A Novel Radiomics Model Integrating Luminal and Mesenteric Features to Predict Mucosal Activity and Surgery Risk in Crohn's Disease Patients: A Multicenter Study

Academic Radiology, Journal Year: 2023, Volume and Issue: 30, P. S207 - S219

Published: May 5, 2023

Language: Английский

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AI-luminating Artificial Intelligence in Inflammatory Bowel Diseases: A Narrative Review on the Role of AI in Endoscopy, Histology, and Imaging for IBD

Inflammatory Bowel Diseases, Journal Year: 2024, Volume and Issue: 30(12), P. 2467 - 2485

Published: March 7, 2024

Endoscopy, histology, and cross-sectional imaging serve as fundamental pillars in the detection, monitoring, prognostication of inflammatory bowel disease (IBD). However, interpretation these studies often relies on subjective human judgment, which can lead to delays, intra- interobserver variability, potential diagnostic discrepancies. With rising incidence IBD globally coupled with exponential digitization data, there is a growing demand for innovative approaches streamline diagnosis elevate clinical decision-making. In this context, artificial intelligence (AI) technologies emerge timely solution address evolving challenges IBD. Early using deep learning radiomics endoscopy, have demonstrated promising results AI detect, diagnose, characterize, phenotype, prognosticate Nonetheless, available literature has inherent limitations knowledge gaps that need be addressed before transition into mainstream tool To better understand value integrating IBD, we review summarize our current understanding identify inform future investigations.

Language: Английский

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Data-driven decision-making for precision diagnosis of digestive diseases

BioMedical Engineering OnLine, Journal Year: 2023, Volume and Issue: 22(1)

Published: Sept. 1, 2023

Abstract Modern omics technologies can generate massive amounts of biomedical data, providing unprecedented opportunities for individualized precision medicine. However, traditional statistical methods cannot effectively process and utilize such big data. To meet this new challenge, machine learning algorithms have been developed applied rapidly in recent years, which are capable reducing dimensionality, extracting features, organizing data forming automatable data-driven clinical decision systems. Data-driven decision-making promising applications medicine has studied digestive diseases, including early diagnosis screening, molecular typing, staging stratification malignancies, as well precise Crohn's disease, auxiliary imaging endoscopy, differential cystic lesions, etiology discrimination acute abdominal pain, upper gastrointestinal bleeding (UGIB), real-time esophageal motility function, showing good application prospects. Herein, we reviewed the progress making diseases discussed limitations after a brief introduction making.

Language: Английский

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Automatic Segmentation and Radiomics for Identification and Activity Assessment of CTE Lesions in Crohn’s Disease

Inflammatory Bowel Diseases, Journal Year: 2023, Volume and Issue: unknown

Published: Nov. 27, 2023

The purpose of this article is to develop a deep learning automatic segmentation model for the Crohn's disease (CD) lesions in computed tomography enterography (CTE) images. Additionally, radiomics features extracted from segmented CD will be analyzed and multiple machine classifiers built distinguish activity.This was retrospective study with 2 sets CTE image data. Segmentation datasets were used establish nnU-Net neural network's model. classification dataset processed using obtain results extract features. most optimal then selected build 5 activity. performance evaluated Dice similarity coefficient, while classifier area under curve, sensitivity, specificity, accuracy.The had 84 examinations patients (mean age 31 ± 13 years , 60 males), 193 12 136 males). achieved coefficient 0.824 on testing set. logistic regression showed best among set, an accuracy 0.862, 0.697, 0.840, 0.759, respectively.The automated accurately segments lesions, distinguishes activity well. This method can assist radiologists promptly precisely evaluating activity.The images quickly identifying Crohn’s

Language: Английский

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Advancements in Automatic Kidney Segmentation Using Deep Learning Frameworks and Volumetric Segmentation Techniques for CT Imaging: A Review

Archives of Computational Methods in Engineering, Journal Year: 2024, Volume and Issue: 31(5), P. 3151 - 3169

Published: Feb. 19, 2024

Language: Английский

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Radiomics prediction of surgery in ulcerative colitis refractory to medical treatment

Techniques in Coloproctology, Journal Year: 2025, Volume and Issue: 29(1)

Published: May 10, 2025

Language: Английский

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Longitudinal computed tomography-based delta-radiomics of visceral adipose tissue predicts infliximab secondary loss of response in Crohn’s disease patients

World Journal of Gastroenterology, Journal Year: 2025, Volume and Issue: 31(21)

Published: June 6, 2025

BACKGROUND Visceral adipose tissue (VAT) plays a role in the pathogenesis of Crohn's disease (CD) and is associated with treatment outcomes following infliximab (IFX) therapy. We developed validated first delta-radiomics model to quantify VAT heterogeneity as predictive biomarker for IFX response patients CD. AIM To develop longitudinal computed tomography (CT)-based predicting secondary loss (SLR) METHODS This retrospective study included 161 CD who achieved clinical remission induction therapy between 2015 2023. All underwent CT enterography before initiation after completing volume was delineated by two radiologists consensus. Radiomics features were extracted from pre-treatment post-induction images, calculated follows: Delta = Feature-post - Feature-pre. A radiomics constructed using logistic regression. Model performance assessed discrimination, calibration, decision curve analyses. RESULTS Nine significant used model, yielding an area under receiver operating characteristic (AUC) 0.816 (95%CI: 0.737-0.896) training cohort 0.750 0.605-0.895) validation cohort. Multivariable regression identified platelet count, Montreal behavior classification, VAT/subcutaneous ratio prior independent risk factors SLR. The combined integrating predictors superior performance, AUC 0.853 0.786-0.921) 0.812 0.677-0.948) CONCLUSION based on changes VAT, demonstrating potential identifying at high SLR

Language: Английский

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Machine and deep learning in inflammatory bowel disease

Current Opinion in Gastroenterology, Journal Year: 2023, Volume and Issue: unknown

Published: May 5, 2023

The Management of inflammatory bowel disease (IBD) has evolved with the introduction and widespread adoption biologic agents; however, advent artificial intelligence technologies like machine learning deep presents another watershed moment in IBD treatment. Interest these methods research increased over past 10 years, they offer a promising path to better clinical outcomes for patients.Developing new tools evaluate inform management is challenging because expansive volume data requisite manual interpretation data. Recently, models have been used streamline diagnosis evaluation by automating review from several diagnostic modalities high accuracy. These decrease amount time that clinicians spend manually reviewing formulate an assessment.Interest increasing medicine, are poised revolutionize way we treat IBD. Here, highlight recent advances using discuss ways can be leveraged improve outcomes.

Language: Английский

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Performance of Machine Learning Algorithms for Predicting Disease Activity in Inflammatory Bowel Disease

Inflammation, Journal Year: 2023, Volume and Issue: 46(4), P. 1561 - 1574

Published: May 12, 2023

Language: Английский

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Radiological biomarkers reflecting visceral fat distribution help distinguish inflammatory bowel disease subtypes: a multicenter cross-sectional study

Insights into Imaging, Journal Year: 2024, Volume and Issue: 15(1)

Published: March 13, 2024

Abstract Objectives To achieve automated quantification of visceral adipose tissue (VAT) distribution in CT images and screen out parameters with discriminative value for inflammatory bowel disease (IBD) subtypes. Methods This retrospective multicenter study included Crohn’s (CD) ulcerative colitis (UC) patients from three institutions between 2012 2021, acute appendicitis as controls. An automatic VAT segmentation algorithm was developed using abdominal scans. The volume, well the coefficient variation (CV) areas within lumbar region, calculated. Binary logistic regression receiver operating characteristic analysis performed to evaluate potential indicators distinguish IBD Results 772 (365 CDs, median age [inter-quartile range] = 31.0. (25.0, 42.0) years, 255 males; 241 UCs, 46.0 (34.0, 55.5) 138 166 controls, 40.0 (29.0, 53.0) 80 males). CD had lower volume (CD 1584.95 ± 1128.31 cm 3 , UC 1855.30 1326.12 controls 2470.91 1646.42 ) but a higher CV 29.42 15.54 %, p 0.006 ˂ 0.001) compared (25.69 12.61 % vs. 23.42 15.62 0.11). Multivariate showed significant predictor (odds ratio 6.05 (1.17, 31.12), 0.03). inclusion improved diagnostic efficiency (AUC 0.811 (0.774, 0.844) 0.803 (0.766, 0.836), 0.08). Conclusion CT-based can serve biomarker distinguishing Critical relevance statement Visceral fat features extracted an (1.14 min) show differences are promising practical radiological screening. Key points • Radiological reflecting were discrimination (UC). In CD, concentrated vertebrae, (OR UC, Graphical

Language: Английский

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