ArticleJournal of clinical medicine2019
Artificial Intelligence-Based Classification of Multiple Gastrointestinal Diseases Using Endoscopy Videos for Clinical Diagnosis.
Article in Journal of clinical medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic accuracy of artificial intelligence for detecting gastrointestinal luminal pathologies: A systematic review and meta-analysis.Frontiers in medicine · 2022Pooled it
- YBX1 regulation of alveolar type II epithelial cells in idiopathic pulmonary fibrosis: mechanistic insights and small-molecule drug screening.Journal of translational medicine · 2025Article
- CAS-Colon: A Comprehensive Colonoscopy Anatomical Segmentation Dataset for Artificial Intelligence Development.Scientific data · 2025Article
- A review on computer-aided diagnostic system to classify the disorders of the gastrointestinal tract.European journal of medical research · 2025Review
- A Novel 3D Convolutional Neural Network-Based Deep Learning Model for Spatiotemporal Feature Mapping for Video Analysis: Feasibility Study for Gastrointestinal Endoscopic Video Classification.Journal of imaging · 2025Article
- Article
- Role of Artificial Intelligence in Nanomedicine and Organ-specific Therapy: An Updated Review.Current drug targets · 2025Review
- Diagnosis Based on Population Data versus Personalized Data: The Evolving Paradigm in Laboratory Medicine.Diagnostics (Basel, Switzerland) · 2024Review
- Analysis of Vocal Signatures of COVID-19 in Cough Sounds: A Newer Diagnostic Approach Using Artificial Intelligence.Cureus · 2024Article
- Colonoscopy polyp classification via enhanced scattering wavelet Convolutional Neural Network.PloS one · 2024Article
- Efficient-gastro: optimized EfficientNet model for the detection of gastrointestinal disorders using transfer learning and wireless capsule endoscopy images.PeerJ. Computer science · 2024Article
- Deep Learning in Coeliac Disease: A Systematic Review on Novel Diagnostic Approaches to Disease Diagnosis.Journal of clinical medicine · 2023Review
- Artificial Intelligence Its Uses and Application in Pediatric Dentistry: A Review.Biomedicines · 2023Review
- Comparative study of convolutional neural network architectures for gastrointestinal lesions classification.PeerJ · 2023Article
- Evaluation of artificial intelligence techniques in disease diagnosis and prediction.Discover artificial intelligence · 2023Review
- RETRACTED ARTICLE: Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda.Journal of ambient intelligence and humanized computing · 2023Article
- Simplified Convolutional Neural Network Application for Cervix Type Classification via Colposcopic Images.Bioengineering (Basel, Switzerland) · 2022Article
- Hybrid and Deep Learning Approach for Early Diagnosis of Lower Gastrointestinal Diseases.Sensors (Basel, Switzerland) · 2022Article
- Deep Learning Neural Modelling as a Precise Method in the Assessment of the Chronological Age of Children and Adolescents Using Tooth and Bone Parameters.Sensors (Basel, Switzerland) · 2022Article
- Feature preserving mesh network for semantic segmentation of retinal vasculature to support ophthalmic disease analysis.Frontiers in medicine · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Various techniques using artificial intelligence (AI) have resulted in a significant contribution to field of medical image and video-based diagnoses, such as radiology, pathology, and endoscopy, including the classification of gastrointestinal (GI) diseases. Most previous studies on the classification of GI diseases use only spatial features, which demonstrate low performance in the classification of multiple GI diseases. Although there are a few previous studies using temporal features based on a three-dimensional convolutional neural network, only a specific part of the GI tract was involved with the limited number of classes. To overcome these problems, we propose a comprehensive AI-based framework for the classification of multiple GI diseases by using endoscopic videos, which can simultaneously extract both spatial and temporal features to achieve better classification performance. Two different residual networks and a long short-term memory model are integrated in a cascaded mode to extract spatial and temporal features, respectively. Experiments were conducted on a combined dataset consisting of one of the largest endoscopic videos with 52,471 frames. The results demonstrate the effectiveness of the proposed classification framework for multi-GI diseases. The experimental results of the proposed model (97.057% area under the curve) demonstrate superior performance over the state-of-the-art methods and indicate its potential for clinical applications.
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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.