Deep Learning-Enhanced Nanozyme-Based Biosensors for Next-Generation Medical Diagnostics

Area of Science:

• Biomedical Engineering
• Artificial Intelligence
• Nanotechnology

Background:

• Nanozyme-based biosensors offer promising diagnostic capabilities.
• Deep learning (DL) presents opportunities to enhance nanozyme performance and applications.
• Next-generation medical diagnostics require innovative sensing strategies.

Purpose of the Study:

• To review the integration of deep learning (DL) with nanozyme-based biosensing.
• To explore how DL enhances nanozyme design, optimization, and predictive modeling.
• To discuss applications, challenges, and future directions in AI-enhanced nanozyme diagnostics.

Main Methods:

• Review of literature on DL architectures and nanozyme catalysis.
• Analysis of DL's role in elucidating catalytic mechanisms (e.g., dual-atom sites).
• Examination of DL applications in biomarker detection, medical imaging, and point-of-care diagnostics.

Main Results:

• DL improves nanozyme design, functional optimization, and predictive modeling.
• Key applications include disease biomarker detection and enhanced medical imaging.
• Wearable biosensors and smart platforms leverage DL for real-time analysis and decision-making.

Conclusions:

• The convergence of nanozymes and DL offers significant potential for intelligent biosensing.
• Challenges include data standardization, model robustness, and clinical translation.
• Future directions involve integration with IoMT, personalized medicine, and sustainable development.