ArticleDiagnostics (Basel, Switzerland)2021
Development and Verification of a Deep Learning Algorithm to Evaluate Small-Bowel Preparation Quality.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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.
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.
Who cites it
12 citing papers in PubMed.
- Automatic Evaluation of Small Bowel Cleanliness in Capsule Endoscopy: A Comparative Study of Four Preparations using Artificial Intelligence.Endoscopy international open · 2026Article
- Expected Values of Artificial Intelligence in Colon Capsule Endoscopy: Artificial Intelligence in Capsule Endoscopy Consortium Position Statement.Endoscopy international open · 2026Article
- AI-Assisted Evaluation of Colon Cleanliness in Capsule Endoscopy Videos.Diagnostics (Basel, Switzerland) · 2025Article
- Unmet Needs of Artificial Intelligence in Small Bowel Capsule Endoscopy.Diagnostics (Basel, Switzerland) · 2025Review
- A multi-label dataset and its evaluation for automated scoring system for cleanliness assessment in video capsule endoscopy.Physical and engineering sciences in medicine · 2024Article
- Reading of small bowel capsule endoscopy after frame reduction using an artificial intelligence algorithm.BMC gastroenterology · 2024Article
- AI-Driven Colon Cleansing Evaluation in Capsule Endoscopy: A Deep Learning Approach.Diagnostics (Basel, Switzerland) · 2023Article
- Design of a Convolutional Neural Network as a Deep Learning Tool for the Automatic Classification of Small-Bowel Cleansing in Capsule Endoscopy.Medicina (Kaunas, Lithuania) · 2023Article
- A systematic review on diagnosis and treatment of gastrointestinal diseases by magnetically controlled capsule endoscopy and artificial intelligence.Therapeutic advances in gastroenterology · 2023Review
- Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions.NPJ digital medicine · 2022Review
- Small bowel cleanliness in capsule endoscopy: a case-control study using validated artificial intelligence algorithm.Scientific reports · 2022Article
- Diagnostic Yields and Clinical Impacts of Capsule Endoscopy.Diagnostics (Basel, Switzerland) · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Capsule endoscopy (CE) quality control requires an objective scoring system to evaluate the preparation of the small bowel (SB). We propose a deep learning algorithm to calculate SB cleansing scores and verify the algorithm's performance. A 5-point scoring system based on clarity of mucosal visualization was used to develop the deep learning algorithm (400,000 frames; 280,000 for training and 120,000 for testing). External validation was performed using additional CE cases (
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What OpenQuestion holds
Registered trials
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.