Evidence map›Paper›PMID 35204524›Full record

ArticleDiagnostics (Basel, Switzerland)2022

Development of a Novel Evaluation Method for Endoscopic Ultrasound-Guided Fine-Needle Biopsy in Pancreatic Diseases Using Artificial Intelligence.

Takuya Ishikawa, Masato Hayakawa, Hirotaka Suzuki, Eizaburo Ohno, Yasuyuki Mizutani, Tadashi Iida, Mitsuhiro Fujishiro, Hiroki Kawashima, Kazuhiro Hotta

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 3 pooled it
3.1field-weighted citation impact, top 7% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed, 3 syntheses or guidelines pooled it, 30 citations in OpenAlex.

  1. Pooled it
  2. Artificial Intelligence in Pancreatic Imaging: A Systematic Review.United European gastroenterology journal · 2025
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  5. Diagnostic value of endoscopic ultrasound in staging of pancreatic cancer.World journal of gastrointestinal oncology · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 5 institutions in 1 country.

Takuya IshikawaDepartment of Gastroenterology and Hepatology, Graduate School of Medicine, Nagoya University, 65 Tsurumai-cho, Showa-ku, Nagoya 4668550, Japan.ORCID 0000-0001-5814-3555
Masato HayakawaDepartment of Electrical and Electronic Engineering, Faculty of Science and Technology, Meijo University, Nagoya 4688502, Japan.
Hirotaka SuzukiDepartment of Gastroenterology and Hepatology, Graduate School of Medicine, Nagoya University, 65 Tsurumai-cho, Showa-ku, Nagoya 4668550, Japan.
Eizaburo OhnoDepartment of Gastroenterology and Hepatology, Graduate School of Medicine, Nagoya University, 65 Tsurumai-cho, Showa-ku, Nagoya 4668550, Japan.
Yasuyuki MizutaniDepartment of Gastroenterology and Hepatology, Graduate School of Medicine, Nagoya University, 65 Tsurumai-cho, Showa-ku, Nagoya 4668550, Japan.ORCID 0000-0002-4363-3161
Tadashi IidaDepartment of Gastroenterology and Hepatology, Graduate School of Medicine, Nagoya University, 65 Tsurumai-cho, Showa-ku, Nagoya 4668550, Japan.
Mitsuhiro FujishiroDepartment of Gastroenterology and Hepatology, Graduate School of Medicine, The University of Tokyo, Tokyo 1138655, Japan.ORCID 0000-0002-4074-1140
Hiroki KawashimaDepartment of Endoscopy, Nagoya University Hospital, Nagoya 4668550, Japan.
Kazuhiro HottaDepartment of Electrical and Electronic Engineering, Faculty of Science and Technology, Meijo University, Nagoya 4688502, Japan.
Nagoya University · JPMeijo University · JPNagoya University Hospital · JPThe University of Tokyo · JPToyohashi Municipal Hospital · JP

Funding

Grant-in-Aid for Scientific Research support JP20K12689
6 · The paper itself

Abstract

We aimed to develop a new artificial intelligence (AI)-based method for evaluating endoscopic ultrasound-guided fine-needle biopsy (EUS-FNB) specimens in pancreatic diseases using deep learning and contrastive learning. We analysed a total of 173 specimens from 96 patients who underwent EUS-FNB with a 22 G Franseen needle for pancreatic diseases. In the initial study, the deep learning method based on stereomicroscopic images of 98 EUS-FNB specimens from 63 patients showed an accuracy of 71.8% for predicting the histological diagnosis, which was lower than that of macroscopic on-site evaluation (MOSE) performed by EUS experts (81.6%). Then, we used image analysis software to mark the core tissues in the photomicrographs of EUS-FNB specimens after haematoxylin and eosin staining and verified whether the diagnostic performance could be improved by applying contrastive learning for the features of the stereomicroscopic images and stained images. The sensitivity, specificity, and accuracy of MOSE were 88.97%, 53.5%, and 83.24%, respectively, while those of the AI-based diagnostic method using contrastive learning were 90.34%, 53.5%, and 84.39%, respectively. The AI-based evaluation method using contrastive learning was comparable to MOSE performed by EUS experts and can be a novel objective evaluation method for EUS-FNB.

Indexed as

artificial intelligencecontrastive learningdeep learningendoscopic ultrasound-guided fine-needle biopsypancreatic diseasesstereomicroscope

Identifiers

PMID35204524
PMCPMC8871496
OpenAlexW4211210514

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.