ArticleCancer medicine2023
A deep learning model using hyperspectral image for EUS-FNA cytology diagnosis in pancreatic ductal adenocarcinoma.
Article in Cancer medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07381192 (An Artificial Intelligence System for Multimodal, Multi-class Diagnosing Solid Pancreatic Lesions Based on Endoscopic Ultrasound), which is not on this map. Cited by 18 papers, 4 of them syntheses that pooled it.
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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.
An Artificial Intelligence System for Multimodal, Multi-class Diagnosing Solid Pancreatic Lesions Based on Endoscopic Ultrasound
Who cites it
18 citing papers in PubMed, 4 syntheses or guidelines pooled it, 24 citations in OpenAlex.
- What Are Computer-Assisted Methods Achieving in Fine-Needle Aspiration Cytology of the Pancreas? A Systematic Review and Meta-Analysis.Cytopathology : official journal of the British Society for Clinical Cytology · 2026Pooled it
- Clinical applications of hyperspectral imaging in gastroenterology and hepatology: A systematic review.Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology · 2026Pooled it
- Artificial Intelligence in Pancreatic Imaging: A Systematic Review.United European gastroenterology journal · 2025Pooled it
- Diagnosis methods for pancreatic cancer with the technique of deep learning: a review and a meta-analysis.Frontiers in oncology · 2025Pooled it
- Artificial intelligence in pancreatic cancer: applications in early detection, tumor staging, and survival prediction-a comprehensive review.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Artificial Intelligence in Pancreatic Endoscopic Ultrasonography: From Image-Based Diagnosis to Cytopathology.Journal of clinical medicine · 2026Review
- Evaluation of the Usefulness of Machine Learning and Artificial Intelligence on Hyperspectral Images in the Diagnosis of Myelodysplastic Syndrome.Journal of biophotonics · 2026Article
- Multimodal imaging platform for rapid non-destructive evaluation of pancreatic puncture specimens.Journal of biomedical optics · 2026Article
- Hyperspectral Imaging of Whole-Cell Region for Differentiating Cervical Squamous Intraepithelial Lesion Cytology.Cancer medicine · 2026Article
- Differentiating cytology of pancreatic ductal adenocarcinoma and pancreatic neuroendocrine tumors by EUS-FNA through hyperspectral imaging technology combined with artificial intelligence.Therapeutic advances in gastroenterology · 2026Article
- Visible-Light Hyperspectral Reconstruction and PCA-Based Feature Extraction for Malignant Pleural Effusion Cytology.Biosensors · 2025Article
- Unlocking the Potential of AI in EUS and ERCP: A Narrative Review for Pancreaticobiliary Disease.Cancers · 2025Review
- A semi-supervised convolutional neural network for diagnosis of pancreatic ductal adenocarcinoma based on EUS-FNA cytological images.BMC cancer · 2025Article
- Review
- Artificial Intelligence in Endoscopic Ultrasonography-Guided Fine-Needle Aspiration/Biopsy (EUS-FNA/B) for Solid Pancreatic Lesions: Opportunities and Challenges.Diagnostics (Basel, Switzerland) · 2023Review
- A deep learning model using hyperspectral image for EUS-FNA cytology diagnosis in pancreatic ductal adenocarcinoma.Cancer medicine · 2023Article
- AI-assisted rapid on-site evaluation ROSE of EUS-FNA cytopathology for pancreatic solid lesions: A two-stage deep learning approach.Endoscopic ultrasoundArticle
- A Critical Review of Deep Learning Technique and Its Applications in Clinical Cytology.Journal of cytologyReview
Corrections and comments
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Authors and funding
16 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
aimsEndoscopic ultrasonography-guided fine-needle aspiration/biopsy (EUS-FNA/B) is considered to be a first-line procedure for the pathological diagnosis of pancreatic cancer owing to its high accuracy and low complication rate. The number of new cases of pancreatic ductal adenocarcinoma (PDAC) is increasing, and its accurate pathological diagnosis poses a challenge for cytopathologists. Our aim was to develop a hyperspectral imaging (HSI)-based convolution neural network (CNN) algorithm to aid in the diagnosis of pancreatic EUS-FNA cytology specimens.
methodsHSI images were captured of pancreatic EUS-FNA cytological specimens from benign pancreatic tissues (n = 33) and PDAC (n = 39) prepared using a liquid-based cytology method. A CNN was established to test the diagnostic performance, and Attribution Guided Factorization Visualization (AGF-Visualization) was used to visualize the regions of important classification features identified by the model.
resultsA total of 1913 HSI images were obtained. Our ResNet18-SimSiam model achieved an accuracy of 0.9204, sensitivity of 0.9310 and specificity of 0.9123 (area under the curve of 0.9625) when trained on HSI images for the differentiation of PDAC cytological specimens from benign pancreatic cells. AGF-Visualization confirmed that the diagnoses were based on the features of tumor cell nuclei.
conclusionsAn HSI-based model was developed to diagnose cytological PDAC specimens obtained using EUS-guided sampling. Under the supervision of experienced cytopathologists, we performed multi-staged consecutive in-depth learning of the model. Its superior diagnostic performance could be of value for cytologists when diagnosing PDAC.
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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.