AIMC Topic: Capsule Endoscopy

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Use of Artificial Intelligence in Lower Gastrointestinal and Small Bowel Disorders: An Update Beyond Polyp Detection.

Journal of clinical gastroenterology
Machine learning and its specialized forms, such as Artificial Neural Networks and Convolutional Neural Networks, are increasingly being used for detecting and managing gastrointestinal conditions. Recent advancements involve using Artificial Neural ...

S2P-Matching: Self-Supervised Patch-Based Matching Using Transformer for Capsule Endoscopic Images Stitching.

IEEE transactions on bio-medical engineering
The Magnetically Controlled Capsule Endoscopy (MCCE) has a limited shooting range, resulting in capturing numerous fragmented images and an inability to precisely locate and examine the region of interest (ROI) as traditional endoscopy can. Addressin...

Reducing reading time and assessing disease in capsule endoscopy videos: A deep learning approach.

International journal of medical informatics
BACKGROUND: The wireless capsule endoscope (CE) is a valuable diagnostic tool in gastroenterology, offering a safe and minimally invasive visualization of the gastrointestinal tract. One of the few drawbacks identified by the gastroenterology communi...

The Use of Artificial Intelligence for Endoscopic Evaluation of the Small Bowel.

Gastrointestinal endoscopy clinics of North America
There remains great potential for widespread implementation of artificial intelligence (AI) in managing small bowel disorders. Studies have shown excellent accuracy in diagnosing various diseases and lesions throughout the small bowel, with most show...

Toward automated small bowel capsule endoscopy reporting using a summarizing machine learning algorithm: The SUM UP study.

Clinics and research in hepatology and gastroenterology
BACKGROUND AND OBJECTIVES: Deep learning (DL) algorithms demonstrate excellent diagnostic performance for the detection of vascular lesions via small bowel (SB) capsule endoscopy (CE), including vascular abnormalities with high (P2), intermediate (P1...

Establishing an AI model and application for automated capsule endoscopy recognition based on convolutional neural networks (with video).

BMC gastroenterology
BACKGROUND: Although capsule endoscopy (CE) is a crucial tool for diagnosing small bowel diseases, the need to process a vast number of images imposes a significant workload on physicians, leading to a high risk of missed diagnoses. This study aims t...

Review of Deep Learning Performance in Wireless Capsule Endoscopy Images for GI Disease Classification.

F1000Research
Wireless capsule endoscopy is a non-invasive medical imaging modality used for diagnosing and monitoring digestive tract diseases. However, the analysis of images obtained from wireless capsule endoscopy is a challenging task, as the images are of lo...

Characterizing the Coefficient of Friction Between a Capsule Robot and the Colon.

IEEE transactions on bio-medical engineering
OBJECTIVE: A capsule robot (CR) with an onboard active locomotion mechanism, has been developed as a promising alternative to colonoscopy due to its minimally-invasive advantage. Predicting the traction force and locomotion resistance of the CR, whic...

AI-KODA score application for cleanliness assessment in video capsule endoscopy frames.

Minimally invasive therapy & allied technologies : MITAT : official journal of the Society for Minimally Invasive Therapy
BACKGROUND: Currently, there is no automated method for assessing cleanliness in video capsule endoscopy (VCE). Our objectives were to automate the process of evaluating and collecting medical scores of VCE frames according to the existing KOrea-Cana...

A new artificial intelligence system for both stomach and small-bowel capsule endoscopy.

Gastrointestinal endoscopy
BACKGROUND AND AIMS: Despite the benefits of artificial intelligence in small-bowel (SB) capsule endoscopy (CE) image reading, information on its application in the stomach and SB CE is lacking.