436 - Automated Digital Calculation of Endoscopic Inflammation in Ulcerative Colitis: Results of the Red Density Study

Bossuyt, P; Nakase, Hiroshi; Vermeire, Séverine; Willekens, Hilde; Ikemoto, Yousuke; Makino, Takao; Bisschops, Raf

doi:10.1016/s0016-5085(18)30770-4

Cited by 2 publications

(3 citation statements)

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“…A novel objective computer-based score to assess UC disease activity based on endoscopic images has been developed. In particular, deep learning has been used to extract different layers of pixel data, such as measuring the redness degree through extraction of the intensity and distribution of red pixels in the red density score in UC 87 88. Similarly, assessment of CT/MRI images in IBD is extremely subjective; therefore, computer-aided scores could potentially overcome interobserver variation.…”

Section: Current Paradigm Of Ibd Disease Management and Its Limitationsmentioning

confidence: 99%

“…In particular, deep learning has been used to extract different layers of pixel data, such as measuring the redness degree through extraction of the intensity and distribution of red pixels in the red density score in UC. 87 88 Similarly, assessment of CT/MRI images in IBD is extremely subjective; therefore, computer-aided scores could potentially overcome interobserver variation. A semiautomated image analysis software showed a performance similar to those of experienced radiologists for the assessment of CD structural bowel damage in abdominal CT-enterography data.…”

Section: Current Paradigm Of Ibd Disease Management and Its Limitatiomentioning

confidence: 99%

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Big data in IBD: big progress for clinical practice

Tabib

Madgwick

Sudhakar³

et al. 2020

Gut

139

108

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IBD is a complex multifactorial inflammatory disease of the gut driven by extrinsic and intrinsic factors, including host genetics, the immune system, environmental factors and the gut microbiome. Technological advancements such as next-generation sequencing, high-throughput omics data generation and molecular networks have catalysed IBD research. The advent of artificial intelligence, in particular, machine learning, and systems biology has opened the avenue for the efficient integration and interpretation of big datasets for discovering clinically translatable knowledge. In this narrative review, we discuss how big data integration and machine learning have been applied to translational IBD research. Approaches such as machine learning may enable patient stratification, prediction of disease progression and therapy responses for fine-tuning treatment options with positive impacts on cost, health and safety. We also outline the challenges and opportunities presented by machine learning and big data in clinical IBD research.

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Section: Current Paradigm Of Ibd Disease Management and Its Limitationsmentioning

confidence: 99%

Section: Current Paradigm Of Ibd Disease Management and Its Limitatiomentioning

confidence: 99%

Big data in IBD: big progress for clinical practice

Tabib

Madgwick

Sudhakar³

et al. 2020

Gut

139

108

View full text Add to dashboard Cite

show abstract

“…These unbiased image data can be linked further to histological data. By doing so, we recently demonstrated that this approach leads to strong correlation with histology, which is known to be the best predictor of sustained clinical remission in UC 10. To avoid disappointment in the utopic quest for objective endoscopic scoring systems, let us ask the computers to do the work.…”

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confidence: 99%