Challenges in Image Processing for Genetic Disorders: Barriers in Early Detection, Gene Editing, and Personalized Therapy

Background: In recent years, medical biotechnology has seen remarkable advancements, particularly in addressing genetic disorders conditions caused by gene mutations and affecting millions worldwide. Genetic disorders are among the leading contributors to chronic illnesses and mortality, accounting for nearly ۱۰% of annual deaths in developed nations. Advances in medical image processing, including molecular, tissue, and brain imaging, have revolutionized our understanding of genetic disorders by uncovering structural and molecular changes. Techniques like MRI, PET, and deep learning algorithms have enhanced diagnostic accuracy, enabling early detection and personalized therapies. Materials and Methods: This review synthesizes peer-reviewed studies published from ۲۰۲۰ to ۲۰۲۴, focusing on advancements in medical imaging for genetic disorders. Key imaging modalities (MRI, CT, PET, molecular imaging) and machine learning algorithms were analyzed for their roles in early detection, genetic profiling integration, and personalized therapy. Results: Advancements in imaging technologies, such as MRI, PET, and molecular imaging, have significantly improved the detection and diagnosis of genetic disorders. MRI, with an accuracy of ۸۵-۹۰%, is highly effective for visualizing soft tissues and identifying structural anomalies in conditions like Huntington's disease and muscular dystrophies. PET imaging, with over ۹۵% sensitivity, excels in detecting amyloid plaques in Alzheimer's disease, enabling diagnosis before clinical symptoms appear. Molecular imaging, using radiolabeled markers, provides unparalleled accuracy in linking genetic profiles to imaging data, especially in cancers driven by specific mutations. These advancements offer earlier detection and more personalized treatment options. Machine learning (ML) and deep learning (DL) technologies have further enhanced diagnostic accuracy. For instance, deep learning models analyzing MRI images achieved over ۹۰% accuracy in detecting genetic mutations, compared to ۷۰% with traditional methods. Convolutional neural networks (CNNs) have proven particularly effective in identifying structural brain changes in neurogenetic diseases, enabling earlier interventions. However, challenges remain. High costs and technical complexities limit the accessibility of advanced modalities like PET and molecular imaging, especially in low-resource settings. Additionally, integrating imaging data with genetic profiles is hindered by differences in data formats and variability in genetic expression. Overcoming these challenges will require innovations in affordable multimodal imaging technologies and AI-driven data integration methods. Conclusion: The integration of advanced imaging modalities with genetic profiling and AI technologies holds transformative potential for diagnosing and treating genetic disorders. Despite significant progress in improving diagnostic accuracy and enabling personalized therapies, challenges such as accessibility, data integration, and variability remain. Addressing these hurdles will ensure broader clinical adoption and enhanced patient outcomes.

کلیدواژه ها:

Genetic disorders ، image processing ، early detection ، gene editing ، personalized therapy