Colon capsule endoscopy and artificial intelligence: a perfect match for panendoscopy

Ribeiro, Tiago; Fernández-Urién, Ignacio; Cardoso, Hélder

doi:10.1016/b978-0-323-99647-1.00007-1

Cited by 2 publications

(3 citation statements)

References 48 publications

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“…The introduction of dual-camera capsule has prompted to discussion a CE-based panendoscopic evaluation of the digestive tract, especially for colorectal cancer screening and for Crohn’s assessment 8 9 . It would be beneficial if CE allowed for complete assessment of the whole digestive tract, excluding all potential vascular lesions while avoiding repeat exams.…”

Section: Introductionmentioning

confidence: 99%

“…It would be beneficial if CE allowed for complete assessment of the whole digestive tract, excluding all potential vascular lesions while avoiding repeat exams. Nonetheless, it is associated with an even greater reading burden which, along with its significant cost and lack of experience in the majority of centers, may limit its use in clinical practice, mainly in low-volume ones 9 .…”

Section: Introductionmentioning

confidence: 99%

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Deep learning and capsule endoscopy: Automatic multi-brand and multi-device panendoscopic detection of vascular lesions

Mascarenhas,

Martins,

Afonso

et al. 2024

Endosc Int Open

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Introduction: Capsule endoscopy (CE) is commonly used as the initial exam for suspected mid-gastrointestinal bleeding after normal upper and lower endoscopy. Although the assessment of the small bowel is the primary focus of CE, detecting upstream or downstream vascular lesions may also be clinically significant. This study aimed to develop and test a Convolutional Neural Network (CNN)-based model for panendoscopic automatic detection of vascular lesions during CE. Methods: A multicentric AI model development study was based on 1022 CE exams. Our group used 34655 frames from seven types of CE devices, of which 11091 were considered to have vascular lesions (angiectasia or varices) after triple validation. We divided data into a training and a validation set, and the latter was used to evaluate the model’s performance. At the time of division, all frames from a given patient were assigned to the same dataset. Our primary outcome measures were sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and an area under the precision-recall curve (AUC-PR). Results: Sensitivity and specificity were 86.4% and 98.3%, respectively. PPV was 95.2%, while the NPV was 95.0%. Overall accuracy was 95.0%. The AUC-PR value was 0.96. The CNN processed 115 frames per second. Conclusion: This is the first proof-of-concept AI deep learning model developed for pan-endoscopic automatic detection of vascular lesions during CE. The diagnostic performance of this CNN in multi-brand devices addresses an essential issue of technological interoperability, allowing it to be replicated in multiple technological settings.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep learning and capsule endoscopy: Automatic multi-brand and multi-device panendoscopic detection of vascular lesions

Mascarenhas,

Martins,

Afonso

et al. 2024

Endosc Int Open

Self Cite

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“…Indeed, an increasing number of CNN-based models are being designed to detect different lesions or conditions in the digestive system through the automated analysis of endoscopy images [9][10][11][12][13][14][15][16]. Moreover, the use of these applications in conjunction with Capsule Endoscopy (CE) is paving the way towards greater automation of a range of endoscopy examinations, including panendoscopy studies [17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Smart Endoscopy Is Greener Endoscopy: Leveraging Artificial Intelligence and Blockchain Technologies to Drive Sustainability in Digestive Health Care

Mascarenhas,

Ribeiro,

Afonso

et al. 2023

Diagnostics

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The surge in the implementation of artificial intelligence (AI) in recent years has permeated many aspects of our life, and health care is no exception. Whereas this technology can offer clear benefits, some of the problems associated with its use have also been recognised and brought into question, for example, its environmental impact. In a similar fashion, health care also has a significant environmental impact, and it requires a considerable source of greenhouse gases. Whereas efforts are being made to reduce the footprint of AI tools, here, we were specifically interested in how employing AI tools in gastroenterology departments, and in particular in conjunction with capsule endoscopy, can reduce the carbon footprint associated with digestive health care while offering improvements, particularly in terms of diagnostic accuracy. We address the different ways that leveraging AI applications can reduce the carbon footprint associated with all types of capsule endoscopy examinations. Moreover, we contemplate how the incorporation of other technologies, such as blockchain technology, into digestive health care can help ensure the sustainability of this clinical speciality and by extension, health care in general.

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Colon capsule endoscopy and artificial intelligence: a perfect match for panendoscopy

Cited by 2 publications

References 48 publications

Deep learning and capsule endoscopy: Automatic multi-brand and multi-device panendoscopic detection of vascular lesions

Deep learning and capsule endoscopy: Automatic multi-brand and multi-device panendoscopic detection of vascular lesions

Smart Endoscopy Is Greener Endoscopy: Leveraging Artificial Intelligence and Blockchain Technologies to Drive Sustainability in Digestive Health Care

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