Effective abdominal ultrasonographic detection of pancreatic cystic lesions using artificial intelligence‐assisted noise reduction

Abstract

AbstractIntroductionPatients with intraductal papillary mucinous neoplasms (IPMNs) exhibit a high incidence of pancreatic cancer. An IPMN presents as a pancreatic cystic lesion (PCL). Artificial intelligence (AI) can be used to enhance ultrasonographic images. We retrospectively evaluated the utility of AI‐assisted ultrasonography in terms of PCL detection.MethodsIn total, 969 patients who underwent artificial narrow intelligence‐assisted transabdominal ultrasound examinations (AI group) were compared with 943 patients who underwent non‐AI‐assisted examinations (control group). The morphological changes and the frequencies of newly detected PCLs were compared between the groups.ResultsThe PCL detection rate was significantly higher in the AI group than in the control group (1.25 ± 0.36 and 0.21 ± 0.15%, respectively; p = .008). AI‐assisted ultrasound evaluation was independently associated with enhanced PCL detection (odds ratio: 5.83, 95% confidence interval: 1.29–26.29, p = .02). PCLs smaller than 8 mm and all PCLs in the pancreatic tail were detected in the AI group.PCL morphological changes were more common in the AI group than in the control group (63.6 ± 14.5 and 16.7 ± 15.2%, respectively; p = .04).ConclusionAI‐assisted transabdominal ultrasonography enhances the PCL detection rate. The use of AI is likely to increase the IPMN detection rate.