Video Capsule Endoscopy (VCE) is a diagnostic imaging technology, based on capsule with a built-in camera, that enables screening of the gastro-intestinal tract by reducing the invasiveness of traditional endoscopy procedures. Despite VCE has been designed mainly for investigations on small intestine, it is a powerful tool, because of its low invasiveness and usage simplicity, for supporting large scale screening. However, each VCE video is typically long about eight hours, and endoscopists usually take about two hours, using simple computing methods, for its analysis, thus limiting its application for large scale studies. In this paper, we propose a novel computational framework leveraging the recent advances in artificial intelligence based on the deep learning paradigm to support effectively the whole screening procedure from video transmission to automated lesion identification to reporting. More specifically, our approach handles multiple video uploads at the same time, processes them automatically with the objective of identifying key video frames with potential lesions (for subsequent analysis by endoscopists) and provides physicians with means to compare the findings with either previously detected lesions or with images and scientific information from relevant retrieved documents for a more accurate final diagnosis.

An AI-based Framework for Supporting Large Scale Automated Analysis of Video Capsule Endoscopy

Giordano D;Murabito F;Palazzo S;Pino C;Spampinato C
2019-01-01

Abstract

Video Capsule Endoscopy (VCE) is a diagnostic imaging technology, based on capsule with a built-in camera, that enables screening of the gastro-intestinal tract by reducing the invasiveness of traditional endoscopy procedures. Despite VCE has been designed mainly for investigations on small intestine, it is a powerful tool, because of its low invasiveness and usage simplicity, for supporting large scale screening. However, each VCE video is typically long about eight hours, and endoscopists usually take about two hours, using simple computing methods, for its analysis, thus limiting its application for large scale studies. In this paper, we propose a novel computational framework leveraging the recent advances in artificial intelligence based on the deep learning paradigm to support effectively the whole screening procedure from video transmission to automated lesion identification to reporting. More specifically, our approach handles multiple video uploads at the same time, processes them automatically with the objective of identifying key video frames with potential lesions (for subsequent analysis by endoscopists) and provides physicians with means to compare the findings with either previously detected lesions or with images and scientific information from relevant retrieved documents for a more accurate final diagnosis.
2019
978-1-7281-0848-3
File in questo prodotto:
File Dimensione Formato  
08834577-An AI-based Framework for Supporting Large Scale Automated.pdf

solo gestori archivio

Tipologia: Versione Editoriale (PDF)
Dimensione 173.87 kB
Formato Adobe PDF
173.87 kB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/373619
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 5
  • ???jsp.display-item.citation.isi??? 3
social impact