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

Chierici, Marco; Puica, Nicolae; Pozzi, Matteo; Capistrano, Antonello; Donzella, Marcello Dorian; Colangelo, Antonio; Osmani, Venet; Jurman, Giuseppe

doi:10.1186/s12911-022-02043-w

Cited by 19 publications

(5 citation statements)

References 21 publications

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“…Thus, this is the first attempt to establish AI‐based algorithms to distinguish IBD, infectious and ischemic colitis using endoscopic images and clinical data. Of note, our algorithms were not trained to distinguish colitis from healthy colonic mucosa or other gastrointestinal pathologies 23,24 …”

Section: Discussionmentioning

confidence: 99%

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Artificial‐intelligence‐based decision support tools for the differential diagnosis of colitis

Guimarães

Finkler

Reichert

et al. 2023

Eur J Clin Investigation

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Background Whereas Artificial Intelligence (AI) based tools have recently been introduced in the field of gastroenterology, application in inflammatory bowel disease (IBD) is in its infancies. We established AI‐based algorithms to distinguish IBD from infectious and ischemic colitis using endoscopic images and clinical data. Methods First, we trained and tested a Convolutional Neural Network (CNN) using 1796 real‐world images from 494 patients, presenting with three diseases (IBD [n = 212], ischemic colitis [n = 157], and infectious colitis [n = 125]). Moreover, we evaluated a Gradient Boosted Decision Trees (GBDT) algorithm using five clinical parameters as well as a hybrid approach (CNN + GBDT). Patients and images were randomly split into two completely independent datasets. The proposed approaches were benchmarked against each other and three expert endoscopists on the test set. Results For the image‐based CNN, the GBDT algorithm and the hybrid approach global accuracies were .709, .792, and .766, respectively. Positive predictive values were .602, .702, and .657. Global areas under the receiver operating characteristics (ROC) and precision recall (PR) curves were .727/.585, .888/.823, and .838/.733, respectively. Global accuracy did not differ between CNN and endoscopists (.721), but the clinical parameter‐based GBDT algorithm outperformed CNN and expert image classification. Conclusions Decision support systems exclusively based on endoscopic image analysis for the differential diagnosis of colitis, representing a complex clinical challenge, seem not yet to be ready for primetime and more diverse image datasets may be necessary to improve performance in future development. The clinical value of the proposed clinical parameters algorithm should be evaluated in prospective cohorts.

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Section: Discussionmentioning

confidence: 99%

“…Of note, our algorithms were not trained to distinguish colitis from healthy colonic mucosa or other gastrointestinal pathologies. 23,24 With an overall accuracy of .709 [.682-.743] and a PPV of .602 [.560-.659] performance of our image-based CNN might appear low. Especially infectious colitis seems to be problematic.…”

Section: Endoscopistsmentioning

confidence: 99%

Artificial‐intelligence‐based decision support tools for the differential diagnosis of colitis

Guimarães

Finkler

Reichert

et al. 2023

Eur J Clin Investigation

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“…Establishing the correct diagnosis as early as possible is crucial because the treatment for ulcerative colitis and Crohn's disease can vary – especially in severe cases, where surgical interventions are considered. In order to address this challenge, Chierici et al [15] developed a deep learning model to distinguish healthy versus inflamed tissue, ulcerative colitis from healthy tissue, and ulcerative colitis from Crohn's disease. Although the model's performance was best in the first two tasks, it was able to distinguish ulcerative colitis from Crohn's disease fairly accurately with a Matthew's correlation coefficient of 0.688.…”

Section: Machine Learning In Endoscopymentioning

confidence: 99%

Machine and deep learning in inflammatory bowel disease

Zulqarnain¹,

Rhoads²,

Syed³

2023

Current Opinion in Gastroenterology

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

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“…These models can be computationally expensive to train and may require a large amount of data and computational resources. This can make their use challenging in certain contexts, such as when data is limited or when computational resources are constrained, as in the case of automatic lesion detection techniques where the gastroenterologist manipulates the patient using the colonoscope and works on the software in real-time [36,37].…”

Section: Trainability Of the Modelmentioning

confidence: 99%

Unsupervised physics-informed neural network in reaction-diffusion biology models (Ulcerative colitis and Crohn’s disease cases) A preliminary study

Rebai,

Boukhris,

Toujani

et al. 2023

Preprint

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We propose to explore the potential of physics-informed neural networks (PINNs) in solving a class of partial differential equations (PDEs) used to model the propagation of chronic inflammatory bowel diseases, such as Crohn’s disease and ulcerative colitis. An unsupervised approach was privileged during the deep neural network training. Given the complexity of the underlying biological system, characterized by intricate feedback loops and limited availability of high-quality data, the aim of this study is to explore the potential of PINNs in solving PDEs. In addition to providing this exploratory assessment, we also aim to emphasize the principles of reproducibility and transparency in our approach, with a specific focus on ensuring the robustness and generalizability through the use of artificial intelligence. We will quantify the relevance of the PINN method with several linear and non-linear PDEs in relation to biology. However, it is important to note that the final solution is dependent on the initial conditions, chosen boundary conditions, and neural network architectures.

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

Cited by 19 publications

References 21 publications

Artificial‐intelligence‐based decision support tools for the differential diagnosis of colitis

Artificial‐intelligence‐based decision support tools for the differential diagnosis of colitis

Machine and deep learning in inflammatory bowel disease

Unsupervised physics-informed neural network in reaction-diffusion biology models (Ulcerative colitis and Crohn’s disease cases) A preliminary study

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