 and IL-1β (r = 0.311, P = 0.005), and IL-33 and IL-6 (r = 0.264, P = 0.017) in 81 RA patients. The levels of IL-33, sST2 and C-reactive protein decreased after conventional disease-modifying antirheumatic drugs treatment in 10 patients with treatment-naïve RA. Conclusively, IL-33 is involved in the pathogenesis of RA and may reflect the degree of inflammation in patients with RA.

Artificial intelligence is likely to perform several roles currently performed by humans, and the adoption of artificial intelligence-based medicine in gastroenterology practice is expected in the near future. Medical image-based diagnoses, such as pathology, radiology, and endoscopy, are expected to be the first in the medical field to be affected by artificial intelligence. A convolutional neural network, a kind of deep-learning method with multilayer perceptrons designed to use minimal preprocessing, was recently reported as being highly beneficial in the field of endoscopy, including esophagogastroduodenoscopy, colonoscopy, and capsule endoscopy. A convolutional neural network-based diagnostic program was challenged to recognize anatomical locations in esophagogastroduodenoscopy images, Helicobacter pylori infection, and gastric cancer for esophagogastroduodenoscopy; to detect and classify colorectal polyps; to recognize celiac disease and hookworm; and to perform small intestine motility characterization of capsule endoscopy images. Artificial intelligence is expected to help endoscopists provide a more accurate diagnosis by automatically detecting and classifying lesions; therefore, it is essential that endoscopists focus on this novel technology. In this review, we describe the effects of artificial intelligence on gastroenterology with a special focus on automatic diagnosis, based on endoscopic findings.

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