Deep Learning-Based Crohn's Disease Prediction: A Comprehensive Examination and Future Perspectives

Abstract

This study explores deep learning's (DL) role in enhancing Crohn's Disease (CD) diagnosis and treatment, aiming to overcome current diagnostic challenges and improve patient outcomes through more accurate, efficient, and personalized medical interventions. This comprehensive review scrutinized a plethora of studies focusing on the utilization of machine learning (ML) and DL methodologies for diagnosing CD. The investigation spanned various Artificial Intelligence (AI) techniques. This endeavor aimed to illustrate the transformation from traditional ML methods, which necessitate labor-intensive data preprocessing and expert analysis, to DL approaches that autonomously decipher intricate patterns from voluminous datasets. Special attention was accorded to research that leveraged these technologies for distinguishing CD from ulcerative colitis (UC), anticipating disease complications, and pinpointing diagnostic markers. This review elucidates the progression from traditional machine learning techniques, requiring substantial data preparation and expert knowledge, to deep learning algorithms capable of learning directly from raw data. This shift promises to automate and refine the diagnostic process significantly. An examination of various studies showcased how AI applications in CD diagnosis are evolving, underscoring the potential of these technologies to transform the diagnostic landscape by enhancing accuracy, reducing time, and paving the way for personalized treatment strategies. The integration of AI in CD diagnosis has shown significant promise, with DL and ML models achieving higher diagnostic accuracy and efficiency compared to traditional methods.