External validation of blue light imaging (BLI) criteria for the optical characterization of colorectal polyps by endoscopy experts

Desai, Madhav; Kennedy, Kevin F.; Aihara, Hiroyuki; Dam, Jacques Van; Gross, Seth A.; Haber, Gregory B.; Pohl, Heiko; Rex, Douglas K.; Saltzman, John R.; Sethi, Amrita; Waxman, Irving; Wang, Kenneth; Wallace, Michael B.; Repici, Alessandro

doi:10.1111/jgh.15529

Cited by 8 publications

(6 citation statements)

References 14 publications

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“…In another study, endoscopy experts identified, with the use of the BASIC system, conventional adenoma (vs. non-adenoma) with 94.4% accuracy, 95% sensitivity, 93.8% specificity, 93.8% positive predictive value, and 94.9% negative predictive value. Moreover, the study validated the use of the BASIC classification system to characterize colorectal polyps with >90% accuracy and a high level of interobserver agreement [32].…”

Section: Blue Laser Imaging (Bli)mentioning

confidence: 64%

From Staining Techniques to Artificial Intelligence: A Review of Colorectal Polyps Characterization

Khalaf,

Fujiyoshi,

Spadaccini

et al. 2024

Medicina

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This review article provides a comprehensive overview of the evolving techniques in image-enhanced endoscopy (IEE) for the characterization of colorectal polyps, and the potential of artificial intelligence (AI) in revolutionizing the diagnostic accuracy of endoscopy. We discuss the historical use of dye-spray and virtual chromoendoscopy for the characterization of colorectal polyps, which are now being replaced with more advanced technologies. Specifically, we focus on the application of AI to create a “virtual biopsy” for the detection and characterization of colorectal polyps, with potential for replacing histopathological diagnosis. The incorporation of AI has the potential to provide an evolutionary learning system that aids in the diagnosis and management of patients with the best possible outcomes. A detailed analysis of the literature supporting AI-assisted diagnostic techniques for the detection and characterization of colorectal polyps, with a particular emphasis on AI’s characterization mechanism, is provided. The benefits of AI over traditional IEE techniques, including the reduction in human error in diagnosis, and its potential to provide an accurate diagnosis with similar accuracy to the gold standard are presented. However, the need for large-scale testing of AI in clinical practice and the importance of integrating patient data into the diagnostic process are acknowledged. In conclusion, the constant evolution of IEE technology and the potential for AI to revolutionize the field of endoscopy in the future are presented.

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Section: Blue Laser Imaging (Bli)mentioning

confidence: 64%

From Staining Techniques to Artificial Intelligence: A Review of Colorectal Polyps Characterization

Khalaf,

Fujiyoshi,

Spadaccini

et al. 2024

Medicina

Self Cite

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“…This requires that in the context of suspected rectosigmoid hyperplastic polyps that are 5 mm or smaller, the technology should provide a negative predictive value (NPV) greater than 90%, when used with high confidence, for adenomatous histology. (45) Examples of IEE classification include the Kudo pit pattern, (41) Sano, (46) NBI International Colorectal Endoscopic, (47) Japan NBI Expert Team (48) and BLI Adenoma Serrated International Classification (49) systems.…”

Section: Computer-aided Diagnosis For Polyp Characterisation During C...mentioning

confidence: 99%

Artificial intelligence-assisted colonoscopy: a narrative review of current data and clinical applications

Li¹,

Lm²

2022

smedj

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Colonoscopy is the reference standard procedure for the prevention and diagnosis of colorectal cancer, which is a leading cause of cancer-related deaths in Singapore. Artificial intelligence systems are automated, objective and reproducible. Artificial intelligence-assisted colonoscopy has recently been introduced into clinical practice as a clinical decision support tool. This review article provides a summary of the current published data and discusses ongoing research and current clinical applications of artificial intelligence-assisted colonoscopy.

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“…Blue light imaging (BLI) is form of digitally enhanced imaging which concentrates and enhances a specific wavelength of light between 410-450 nm, increasing the contrast of superficial micro-vessels and mucosal surface structures (Figure 2 ). BLI uses four independent light-emitting diodes rather than the xenon light used in NBI, which is postulated to improve brightness[ 88 ]. This new technology has not been as extensively studied, however a video-based 2015 study demonstrated improved visibility scores with BLI bright mode compared to WLI according to both expert and non-expert proceduralists[ 89 ].…”

Section: Polyp Detectionmentioning

confidence: 99%

Mucosal imaging in colon polyps: New advances and what the future may hold

Young¹,

Rajandran²,

Philpott³

et al. 2022

World J Gastroenterol

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An expanding range of advanced mucosal imaging technologies have been developed with the goal of improving the detection and characterization of lesions in the gastrointestinal tract. Many technologies have targeted colorectal neoplasia given the potential for intervention prior to the development of invasive cancer in the setting of widespread surveillance programs. Improvement in adenoma detection reduces miss rates and prevents interval cancer development. Advanced imaging technologies aim to enhance detection without significantly increasing procedural time. Accurate polyp characterisation guides resection techniques for larger polyps, as well as providing the platform for the “resect and discard” and “do not resect” strategies for small and diminutive polyps. This review aims to collate and summarise the evidence regarding these technologies to guide colonoscopic practice in both interventional and non-interventional endoscopists.

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External validation of blue light imaging (BLI) criteria for the optical characterization of colorectal polyps by endoscopy experts

Cited by 8 publications

References 14 publications

From Staining Techniques to Artificial Intelligence: A Review of Colorectal Polyps Characterization

From Staining Techniques to Artificial Intelligence: A Review of Colorectal Polyps Characterization

Artificial intelligence-assisted colonoscopy: a narrative review of current data and clinical applications

Mucosal imaging in colon polyps: New advances and what the future may hold

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