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Acute pancreatitis (AP) is a potentially life-threatening inflammatory disease of the pancreas, with clinical management determined by the severity of the disease. Diagnosis, severity prediction, and prognosis assessment of AP typically involve the use of imaging technologies, such as computed tomog...
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| Published in | World journal of gastroenterology : WJG Vol. 29; no. 37; pp. 5327 - 5338 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Baishideng Publishing Group Inc
07.10.2023
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1007-9327 2219-2840 2219-2840 |
| DOI | 10.3748/wjg.v29.i37.5327 |
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| Summary: | Acute pancreatitis (AP) is a potentially life-threatening inflammatory disease of the pancreas, with clinical management determined by the severity of the disease. Diagnosis, severity prediction, and prognosis assessment of AP typically involve the use of imaging technologies, such as computed tomography, magnetic resonance imaging, and ultrasound, and scoring systems, including Ranson, Acute Physiology and Chronic Health Evaluation II, and Bedside Index for Severity in AP scores. Computed tomography is considered the gold standard imaging modality for AP due to its high sensitivity and specificity, while magnetic resonance imaging and ultrasound can provide additional information on biliary obstruction and vascular complications. Scoring systems utilize clinical and laboratory parameters to classify AP patients into mild, moderate, or severe categories, guiding treatment decisions, such as intensive care unit admission, early enteral feeding, and antibiotic use. Despite the central role of imaging technologies and scoring systems in AP management, these methods have limitations in terms of accuracy, reproducibility, practicality and economics. Recent advancements of artificial intelligence (AI) provide new opportunities to enhance their performance by analyzing vast amounts of clinical and imaging data. AI algorithms can analyze large amounts of clinical and imaging data, identify scoring system patterns, and predict the clinical course of disease. AI-based models have shown promising results in predicting the severity and mortality of AP, but further validation and standardization are required before widespread clinical application. In addition, understanding the correlation between these three technologies will aid in developing new methods that can accurately, sensitively, and specifically be used in the diagnosis, severity prediction, and prognosis assessment of AP through complementary advantages. |
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| Bibliography: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 Corresponding author: Trana Hussaini, BPharm, MHSc, PharmD, Associate Professor, Pharmacist, BC Liver Transplant Program, Vancouver General Hospital, 899 West 12th Avenue, Vancouver V5Z 1M9, BC, Canada. trana.hussaini@vch.ca Author contributions: Zhu K contributed to conception and design, analysis and interpretation of data, drafting the article, and final approval; Rohan K contributed to acquisition of data, analysis and interpretation of data, drafting the article; Chahal D contributed to revising the article and final approval; Yoshida E contributed to conception and design, revising the article, and final approval; Hussaini T contributed to conception and design, revising the article, and final approval. |
| ISSN: | 1007-9327 2219-2840 2219-2840 |
| DOI: | 10.3748/wjg.v29.i37.5327 |