Artificial intelligence in inflammatory bowel disease endoscopy: current landscape and the road ahead

Sundaram, Suneha; Choden, Tenzin; Mattar, Mark; Desai, Sanjal; Desai, Madhav

doi:10.1177/26317745211017809

Cited by 13 publications

(11 citation statements)

References 27 publications

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“…These AI systems are expected to transform patient care and provide improved outcomes for patients and resource savings for the healthcare system in the future 6 . In particular, AI is expected to have a profound effect on diagnosis and treatment of diseases of the gastrointestinal (GI) tract, including cancer of the gut and liver 7 , 8 , inflammatory diseases 9 and other diseases of major epidemiological importance.…”

Section: Introductionmentioning

confidence: 99%

Weakly supervised end-to-end artificial intelligence in gastrointestinal endoscopy

Buendgens

Cifci

Laleh

et al. 2022

Sci Rep

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Artificial intelligence (AI) is widely used to analyze gastrointestinal (GI) endoscopy image data. AI has led to several clinically approved algorithms for polyp detection, but application of AI beyond this specific task is limited by the high cost of manual annotations. Here, we show that a weakly supervised AI can be trained on data from a clinical routine database to learn visual patterns of GI diseases without any manual labeling or annotation. We trained a deep neural network on a dataset of N = 29,506 gastroscopy and N = 18,942 colonoscopy examinations from a large endoscopy unit serving patients in Germany, the Netherlands and Belgium, using only routine diagnosis data for the 42 most common diseases. Despite a high data heterogeneity, the AI system reached a high performance for diagnosis of multiple diseases, including inflammatory, degenerative, infectious and neoplastic diseases. Specifically, a cross-validated area under the receiver operating curve (AUROC) of above 0.70 was reached for 13 diseases, and an AUROC of above 0.80 was reached for two diseases in the primary data set. In an external validation set including six disease categories, the AI system was able to significantly predict the presence of diverticulosis, candidiasis, colon and rectal cancer with AUROCs above 0.76. Reverse engineering the predictions demonstrated that plausible patterns were learned on the level of images and within images and potential confounders were identified. In summary, our study demonstrates the potential of weakly supervised AI to generate high-performing classifiers and identify clinically relevant visual patterns based on non-annotated routine image data in GI endoscopy and potentially other clinical imaging modalities.

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Section: Introductionmentioning

confidence: 99%

Weakly supervised end-to-end artificial intelligence in gastrointestinal endoscopy

Buendgens

Cifci

Laleh

et al. 2022

Sci Rep

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“…As a consequence, the emergence of Artificial Intelligence (AI) solutions based on Deep Learning (DL) models comes as no surprise. The models exploit the potential of artificial neural networks to automatically process different types of data [ 11 , 12 ], including endoscopic imaging [ 13 ]. Our work naturally embeds in this research line, combining recent DL architectures with more classical ML strategies such as ensemble learning to further enhance the predictive performance.…”

Section: Introductionmentioning

confidence: 99%

“…Our work naturally embeds in this research line, combining recent DL architectures with more classical ML strategies such as ensemble learning to further enhance the predictive performance. The promising results obtained can support the clinicians in providing a more objective and reliable diagnosis, thus reducing the risk of misidentification of CD and UC, an important aspect considering different treatment options and follow-ups of the two conditions [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Automatically detecting Crohn’s disease and Ulcerative Colitis from endoscopic imaging

Chierici

Puica

Pozzi

et al. 2022

BMC Med Inform Decis Mak

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Background The SI-CURA project (Soluzioni Innovative per la gestione del paziente e il follow up terapeutico della Colite UlceRosA) is an Italian initiative aimed at the development of artificial intelligence solutions to discriminate pathologies of different nature, including inflammatory bowel disease (IBD), namely Ulcerative Colitis (UC) and Crohn’s disease (CD), based on endoscopic imaging of patients (P) and healthy controls (N). Methods In this study we develop a deep learning (DL) prototype to identify disease patterns through three binary classification tasks, namely (1) discriminating positive (pathological) samples from negative (healthy) samples (P vs N); (2) discrimination between Ulcerative Colitis and Crohn’s Disease samples (UC vs CD) and, (3) discrimination between Ulcerative Colitis and negative (healthy) samples (UC vs N). Results The model derived from our approach achieves a high performance of Matthews correlation coefficient (MCC) > 0.9 on the test set for P versus N and UC versus N, and MCC > 0.6 on the test set for UC versus CD. Conclusion Our DL model effectively discriminates between pathological and negative samples, as well as between IBD subgroups, providing further evidence of its potential as a decision support tool for endoscopy-based diagnosis.

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“…Furthermore, most methods primarily focused on CRC classification, with relatively little attention paid to inflammatory bowel disease (IBD) [ 22 ]. The number of patients with IBD, which has recently established itself as a global disease, is rapidly increasing [ 23 , 24 , 25 , 26 ]. However, it is difficult to accurately distinguish CRC from colonic inflammation because the patterns appearing in tissue confocal microscopy images look similar [ 27 , 28 , 29 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

Classification of the Confocal Microscopy Images of Colorectal Tumor and Inflammatory Colitis Mucosa Tissue Using Deep Learning

et al. 2022

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Confocal microscopy image analysis is a useful method for neoplasm diagnosis. Many ambiguous cases are difficult to distinguish with the naked eye, thus leading to high inter-observer variability and significant time investments for learning this method. We aimed to develop a deep learning-based neoplasm classification model that classifies confocal microscopy images of 10× magnified colon tissues into three classes: neoplasm, inflammation, and normal tissue. ResNet50 with data augmentation and transfer learning approaches was used to efficiently train the model with limited training data. A class activation map was generated by using global average pooling to confirm which areas had a major effect on the classification. The proposed method achieved an accuracy of 81%, which was 14.05% more accurate than three machine learning-based methods and 22.6% better than the predictions made by four endoscopists. ResNet50 with data augmentation and transfer learning can be utilized to effectively identify neoplasm, inflammation, and normal tissue in confocal microscopy images. The proposed method outperformed three machine learning-based methods and identified the area that had a major influence on the results. Inter-observer variability and the time required for learning can be reduced if the proposed model is used with confocal microscopy image analysis for diagnosis.

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Artificial intelligence in inflammatory bowel disease endoscopy: current landscape and the road ahead

Cited by 13 publications

References 27 publications

Weakly supervised end-to-end artificial intelligence in gastrointestinal endoscopy

Weakly supervised end-to-end artificial intelligence in gastrointestinal endoscopy

Automatically detecting Crohn’s disease and Ulcerative Colitis from endoscopic imaging

Classification of the Confocal Microscopy Images of Colorectal Tumor and Inflammatory Colitis Mucosa Tissue Using Deep Learning

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