Comment on “Artificial intelligence in gastroenterology: A state-of-the-art review”

Bjørsum-Meyer, Thomas; Koulaouzidis, Anastasios; Baatrup, Gunnar

doi:10.3748/wjg.v28.i16.1722

Cited by 6 publications

(6 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These improvements in commonly used performance metrics have shown not to be affected by factors known to influence detection by the human eye, including the size and morphology of the lesions [ 19 ]. Artificial intelligence is expected to play a major role in improving the acceptability and the diagnostic yield of CCE [ 20 ]. These systems may help in several steps of the CCE process, from predicting the quality of colon cleanliness, lesion detection and the distinction of colorectal lesions [ 20 , 21 , 22 ].…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Performance of a Deep Learning System for Automatic Diagnosis of Protruding Lesions in Colon Capsule Endoscopy

et al. 2022

View full text Add to dashboard Cite

Background: Colon capsule endoscopy (CCE) is an alternative for patients unwilling or with contraindications for conventional colonoscopy. Colorectal cancer screening may benefit greatly from widespread acceptance of a non-invasive tool such as CCE. However, reviewing CCE exams is a time-consuming process, with risk of overlooking important lesions. We aimed to develop an artificial intelligence (AI) algorithm using a convolutional neural network (CNN) architecture for automatic detection of colonic protruding lesions in CCE images. An anonymized database of CCE images collected from a total of 124 patients was used. This database included images of patients with colonic protruding lesions or patients with normal colonic mucosa or with other pathologic findings. A total of 5715 images were extracted for CNN development. Two image datasets were created and used for training and validation of the CNN. The AUROC for detection of protruding lesions was 0.99. The sensitivity, specificity, PPV and NPV were 90.0%, 99.1%, 98.6% and 93.2%, respectively. The overall accuracy of the network was 95.3%. The developed deep learning algorithm accurately detected protruding lesions in CCE images. The introduction of AI technology to CCE may increase its diagnostic accuracy and acceptance for screening of colorectal neoplasia.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Artificial intelligence is expected to play a major role in improving the acceptability and the diagnostic yield of CCE [ 20 ]. These systems may help in several steps of the CCE process, from predicting the quality of colon cleanliness, lesion detection and the distinction of colorectal lesions [ 20 , 21 , 22 ].…”

Section: Discussionmentioning

confidence: 99%

Performance of a Deep Learning System for Automatic Diagnosis of Protruding Lesions in Colon Capsule Endoscopy

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In general, the AI tools currently under development for digestive health care mainly aim to be assistive, guiding the clinical decision-making process. Many of the initial efforts have centred on automating the detection and diagnosis of colorectal neoplasia, highlighting the potential of AI applied to CE to identify and characterise lesions in colorectal cancer screening [60][61][62]. The initial explorations of AI algorithms to identify colorectal neoplasia in CE images had relatively modest sensitivity [63].…”

Section: Improved Efficiency Of Gastrointestinal Examinationsmentioning

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

View full text Add to dashboard Cite

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.

show abstract

“…However, following the impact of the recent COVID pandemic, an NHS Scotland evaluation demonstrated that the technology could lead to relieving the backlog in endoscopy on a national level. Still, CCE generates a video containing more than 50,000 images; this could be time-consuming and inefficient to analyse [ 2 , 3 ]. As a result, the advances in AI application on image analysis make AI-assisted CCE video analysis one of the most active research areas.…”

Section: Introductionmentioning

confidence: 99%

Clinicians’ Guide to Artificial Intelligence in Colon Capsule Endoscopy—Technology Made Simple

Lei

Nia

White³

et al. 2023

Diagnostics

Self Cite

View full text Add to dashboard Cite

Artificial intelligence (AI) applications have become widely popular across the healthcare ecosystem. Colon capsule endoscopy (CCE) was adopted in the NHS England pilot project following the recent COVID pandemic’s impact. It demonstrated its capability to relieve the national backlog in endoscopy. As a result, AI-assisted colon capsule video analysis has become gastroenterology’s most active research area. However, with rapid AI advances, mastering these complex machine learning concepts remains challenging for healthcare professionals. This forms a barrier for clinicians to take on this new technology and embrace the new era of big data. This paper aims to bridge the knowledge gap between the current CCE system and the future, fully integrated AI system. The primary focus is on simplifying the technical terms and concepts in machine learning. This will hopefully address the general “fear of the unknown in AI” by helping healthcare professionals understand the basic principle of machine learning in capsule endoscopy and apply this knowledge in their future interactions and adaptation to AI technology. It also summarises the evidence of AI in CCE and its impact on diagnostic pathways. Finally, it discusses the unintended consequences of using AI, ethical challenges, potential flaws, and bias within clinical settings.

show abstract

Comment on “Artificial intelligence in gastroenterology: A state-of-the-art review”

Cited by 6 publications

References 8 publications

Performance of a Deep Learning System for Automatic Diagnosis of Protruding Lesions in Colon Capsule Endoscopy

Performance of a Deep Learning System for Automatic Diagnosis of Protruding Lesions in Colon Capsule Endoscopy

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

Clinicians’ Guide to Artificial Intelligence in Colon Capsule Endoscopy—Technology Made Simple

Contact Info

Product

Resources

About