Acute pancreatitis: A review of diagnosis, severity prediction and prognosis assessment from imaging technology, scoring system and artificial intelligence

Abstract

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.

Complementary comments on diagnosis, severity and prognosis prediction of acute pancreatitis

“…Hu et al [ 1 ] recently published a study that reviewed the diagnosis, severity prediction and prognosis assessment of acute pancreatitis. In their article details were provided regarding the utility and certain restrictions of magnetic resonance imaging (MRI), computed tomography (CT) and ultrasonography in the diagnosis, severity assessment, and the prognostic evaluation of acute pancreatitis.…”

“…In the section of the article devoted to imaging, Hu et al [ 1 ] provided detailed imaging findings of acute pancreatitis. On the other hand, there are some diseases that, both clinically and radiologically, can be mistaken for acute pancreatitis.…”

“…As mentioned in the article by Hu et al [ 1 ], imaging methods, especially CT and MRI, play an important role in determining the severity and predicting the prognosis of acute pancreatitis. CT is frequently used to determine the presence and extent of pancreatic necrosis as well as to identify complications, thus showing the severity of the acute pancreatitis.…”

“…In addition to the imaging techniques listed in the article from Hu et al [ 1 ], DECT is another technique that can be used in diagnosis, severity assessment and prognosis prediction. When compared to standard CT, DECT has a better sensitivity for early acute pancreatitis[ 5 ].…”

“…Because of its limited sensitivity in detecting the necrotic debris in the early stage, it is challenging to differentiate between acute necrotic collection and acute periprancreatic fluid collection on CT. For that reason, the ideal time to have an initial CT assessment is between 72 h and 96 h after the onset of symptoms, according to the recommendations from the American Pancreatic Association and the International Association of Pancreatology. The limitations of MRI include the need for a greater degree of patient cooperation, limited field of view, increased cost and longer scanning times[ 1 , 9 ].…”