Evidence map›Paper›PMID 38846970›Full record

ArticleFrontiers in oncology2024

Optical diagnosis in still images of colorectal polyps: comparison between expert endoscopists and PolyDeep, a Computer-Aided Diagnosis system.

Pedro Davila-Piñón, Alba Nogueira-Rodríguez, Astrid Irene Díez-Martín, Laura Codesido, Jesús Herrero, Manuel Puga, Laura Rivas, Eloy Sánchez, Florentino Fdez-Riverola, Daniel Glez-Peña and 3 more

Abstract read
In one paragraph

Article in Frontiers in oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Article
  2. 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

13 authors.

Pedro Davila-PiñónResearch Group in Gastrointestinal Oncology Ourense, Hospital Universitario de Ourense, Ourense, Spain.
Alba Nogueira-RodríguezDepartment of Computer Science, Escuela Superior de Ingenieria Informática (ESEI), CINBIO, University of Vigo, Ourense, Spain.
Astrid Irene Díez-MartínResearch Group in Gastrointestinal Oncology Ourense, Hospital Universitario de Ourense, Ourense, Spain.
Laura CodesidoResearch Group in Gastrointestinal Oncology Ourense, Hospital Universitario de Ourense, Ourense, Spain.
Jesús HerreroResearch Group in Gastrointestinal Oncology Ourense, Hospital Universitario de Ourense, Ourense, Spain.
Manuel PugaResearch Group in Gastrointestinal Oncology Ourense, Hospital Universitario de Ourense, Ourense, Spain.
Laura RivasResearch Group in Gastrointestinal Oncology Ourense, Hospital Universitario de Ourense, Ourense, Spain.
Eloy SánchezResearch Group in Gastrointestinal Oncology Ourense, Hospital Universitario de Ourense, Ourense, Spain.
Florentino Fdez-RiverolaDepartment of Computer Science, Escuela Superior de Ingenieria Informática (ESEI), CINBIO, University of Vigo, Ourense, Spain.
Daniel Glez-PeñaDepartment of Computer Science, Escuela Superior de Ingenieria Informática (ESEI), CINBIO, University of Vigo, Ourense, Spain.
Miguel Reboiro-JatoDepartment of Computer Science, Escuela Superior de Ingenieria Informática (ESEI), CINBIO, University of Vigo, Ourense, Spain.
Hugo López-FernándezDepartment of Computer Science, Escuela Superior de Ingenieria Informática (ESEI), CINBIO, University of Vigo, Ourense, Spain.
Joaquín CubiellaResearch Group in Gastrointestinal Oncology Ourense, Hospital Universitario de Ourense, Ourense, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: PolyDeep is a computer-aided detection and classification (CADe/x) system trained to detect and classify polyps. During colonoscopy, CADe/x systems help endoscopists to predict the histology of colonic lesions. Objective: To compare the diagnostic performance of PolyDeep and expert endoscopists for the optical diagnosis of colorectal polyps on still images. Methods: PolyDeep Image Classification (PIC) is an Results: The diagnostic performance of the endoscopists and PolyDeep in the characterization of neoplasia is similar in terms of sensitivity (PolyDeep: 89.05%; E1: 91.23%, p=0.5; E2: 96.11%, p<0.001; E3: 86.65%, p=0.3; E4: 91.26% p=0.3) and specificity (PolyDeep: 35.53%; E1: 33.80%, p=0.8; E2: 34.72%, p=1; E3: 39.24%, p=0.8; E4: 46.84%, p=0.2). The overall discriminative ability also showed no statistically significant differences (PolyDeep: 0.623; E1: 0.625, p=0.8; E2: 0.654, p=0.2; E3: 0.629, p=0.9; E4: 0.690, p=0.09). In the optical diagnosis of adenomatous polyps, we found that PolyDeep had a significantly higher sensitivity and a significantly lower specificity. The overall discriminative ability of adenomatous lesions by expert endoscopists is significantly higher than PolyDeep (PolyDeep: 0.582; E1: 0.685, p < 0.001; E2: 0.677, p < 0.0001; E3: 0.658, p < 0.01; E4: 0.694, p < 0.0001). Conclusion: PolyDeep and endoscopists have similar diagnostic performance in the optical diagnosis of neoplastic lesions. However, endoscopists have a better global discriminatory ability than PolyDeep in the optical diagnosis of adenomatous polyps.

Indexed as

artificial intelligenceCADe/xcolonoscopycolorectal polypsconvolutional neural networksdeep learningscreening

Identifiers

PMID38846970
PMCPMC11153726

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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.