Artificial intelligence for colonoscopic polyp detection: High performance versus human nature

East, James E.; Rittscher, Jens

doi:10.1111/jgh.15262

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…1 ADR is a function of mucosal visibility (which depends on adequacy of bowel preparation, and irrigation and suctioning during colonoscopy), meticulous examination of the entire visible colonic mucosa (careful slow withdrawal of the endoscope, adequate air insufflation, pressing down and looking behind folds, and re-examination of flexures), and the ability of the endoscopist to recognize flat subtle lesions on the exposed mucosal surface. 2 The myriad endoscopic tools available to improve adenoma detection address different aspects of the examination process during colonoscopy. 3 Distal attachments help to flatten mucosal folds and can expose adenomas hidden by folds.…”

Section: Image-enhanced Endoscopy For Detection and Diagnosis Of Colonic Neoplasia: Time To Shift Focusmentioning

confidence: 99%

“…The impact of, and interaction with, artificial intelligence (AI) in this space of adenoma detection and diagnosis, must also be considered. 2,6 In the study by Miyaguchi et al, investigators cannot be blinded to the modality of imaging, even though it was randomly assigned. There is a risk of performance bias in that a new tool effect comes into play, with the endoscopist subconsciously trying harder with a new device.…”

Section: Image-enhanced Endoscopy For Detection and Diagnosis Of Colonic Neoplasia: Time To Shift Focusmentioning

confidence: 99%

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Image‐enhanced endoscopy for detection and diagnosis of colonic neoplasia: Time to shift focus

East

2021

J of Gastro and Hepatol

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Section: Image-enhanced Endoscopy For Detection and Diagnosis Of Colonic Neoplasia: Time To Shift Focusmentioning

confidence: 99%

Section: Image-enhanced Endoscopy For Detection and Diagnosis Of Colonic Neoplasia: Time To Shift Focusmentioning

confidence: 99%

Image‐enhanced endoscopy for detection and diagnosis of colonic neoplasia: Time to shift focus

East

2021

J of Gastro and Hepatol

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Deep learning vs conventional learning algorithms for clinical prediction in Crohn's disease: A proof-of-concept study

Con¹,

Langenberg²,

Vasudevan³

2021

WJG

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BACKGROUND Traditional methods of developing predictive models in inflammatory bowel diseases (IBD) rely on using statistical regression approaches to deriving clinical scores such as the Crohn's disease (CD) activity index. However, traditional approaches are unable to take advantage of more complex data structures such as repeated measurements. Deep learning methods have the potential ability to automatically find and learn complex, hidden relationships between predictive markers and outcomes, but their application to clinical prediction in CD and IBD has not been explored previously. AIM To determine and compare the utility of deep learning with conventional algorithms in predicting response to anti-tumor necrosis factor (anti-TNF) therapy in CD. METHODS This was a retrospective single-center cohort study of all CD patients who commenced anti-TNF therapy (either adalimumab or infliximab) from January 1, 2010 to December 31, 2015. Remission was defined as a C-reactive protein (CRP) < 5 mg/L at 12 mo after anti-TNF commencement. Three supervised learning algorithms were compared: (1) A conventional statistical learning algorithm using multivariable logistic regression on baseline data only; (2) A deep learning algorithm using a feed-forward artificial neural network on baseline data only; and (3) A deep learning algorithm using a recurrent neural network on repeated data. Predictive performance was assessed using area under the receiver operator characteristic curve (AUC) after 10× repeated 5-fold cross-validation. RESULTS A total of 146 patients were included (median age 36 years, 48% male). Concomitant therapy at anti-TNF commencement included thiopurines (68%), methotrexate (18%), corticosteroids (44%) and aminosalicylates (33%). After 12 mo, 64% had CRP < 5 mg/L. The conventional learning algorithm selected the following baseline variables for the predictive model: Complex disease behavior, albumin, monocytes, lymphocytes, mean corpuscular hemoglobin concentration and gamma-glutamyl transferase, and had a cross-validated AUC of 0.659, 95% confidence interval (CI): 0.562-0.756. A feed-forward artificial neural network using only baseline data demonstrated an AUC of 0.710 (95%CI: 0.622-0.799; P = 0.25 vs conventional). A recurrent neural network using repeated biomarker measurements demonstrated significantly higher AUC compared to the conventional algorithm (0.754, 95%CI: 0.674-0.834; P = 0.036). CONCLUSION Deep learning methods are feasible and have the potential for stronger predictive performance compared to conventional model building methods when applied to predicting remission after anti-TNF therapy in CD.

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Artificial intelligence for colonoscopic polyp detection: High performance versus human nature

Cited by 2 publications

References 9 publications

Image‐enhanced endoscopy for detection and diagnosis of colonic neoplasia: Time to shift focus

Image‐enhanced endoscopy for detection and diagnosis of colonic neoplasia: Time to shift focus

Deep learning vs conventional learning algorithms for clinical prediction in Crohn's disease: A proof-of-concept study

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