Evidence map›Paper›PMID 33591447›Full record

ArticleSurgical endoscopy2022

A novel machine learning-based algorithm to identify and classify lesions and anatomical landmarks in colonoscopy images.

Ying-Chun Jheng, Yen-Po Wang, Hung-En Lin, Kuang-Yi Sung, Yuan-Chia Chu, Huann-Sheng Wang, Jeng-Kai Jiang, Ming-Chih Hou, Fa-Yauh Lee, Ching-Liang Lu

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Article in Surgical endoscopy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–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

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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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

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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

10 authors.

Ying-Chun Jheng *Endoscopy Center for Diagnosis and Treatment, Taipei Veterans General Hospital, Taipei, Taiwan.
Yen-Po Wang *Endoscopy Center for Diagnosis and Treatment, Taipei Veterans General Hospital, Taipei, Taiwan.
Hung-En LinEndoscopy Center for Diagnosis and Treatment, Taipei Veterans General Hospital, Taipei, Taiwan.
Kuang-Yi SungEndoscopy Center for Diagnosis and Treatment, Taipei Veterans General Hospital, Taipei, Taiwan.
Yuan-Chia ChuInformation Management Office, Taipei Veterans General Hospital, Taipei, Taiwan.
Huann-Sheng WangEndoscopy Center for Diagnosis and Treatment, Taipei Veterans General Hospital, Taipei, Taiwan.
Jeng-Kai JiangDivision of Colon and Rectum Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan.
Ming-Chih HouEndoscopy Center for Diagnosis and Treatment, Taipei Veterans General Hospital, Taipei, Taiwan.
Fa-Yauh LeeDivision of Gastroenterology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.
Ching-Liang LuEndoscopy Center for Diagnosis and Treatment, Taipei Veterans General Hospital, Taipei, Taiwan. cllu@ym.edu.tw.ORCID 0000-0001-9335-7241

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesComputer-aided diagnosis (CAD)-based artificial intelligence (AI) has been shown to be highly accurate for detecting and characterizing colon polyps. However, the application of AI to identify normal colon landmarks and differentiate multiple colon diseases has not yet been established. We aimed to develop a convolutional neural network (CNN)-based algorithm (GUTAID) to recognize different colon lesions and anatomical landmarks.

methodsColonoscopic images were obtained to train and validate the AI classifiers. An independent dataset was collected for verification. The architecture of GUTAID contains two major sub-models: the Normal, Polyp, Diverticulum, Cecum and CAncer (NPDCCA) and Narrow-Band Imaging for Adenomatous/Hyperplastic polyps (NBI-AH) models. The development of GUTAID was based on the 16-layer Visual Geometry Group (VGG16) architecture and implemented on Google Cloud Platform.

resultsIn total, 7838 colonoscopy images were used for developing and validating the AI model. An additional 1273 images were independently applied to verify the GUTAID. The accuracy for GUTAID in detecting various colon lesions/landmarks is 93.3% for polyps, 93.9% for diverticula, 91.7% for cecum, 97.5% for cancer, and 83.5% for adenomatous/hyperplastic polyps.

conclusionsA CNN-based algorithm (GUTAID) to identify colonic abnormalities and landmarks was successfully established with high accuracy. This GUTAID system can further characterize polyps for optical diagnosis. We demonstrated that AI classification methodology is feasible to identify multiple and different colon diseases.

Indexed as

Artificial IntelligenceColonic PolypsAlgorithmsColonoscopyHumansMachine LearningArtificial intelligenceColon diseasesColonoscopyComputer-aided diagnosis systemconvolution neural networkHeat map

Identifiers

PMID33591447

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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.