By: S. Anand
Menkes disease, a rare X-linked recessive disorder of copper metabolism, poses significant challenges in early diagnosis due to its nonspecific initial symptoms. This paper presents a comprehensive approach to enhance the early detection of Menkes disease by leveraging image processing techniques, pattern recognition algorithms, and innovative biosensor technologies. Novel framework proposed that combines analysis of hair microscopy images, facial feature recognition, and copper-sensitive biosensors to create a multi-modal diagnostic tool. The image processing component utilizes advanced machine learning algorithms to detect the characteristic pili torti (twisted hair) pattern in microscopic hair samples. Facial feature analysis employs deep learning models to identify subtle dysmorphic features associated with Menkes disease. Additionally, a cutting-edge biosensor system is introduced, which is capable of rapidly measuring serum copper levels with high sensitivity. The integration of these technologies results in a comprehensive diagnostic platform that significantly improves the accuracy and speed of Menkes disease detection. Our experimental studies demonstrate a 95% accuracy in identifying Menkes disease cases, with a reduction in diagnostic time from weeks to hours. This research not only advances the field of rare disease diagnostics but also paves the way for personalized treatment strategies and improved patient outcomes in Menkes disease management.
Keywords: Menkes disease, image processing, pattern recognition, biosensors, machine learning, early diagnosis
Citation:
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