Evidence map›Paper›PMID 33565422›Full record

ArticleEndoscopic ultrasound

Deep learning with convex probe endobronchial ultrasound multimodal imaging: A validated tool for automated intrathoracic lymph nodes diagnosis.

Jin Li, Xinxin Zhi, Junxiang Chen, Lei Wang, Mingxing Xu, Wenrui Dai, Jiayuan Sun, Hongkai Xiong

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

Article in Endoscopic ultrasound. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 3 pooled it
2.2field-weighted citation impact, top 12% 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

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

  1. Pooled it
  2. Artificial intelligence in bronchoscopy: a systematic review.European respiratory review : an official journal of the European Respiratory Society · 2025
    Pooled it
  3. Pooled it
  4. Review
  5. Review
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Observational
  14. Article
  15. Artificial Intelligence in Interventional Pulmonology.Therapeutic advances in pulmonary and critical care medicine
    Review
  16. Review
  17. Article
  18. Article
  19. Article
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

8 authors at 2 institutions in 1 country.

Jin LiSchool of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Xinxin ZhiDepartment of Respiratory Endoscopy, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai; Department of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai; Shanghai Engineering Research Center of Respiratory Endoscopy, Shanghai, China.
Junxiang ChenDepartment of Respiratory Endoscopy, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai; Department of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai; Shanghai Engineering Research Center of Respiratory Endoscopy, Shanghai, China.
Lei WangDepartment of Ultrasound, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.
Mingxing XuSchool of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Wenrui DaiSchool of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Jiayuan SunDepartment of Respiratory Endoscopy, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai; Department of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai; Shanghai Engineering Research Center of Respiratory Endoscopy, Shanghai, China.
Hongkai XiongSchool of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Shanghai Jiao Tong University · CNShanghai Chest Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectivesAlong with the rapid improvement of imaging technology, convex probe endobronchial ultrasound (CP-EBUS) sonographic features play an increasingly important role in the diagnosis of intrathoracic lymph nodes (LNs). Conventional qualitative and quantitative methods for EBUS multimodal imaging are time-consuming and rely heavily on the experience of endoscopists. With the development of deep-learning (DL) models, there is great promise in the diagnostic field of medical imaging. MATERIALS AND

methodsWe developed DL models to retrospectively analyze CP-EBUS images of 294 LNs from 267 patients collected between July 2018 and May 2019. The DL models were trained on 245 LNs to differentiate benign and malignant LNs using both unimodal and multimodal CP-EBUS images and independently evaluated on the remaining 49 LNs to validate their diagnostic efficiency. The human comparator group consisting of three experts and three trainees reviewed the same test set as the DL models.

resultsThe multimodal DL framework achieves an accuracy of 88.57% (95% confidence interval [CI] [86.91%-90.24%]) and area under the curve (AUC) of 0.9547 (95% CI [0.9451-0.9643]) using the three modes of CP-EBUS imaging in comparison to the accuracy of 80.82% (95% CI [77.42%-84.21%]) and AUC of 0.8696 (95% CI [0.8369-0.9023]) by experts. Statistical comparison of their average receiver operating curves shows a statistically significant difference (P < 0.001). Moreover, the multimodal DL framework is more consistent than experts (kappa values 0.7605 vs. 0.5800).

conclusionsThe DL models based on CP-EBUS imaging demonstrated an accurate automated tool for diagnosis of the intrathoracic LNs with higher diagnostic efficiency and consistency compared with experts.

Indexed as

convex probe endobronchial ultrasounddeep learninglymph nodesmultimodal imaging

Identifiers

PMID33565422
PMCPMC8544010
OpenAlexW3127252802

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
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.