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Updated: Jun 16, 2026

Nanosensors to Detect Protease Activity In Vivo for Noninvasive Diagnostics

Nanosensors to Detect Protease Activity In Vivo for Noninvasive Diagnostics

Published on: July 16, 2018

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

Seungah Lee¹, Nayra A M Moussa², Seong Ho Kang^1,2

¹Department of Applied Chemistry and Institute of Natural Sciences, Kyung Hee University, Yongin-si 17104, Gyeonggi-do, Republic of Korea.

|September 26, 2025

Summary

This summary is machine-generated.

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Deep learning (DL) enhances nanozyme biosensors for advanced medical diagnostics. This integration improves disease detection, imaging, and point-of-care testing, paving the way for precision healthcare.

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).

Keywords:

biosensing deep learning nanozymes point-of-care diagnostics smartphone-based detection

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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.