Diagnosis and Severity Assessment of Ulcerative Colitis using Self Supervised Learning

by jsendak | Dec 12, 2024 | AI

arXiv:2412.07806v1 Announce Type: cross Abstract: Ulcerative Colitis (UC) is an incurable inflammatory bowel disease that leads to ulcers along the large intestine and rectum. The increase in the prevalence of UC coupled with gastrointestinal physician shortages stresses the healthcare system and limits the care UC patients receive. A colonoscopy is performed to diagnose UC and assess its severity based on the Mayo Endoscopic Score (MES). The MES ranges between zero and three, wherein zero indicates no inflammation and three indicates that the inflammation is markedly high. Artificial Intelligence (AI)-based neural network models, such as convolutional neural networks (CNNs) are capable of analyzing colonoscopies to diagnose and determine the severity of UC by modeling colonoscopy analysis as a multi-class classification problem. Prior research for AI-based UC diagnosis relies on supervised learning approaches that require large annotated datasets to train the CNNs. However, creating such datasets necessitates that domain experts invest a significant amount of time, rendering the process expensive and challenging. To address the challenge, this research employs self-supervised learning (SSL) frameworks that can efficiently train on unannotated datasets to analyze colonoscopies and, aid in diagnosing UC and its severity. A comparative analysis with supervised learning models shows that SSL frameworks, such as SwAV and SparK outperform supervised learning models on the LIMUC dataset, the largest publicly available annotated dataset of colonoscopy images for UC.
The article discusses the challenges faced in diagnosing and assessing the severity of ulcerative colitis (UC), an inflammatory bowel disease that causes ulcers in the large intestine and rectum. The increasing prevalence of UC, coupled with a shortage of gastrointestinal physicians, has put a strain on the healthcare system and limited the care received by UC patients. Currently, a colonoscopy is performed to diagnose UC and assess its severity using the Mayo Endoscopic Score (MES). Artificial Intelligence (AI)-based neural network models, specifically convolutional neural networks (CNNs), have shown promise in analyzing colonoscopies to diagnose and determine the severity of UC. However, previous research has relied on supervised learning approaches that require large annotated datasets, which are expensive and time-consuming to create. To overcome this challenge, this study explores the use of self-supervised learning (SSL) frameworks that can efficiently train on unannotated datasets to analyze colonoscopies and aid in the diagnosis of UC and its severity. The researchers compare SSL frameworks, such as SwAV and SparK, with supervised learning models on the LIMUC dataset, the largest publicly available annotated dataset of colonoscopy images for UC. The results show that SSL frameworks outperform supervised learning models, offering a potential solution to the limitations in UC diagnosis and severity assessment.

Ulcerative Colitis (UC) is a debilitating inflammatory bowel disease that affects millions of people worldwide. The increasing prevalence of UC, coupled with a shortage of gastrointestinal physicians, has put a strain on the healthcare system and limited the care that UC patients receive. Diagnosis and assessment of UC severity are crucial for providing appropriate treatment, and traditionally, a colonoscopy has been the primary tool for this purpose.

In recent years, Artificial Intelligence (AI) has made significant strides in the field of medical imaging analysis, and colonoscopy analysis is no exception. Convolutional Neural Networks (CNNs) have shown promising results in diagnosing and determining the severity of UC by modeling colonoscopy analysis as a multi-class classification problem. However, a major limitation of existing AI-based UC diagnosis models is the requirement for large annotated datasets to train the CNNs.

Creating such datasets, which involve labeling thousands of colonoscopy images, requires extensive time and effort from domain experts, making the process expensive and challenging. To address this challenge, researchers have turned to self-supervised learning (SSL) frameworks, which can efficiently train on unannotated datasets.

SSL frameworks leverage the inherent information within the unannotated dataset to learn useful representations of the images. By leveraging this unsupervised learning approach, AI models can effectively analyze colonoscopy images and aid in the diagnosis of UC and the assessment of its severity.

One such SSL framework that has shown promising results in UC diagnosis is SwAV. SwAV stands for “Self-supervised learning with Swapped Assignments and Visualizations.” It involves randomly swapping patches from different colonoscopy images and training the network to classify whether the patches belong to the same image or not. This process forces the network to learn meaningful representations of the colonoscopy images, even without explicit annotations.

Another SSL framework called SparK, which stands for “Self-supervised training for Analysis of Red Light and KiNetics,” has also demonstrated impressive performance in UC diagnosis. SparK leverages the temporal information within a video sequence of colonoscopy images to learn representations. By predicting the ordering of the frames within the video, SparK can capture the dynamics of UC progression and severity.

A comparative analysis of SSL frameworks with traditional supervised learning models on the LIMUC dataset, the largest publicly available annotated dataset of colonoscopy images for UC, showed that SSL frameworks outperformed supervised learning models. This demonstrates the potential of SSL frameworks in improving the accuracy and efficiency of UC diagnosis and severity assessment.

The use of SSL frameworks in UC diagnosis not only reduces the reliance on large annotated datasets but also opens up avenues for scaling up the deployment of AI models in healthcare settings. By utilizing unannotated datasets, healthcare institutions can analyze a larger volume of colonoscopy images, leading to faster and more accurate diagnoses for patients.

In conclusion, the application of self-supervised learning frameworks, such as SwAV and SparK, in analyzing colonoscopy images for UC diagnosis and severity assessment holds great promise. These frameworks enable AI models to learn from unannotated datasets, reducing the need for extensive manual annotations and accelerating the diagnosis process. By leveraging the power of AI and SSL, we can revolutionize the way UC is diagnosed and improve patient care in the face of the increasing prevalence of this disease.

The paper “Ulcerative Colitis Diagnosis and Severity Assessment using Self-Supervised Learning on Colonoscopy Images” addresses the challenge of diagnosing and assessing the severity of ulcerative colitis (UC) using artificial intelligence (AI)-based neural network models. UC is a chronic inflammatory bowel disease that affects the large intestine and rectum, causing ulcers. The increasing prevalence of UC, coupled with a shortage of gastrointestinal physicians, puts a strain on the healthcare system and limits the care received by UC patients.

Traditionally, UC diagnosis and severity assessment have relied on colonoscopy, a procedure that involves visually examining the colon and rectum using a flexible tube with a camera. The severity of UC is typically assessed using the Mayo Endoscopic Score (MES), which ranges from zero to three, with zero indicating no inflammation and three indicating severe inflammation.

In recent years, AI and machine learning techniques have shown promise in assisting with UC diagnosis and severity assessment. Specifically, convolutional neural networks (CNNs) have been used to analyze colonoscopy images and classify them into different severity levels of UC. However, previous research in this area has relied on supervised learning, which requires large annotated datasets to train the CNNs. Creating such datasets is time-consuming and expensive, as it involves domain experts manually labeling a large number of colonoscopy images.

To overcome this challenge, the authors of this paper propose the use of self-supervised learning (SSL) frameworks for UC diagnosis and severity assessment. SSL is a type of machine learning where the model learns from unannotated data without the need for explicit labels. The advantage of SSL is that it can leverage large amounts of unannotated data, which is more readily available compared to annotated datasets.

The researchers evaluated two SSL frameworks, SwAV and SparK, on the LIMUC dataset, which is the largest publicly available annotated dataset of colonoscopy images for UC. They compared the performance of these SSL frameworks with supervised learning models. The results showed that the SSL frameworks outperformed the supervised learning models in terms of UC diagnosis and severity assessment.

This research is significant as it addresses the limitations of previous approaches to AI-based UC diagnosis and severity assessment. By leveraging SSL frameworks, which can efficiently train on unannotated datasets, the time and cost involved in creating annotated datasets can be significantly reduced. This opens up the possibility of scaling up AI-based UC diagnosis and severity assessment, making it more accessible and cost-effective in real-world healthcare settings.

Moving forward, it would be interesting to see how these SSL frameworks perform on larger and more diverse datasets. Additionally, future research could explore the integration of these AI models into clinical practice, considering factors such as interpretability, validation, and regulatory considerations. Overall, this study highlights the potential of AI and SSL in revolutionizing the diagnosis and assessment of UC, ultimately improving patient care and outcomes.
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