ArticlePloS one2020
Artificial intelligence that determines the clinical significance of capsule endoscopy images can increase the efficiency of reading.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Recent technological advances in video capsule endoscopy: a comprehensive review.Clinical endoscopy · 2026Review
- Deep learning-based localization and lesion detection in capsule endoscopy for patients with suspected small-bowel bleeding.World journal of gastroenterology · 2025Article
- Reducing the workload of medical diagnosis through artificial intelligence: A narrative review.Medicine · 2025Review
- The utility of artificial intelligence in visualization of pediatric gastrointestinal mucosa.Frontiers in pediatrics · 2025Review
- Advances in pediatric video capsule endoscopy: current applications and future directions.Frontiers in pediatrics · 2025Review
- Impact of human and artificial intelligence collaboration on workload reduction in medical image interpretation.NPJ digital medicine · 2024Article
- Development and validation of a deep learning system for detection of small bowel pathologies in capsule endoscopy: a pilot study in a Singapore institution.Singapore medical journal · 2024Article
- Reading of small bowel capsule endoscopy after frame reduction using an artificial intelligence algorithm.BMC gastroenterology · 2024Article
- Wireless capsule endoscopy multiclass classification using three-dimensional deep convolutional neural network model.Biomedical engineering online · 2023Article
- Automatic Classification of GI Organs in Wireless Capsule Endoscopy Using a No-Code Platform-Based Deep Learning Model.Diagnostics (Basel, Switzerland) · 2023Article
- Small Bowel Detection for Wireless Capsule Endoscopy Using Convolutional Neural Networks with Temporal Filtering.Diagnostics (Basel, Switzerland) · 2022Article
- Development of a Deep-Learning Algorithm for Small Bowel-Lesion Detection and a Study of the Improvement in the False-Positive Rate.Journal of clinical medicine · 2022Article
- Artificial Intelligence in Endoscopy.Digestive diseases and sciences · 2022Review
- Automated evaluation of colon capsule endoscopic severity of ulcerative colitis using ResNet50.PloS one · 2022Article
- Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology.World journal of gastroenterology · 2021Review
- Artificial Intelligence in Capsule Endoscopy: A Practical Guide to Its Past and Future Challenges.Diagnostics (Basel, Switzerland) · 2021Review
- A Current and Newly Proposed Artificial Intelligence Algorithm for Reading Small Bowel Capsule Endoscopy.Diagnostics (Basel, Switzerland) · 2021Review
- Development and Verification of a Deep Learning Algorithm to Evaluate Small-Bowel Preparation Quality.Diagnostics (Basel, Switzerland) · 2021Article
- Automatic lumen detection and magnetic alignment control for magnetic-assisted capsule colonoscope system optimization.Scientific reports · 2021Article
- COVID-19 and the challenges faced by gastroenterology trainees: time for capsule endoscopy training?Frontline gastroenterology · 2021Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI), which has demonstrated outstanding achievements in image recognition, can be useful for the tedious capsule endoscopy (CE) reading. We aimed to develop a practical AI-based method that can identify various types of lesions and tried to evaluate the effectiveness of the method under clinical settings. A total of 203,244 CE images were collected from multiple centers selected considering the regional distribution. The AI based on the Inception-Resnet-V2 model was trained with images that were classified into two categories according to their clinical significance. The performance of AI was evaluated with a comparative test involving two groups of reviewers with different experiences. The AI summarized 67,008 (31.89%) images with a probability of more than 0.8 for containing lesions in 210,100 frames of 20 selected CE videos. Using the AI-assisted reading model, reviewers in both the groups exhibited increased lesion detection rates compared to those achieved using the conventional reading model (experts; 34.3%-73.0%; p = 0.029, trainees; 24.7%-53.1%; p = 0.029). The improved result for trainees was comparable to that for the experts (p = 0.057). Further, the AI-assisted reading model significantly shortened the reading time for trainees (1621.0-746.8 min; p = 0.029). Thus, we have developed an AI-assisted reading model that can detect various lesions and can successfully summarize CE images according to clinical significance. The assistance rendered by AI can increase the lesion detection rates of reviewers. Especially, trainees could improve their efficiency of reading as a result of reduced reading time using the AI-assisted model.
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