ReviewFrontiers in surgery2022
Artificial intelligence in clinical endoscopy: Insights in the field of videomics.
Review in Frontiers in surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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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.
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Who cites it
12 citing papers in PubMed, 27 citations in OpenAlex.
- Deep Learning for Automatic Segmentation of Pituitary Adenomas: A Videomics Study.Operative neurosurgery (Hagerstown, Md.) · 2026Article
- Application of Digital Medicine to the Diagnosis and Treatment of Head and Neck Tumors.Cancer reports (Hoboken, N.J.) · 2026Review
- A Deep-Learning Approach for Vocal Fold Pose Estimation in Videoendoscopy.Journal of imaging informatics in medicine · 2026Article
- Artificial intelligence classification of rectal neoplasia by endoscopic fluorescence perfusion analysis.Scientific reports · 2026Article
- Prediction of loss of heterozygosity in oral cavity dysplasia through vascular pattern.Frontiers in oral health · 2026Article
- Perpendicular Vascular Changes in NBI-CE of Laryngeal Lesions: Diagnostic Accuracy, Reproducibility, and Common Pitfalls.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial intelligence for automatic detection and segmentation of nasal polyposis: a pilot study.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2024Article
- Clinical significance of type IV vascularization of laryngeal lesions according to the Ni classification.Frontiers in oncology · 2024Article
- Deep learning in endoscopy: the importance of standardisation.Acta otorhinolaryngologica Italica : organo ufficiale della Societa italiana di otorinolaringologia e chirurgia cervico-facciale · 2023Article
- Contact Endoscopy - Narrow Band Imaging (CE-NBI) data set for laryngeal lesion assessment.Scientific data · 2023Article
- Instance segmentation of upper aerodigestive tract cancer: site-specific outcomes.Acta otorhinolaryngologica Italica : organo ufficiale della Societa italiana di otorinolaringologia e chirurgia cervico-facciale · 2023Article
- Review
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence is being increasingly seen as a useful tool in medicine. Specifically, these technologies have the objective to extract insights from complex datasets that cannot easily be analyzed by conventional statistical methods. While promising results have been obtained for various -omics datasets, radiological images, and histopathologic slides, analysis of videoendoscopic frames still represents a major challenge. In this context, videomics represents a burgeoning field wherein several methods of computer vision are systematically used to organize unstructured data from frames obtained during diagnostic videoendoscopy. Recent studies have focused on five broad tasks with increasing complexity: quality assessment of endoscopic images, classification of pathologic and nonpathologic frames, detection of lesions inside frames, segmentation of pathologic lesions, and in-depth characterization of neoplastic lesions. Herein, we present a broad overview of the field, with a focus on conceptual key points and future perspectives.
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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.