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Hiroyuki Isayama

Publications and source records attributed to Hiroyuki Isayama.

2 recordsLinked to original sources

Current Management of Primary Sclerosing Cholangitis (PSC) ~A Proposal for Early-stage PSC~.

Primary sclerosing cholangitis (PSC) is a chronic, progressive cholangiopathy characterized by inflammation and fibrosis of intrahepatic and/or extrahepatic bile ducts. Its pathogenesis remains incompletely understood, and liver transplantation is currently the only curative treatment available. The diagnosis remains challenging, and no disease-specific biomarkers have been established. Recently, anti-integrin αvβ6 antibodies have emerged as promising serological biomarkers with high specificity for PSC. Advances in imaging modalities, including magnetic resonance cholangiopancreatography and peroral cholangioscopy, have improved diagnostic accuracy for PSC. Although various therapeutic approaches have been investigated, no treatment has been shown to improve the long-term outcomes. Microbiota-targeted therapies represent a promising emerging strategy. The clinical course of PSC, particularly in its early stages, is poorly defined. We propose a definition of early stage PSC consisting of two subtypes: small-duct PSC without liver fibrosis and large-duct PSC without cholestatic enzyme elevation or biliary strictures. Early intervention at this stage may improve the prognosis, thus highlighting the need for further validation.

Primary sclerosing cholangitis

Development and Crossover Evaluation of an Artificial Intelligence-Assisted System for Solid Pancreatic Lesion Detection and Pancreatic Parenchyma Recognition in Endoscopic Ultrasonography (With Video).

BACKGROUND AND STUDY AIMS: Pancreatobiliary endoscopic ultrasonography (EUS) is technically demanding, and supervised training opportunities are limited. We developed an artificial intelligence (AI) overlay system for detecting solid pancreatic lesions (SPL) and recognizing pancreatic parenchyma (PP) and evaluated its effect on reader performance. PATIENTS AND METHODS: Across six centers, two deep learning-based models were trained using expert-annotated EUS frames. We then conducted a randomized, two-sequence, two-period crossover reader study in which eight endosonographers (five novices and three experts) interpreted image sets with and without AI assistance. The primary endpoint was superiority of sensitivity for SPL detection among novices; key secondary endpoints included specificity and PP recognition. RESULTS: From 118 patients, 120 SPL-positive/negative image sets and 160 PP-positive/negative image sets were constructed. Among novices, AI assistance improved SPL detection sensitivity (88.7% vs. 76.8%, p&#x2009;<&#x2009;0.001) and accuracy (86.4% vs. 78.7%), while specificity met the predefined noninferiority criterion (84.2% vs. 80.5%, p&#x2009;<&#x2009;0.001). For PP recognition, sensitivity increased numerically (86.3% vs. 83.3%) but did not meet the predefined superiority criterion (p&#x2009;=&#x2009;0.095); specificity met the noninferiority criterion (87.8% vs. 81.0%), and accuracy increased from 82.1% to 87.0%. Among experts, sensitivity was maintained for both tasks, whereas specificity increased with AI assistance. CONCLUSIONS: AI assistance improved SPL detection among novice endosonographers. For PP recognition, sensitivity increased without reaching statistical superiority, whereas specificity met the predefined noninferiority criterion. These findings support a potential adjunctive role for AI in EUS interpretation.

Humans