ArticleGut2021
Utilisation of artificial intelligence for the development of an EUS-convolutional neural network model trained to enhance the diagnosis of autoimmune pancreatitis.
Article in Gut, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05151939 (Endoscopic Ultrasound), which is not on this map. Cited by 69 papers, 4 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Endoscopic Ultrasound (EUS) Assessment of Normal Mediastinal and Abdominal Organ/Anatomic Strictures Using a Novel Developed Artificial Intelligence Model
Who cites it
69 citing papers in PubMed, 4 syntheses or guidelines pooled it, 136 citations in OpenAlex.
- Effectiveness of Radiomics-Based Machine Learning Models in Differentiating Pancreatitis and Pancreatic Ductal Adenocarcinoma: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Artificial Intelligence in Pancreatic Imaging: A Systematic Review.United European gastroenterology journal · 2025Pooled it
- Image-based artificial intelligence for preoperative differentiation of pancreatic cancer from pancreatitis: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- Application of artificial intelligence in the diagnosis of subepithelial lesions using endoscopic ultrasonography: a systematic review and meta-analysis.Frontiers in oncology · 2022Pooled it
- Trial
- Diagnosing Solid Lesions in the Pancreas With Multimodal Artificial Intelligence: A Randomized Crossover Trial.JAMA network open · 2024Trial
- Type 3 Autoimmune Pancreatitis: What Do We Know So Far?Digestive diseases and sciences · 2026Review
- Artificial Intelligence in Pancreatic Endoscopic Ultrasonography: From Image-Based Diagnosis to Cytopathology.Journal of clinical medicine · 2026Review
- Explainable Machine Learning for Detecting Pancreatic Cancer from Structured Endoscopic Ultrasound Data: A Retrospective Multicenter Observational Study.Journal of clinical medicine · 2026Article
- Multi-label dynamic diagnosis of pancreatic diseases using AI-enhanced endoscopic ultrasound: a multi-cohort real-world study.Surgical endoscopy · 2026Article
- Toward Timely Diagnosis of Pancreatic Cancer: Revolutionizing Early Detection Through Genomics, Artificial Intelligence, and Noninvasive Biomarkers.Journal of gastroenterology and hepatology · 2026Review
- Artificial intelligence in chronic and autoimmune pancreatitis: diagnosis, prognosis, and personalized management.Frontiers in medicine · 2026Review
- Role of endoscopic ultrasound in diagnosis and management of autoimmune pancreatitis.Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology · 2025Review
- Pancreatic cancer in 2025: Have we found a solution?World journal of gastroenterology · 2025Review
- Artificial Intelligence in Pancreatobiliary Endoscopy: Current Advances, Opportunities, and Challenges.Journal of clinical medicine · 2025Review
- Emerging role of artificial intelligence in gastroenterology and hepatology.World journal of gastroenterology · 2025Review
- Application of Explainable Artificial Intelligence Based on Visual Explanation in Digestive Endoscopy.Bioengineering (Basel, Switzerland) · 2025Review
- Diagnostic methods for pancreatic cancer and their clinical applications (Review).Oncology letters · 2025Review
- Artificial intelligence system for EUS navigation and anatomical landmark recognition.VideoGIE : an official video journal of the American Society for Gastrointestinal Endoscopy · 2025Article
- Facing the challenges of autoimmune pancreatitis diagnosis: The answer from artificial intelligence.World journal of gastroenterology · 2025Article
9 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
20 authors at 3 institutions in 1 country.
Funding
Abstract
objectiveThe diagnosis of autoimmune pancreatitis (AIP) is challenging. Sonographic and cross-sectional imaging findings of AIP closely mimic pancreatic ductal adenocarcinoma (PDAC) and techniques for tissue sampling of AIP are suboptimal. These limitations often result in delayed or failed diagnosis, which negatively impact patient management and outcomes. This study aimed to create an endoscopic ultrasound (EUS)-based convolutional neural network (CNN) model trained to differentiate AIP from PDAC, chronic pancreatitis (CP) and normal pancreas (NP), with sufficient performance to analyse EUS video in real time.
designA database of still image and video data obtained from EUS examinations of cases of AIP, PDAC, CP and NP was used to develop a CNN. Occlusion heatmap analysis was used to identify sonographic features the CNN valued when differentiating AIP from PDAC.
resultsFrom 583 patients (146 AIP, 292 PDAC, 72 CP and 73 NP), a total of 1 174 461 unique EUS images were extracted. For video data, the CNN processed 955 EUS frames per second and was: 99% sensitive, 98% specific for distinguishing AIP from NP; 94% sensitive, 71% specific for distinguishing AIP from CP; 90% sensitive, 93% specific for distinguishing AIP from PDAC; and 90% sensitive, 85% specific for distinguishing AIP from all studied conditions (ie, PDAC, CP and NP).
conclusionThe developed EUS-CNN model accurately differentiated AIP from PDAC and benign pancreatic conditions, thereby offering the capability of earlier and more accurate diagnosis. Use of this model offers the potential for more timely and appropriate patient care and improved outcome.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.