Making a resect-and-discard strategy work for diminutive colorectal polyps: let's get real

Rex, Douglas K.

doi:10.1055/a-1616-0659

Cited by 6 publications

(7 citation statements)

References 16 publications

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“… 8 The resect-and-discard strategy based on characterization and expected histology is still a subject of debate in several countries outside USA and could confuse many endoscopists. 35 Developers should follow the recent guidelines from ASGE in all cases. 1 …”

Section: Drawbacksmentioning

confidence: 99%

As how artificial intelligence is revolutionizing endoscopy

Rey

2024

Clin Endosc

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With incessant advances in information technology and its implications in all domains of our lives, artificial intelligence (AI) has emerged as a requirement for improved machine performance. This brings forth the query of how this can benefit endoscopists and improve both diagnostic and therapeutic endoscopy in each part of the gastrointestinal tract. Additionally, it also raises the question of the recent benefits and clinical usefulness of this new technology in daily endoscopic practice. There are two main categories of AI systems: computer-assisted detection (CADe) for lesion detection and computer-assisted diagnosis (CADx) for optical biopsy and lesion characterization. Quality assurance is the next step in the complete monitoring of high-quality colonoscopies. In all cases, computer-aided endoscopy is used, as the overall results rely on the physician. Video capsule endoscopy is a unique example in which a computer operates a device, stores multiple images, and performs an accurate diagnosis. While there are many expectations, we need to standardize and assess various software packages. It is important for healthcare providers to support this new development and make its use an obligation in daily clinical practice. In summary, AI represents a breakthrough in digestive endoscopy. Screening for gastric and colonic cancer detection should be improved, particularly outside expert centers. Prospective and multicenter trials are mandatory before introducing new software into clinical practice.

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Section: Drawbacksmentioning

confidence: 99%

As how artificial intelligence is revolutionizing endoscopy

Rey

2024

Clin Endosc

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“…Indeed, it is expected that AI-assisted optical biopsy will not achieve widespread use in clinical practice unless there are financial incentives provided through reimbursement fee codes. 70 In recent studies, AI models with insurance claims data were capable of accurately predicting IBD-related hospitalization and steroid use within a 6-month period in patients with IBD, while AI models with gene-based data outperformed more costly biomarker analyses for predicting outcomes. 71 Additionally, AI confirmation of investigator scoring without the need for central reading may lead to potential cost savings on drug development.…”

Section: Barriers To Ai Implementationmentioning

confidence: 99%

“…Thus, payer requirements for reimbursement will be key to the adoption of AI in health care. Indeed, it is expected that AI-assisted optical biopsy will not achieve widespread use in clinical practice unless there are financial incentives provided through reimbursement fee codes [ 70 ]. In recent studies, AI models with insurance claims data were capable of accurately predicting IBD-related hospitalization and steroid use within a 6-month period in patients with IBD, while AI models with gene-based data outperformed more costly biomarker analyses for predicting outcomes [ 71 ].…”

Section: Barriers To Ai Implementationmentioning

confidence: 99%

Artificial intelligence in inflammatory bowel disease: implications for clinical practice and future directions

Ahmad¹,

East²,

Panaccione³

et al. 2023

Intest Res

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biochemical investigations. [3][4][5] Endoscopic evaluation defines the location and pattern of inflammation (e.g., segmental or continuous, mild or severe) and can also identify noninflammatory pathology (e.g., dysplasia). 3 Endoscopic and histologic improve ments correlate with better outcomes and are treatment targets in the management of IBD. 6,7 Using an inflammatory pathway-based approach to inform treatment decisions may be more useful than classifying disease by organ involvement alone. 8 The complexity of IBD management lends itself to artificial intelligence (AI) as a means of improving clinical practice (Fig. 1). 3 Comprised of machine learning, deep learning, and neural networks, AI can help optimize IBD diagnosis, refine assessment of macroscopic and microscopic disease severity, and improve disease monitoring. [9][10][11]

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“…For colorectal screening, increase in adenoma detection rate has proved decrease post colonoscopy cancer risk but not yet with IA associated gain [3 ▪▪ ]. Resect-and-discard strategy based on characterization and expected histology is still debated in many countries outside USA and could confuses many endoscopists [31 ▪▪ ]. In any cases, developers should follow recent guideline from ASGE [1 ▪▪ ].…”

Section: General Commentsmentioning

confidence: 99%

Artificial intelligence in digestive endoscopy: recent advances

Rey

2023

Current Opinion in Gastroenterology

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Purpose of review With the incessant advances in information technology and its implications in all domains of our life, artificial intelligence (AI) started to emerge as a need for better machine performance. How it can help endoscopists and what are the areas of interest in improving both diagnostic and therapeutic endoscopy in each part of the gastrointestinal (GI) tract. What are the recent benefits and clinical usefulness of this new technology in daily endoscopic practice. Recent Findings The two main AI systems categories are computer-assisted detection ‘CADe’ for lesion detection and computer-assisted diagnosis ‘CADx’ for optical biopsy and lesion characterization. Multiple softwares are now implemented in endoscopy practice. Other AI systems offer therapeutic assistance such as lesion delineation for complete endoscopic resection or prediction of possible lymphanode after endoscopic treatment. Quality assurance is the coming step with complete monitoring of high-quality colonoscopy. In all cases it is a computer-aid endoscopy as the overall result rely on the physician. Video capsule endoscopy is the unique example were the computer conduct the device, store multiple images, and perform accurate diagnosis. Summary AI is a breakthrough in digestive endoscopy. Screening gastric and colonic cancer detection should be improved especially outside of expert's centers. Prospective and multicenter trials are mandatory before introducing new software in clinical practice.

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Making a resect-and-discard strategy work for diminutive colorectal polyps: let's get real

Cited by 6 publications

References 16 publications

As how artificial intelligence is revolutionizing endoscopy

As how artificial intelligence is revolutionizing endoscopy

Artificial intelligence in inflammatory bowel disease: implications for clinical practice and future directions

Artificial intelligence in digestive endoscopy: recent advances

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