Advancements in Computer-Aided Diagnosis of Celiac Disease: A Systematic Review

Abstract

Celiac disease, a chronic autoimmune condition, manifests in those genetically prone to it through damage to the small intestine upon gluten consumption. This condition is estimated to affect approximately one in every hundred individuals worldwide, though it often goes undiagnosed. The early and accurate diagnosis of celiac disease (CD) is critical to preventing severe health complications, with computer-aided diagnostic approaches showing significant promise. However, there is a shortage of review literature that encapsulates the field's current state and offers a perspective on future advancements. Therefore, this review critically assesses the literature on the role of imaging techniques, biomarker analysis, and computer models in improving CD diagnosis. We highlight the diagnostic strengths of advanced imaging and the non-invasive appeal of biomarker analyses, while also addressing ongoing challenges in standardization and integration into clinical practice. Our analysis stresses the importance of computer-aided diagnostics in fast-tracking the diagnosis of CD, highlighting the necessity for ongoing research to refine these approaches for effective implementation in clinical settings. Future research in the field will focus on standardizing CAD protocols for broader clinical use and exploring the integration of genetic and protein data to enhance early detection and personalize treatment strategies. These advancements promise significant improvements in patient outcomes and broader implications for managing autoimmune diseases.

Keywords: artificial intelligence; celiac disease; computer vision; computer-aided diagnosis; machine learning.

Figure 1

PRISMA flow diagram of our review methodology.

Figure 2

The distribution of the papers for CD diagnosis published in the last five years.

Figure 3

An illustration of (a) epidemiology and clinical aspects, (b) pathophysiology, and (c) diagnostic approaches of CD. Diagnostic approaches include (d) traditional and (e) computer-aided approaches.

Figure 4

An illustration of the described processing methods. The process begins with data acquisition, which includes procedures such as biopsy and endoscopy for medical imaging and serologic testing for clinical data analysis. The next phase, data preparation, includes techniques such as ROI detection, contrast enhancement, and binarization. The final step is classification using methods such as CNNs, K-NNs, and SVMs to predict the presence of celiac disease.

Figure 5

The relationship of current challenges in computer-aided diagnosis methods of CD and possible future directions.