ArticleJournal of personalized medicine2022
No-Code Platform-Based Deep-Learning Models for Prediction of Colorectal Polyp Histology from White-Light Endoscopy Images: Development and Performance Verification.
Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Development of a one-step office hysteroscopic lightweight artificial intelligence application for subfertility risk stratification of intrauterine adhesions.Fundamental research · 2026Article
- Edge Artificial Intelligence Device in Real-Time Endoscopy for the Classification of Colonic Neoplasms.Diagnostics (Basel, Switzerland) · 2025Article
- Advances in artificial intelligence and computer science for computer-aided diagnosis of colorectal polyps: current status.Endoscopy international open · 2023Review
- Automatic Classification of GI Organs in Wireless Capsule Endoscopy Using a No-Code Platform-Based Deep Learning Model.Diagnostics (Basel, Switzerland) · 2023Article
- Detection of Colorectal Polyps from Colonoscopy Using Machine Learning: A Survey on Modern Techniques.Sensors (Basel, Switzerland) · 2023Review
- Impact of the Volume and Distribution of Training Datasets in the Development of Deep-Learning Models for the Diagnosis of Colorectal Polyps in Endoscopy Images.Journal of personalized medicine · 2022Article
- Real-time deep-learning NICE classification and withdrawal-speed monitoring for colorectal endoscopy.Digital healthArticle
- A User-friendly Approach for the Diagnosis of Diabetic Retinopathy Using ChatGPT and Automated Machine Learning.Ophthalmology scienceArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundThe authors previously developed deep-learning models for the prediction of colorectal polyp histology (advanced colorectal cancer, early cancer/high-grade dysplasia, tubular adenoma with or without low-grade dysplasia, or non-neoplasm) from endoscopic images. While the model achieved 67.3% internal-test accuracy and 79.2% external-test accuracy, model development was labour-intensive and required specialised programming expertise. Moreover, the 240-image external-test dataset included only three advanced and eight early cancers, so it was difficult to generalise model performance. These limitations may be mitigated by deep-learning models developed using no-code platforms.
objectiveTo establish no-code platform-based deep-learning models for the prediction of colorectal polyp histology from white-light endoscopy images and compare their diagnostic performance with traditional models.
methodsThe same 3828 endoscopic images used to establish previous models were used to establish new models based on no-code platforms Neuro-T, VLAD, and Create ML-Image Classifier. A prospective multicentre validation study was then conducted using 3818 novel images. The primary outcome was the accuracy of four-category prediction.
resultsThe model established using Neuro-T achieved the highest internal-test accuracy (75.3%, 95% confidence interval: 71.0-79.6%) and external-test accuracy (80.2%, 76.9-83.5%) but required the longest training time. In contrast, the model established using Create ML-Image Classifier required only 3 min for training and still achieved 72.7% (70.8-74.6%) external-test accuracy. Attention map analysis revealed that the imaging features used by the no-code deep-learning models were similar to those used by endoscopists during visual inspection.
conclusionNo-code deep-learning tools allow for the rapid development of models with high accuracy for predicting colorectal polyp histology.
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Registered trials
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