AI-Powered Smart Biosensors: Nanotechnology, Machine Learning & Next-Generation Point-of-Care Diagnostics
Transforming Nanotechnology into Intelligent Diagnostics with AI-Powered Biosensing Solutions
Skills you will gain:
About Workshop:
This three-day hands-on workshop introduces participants to the integration of nanotechnology, biosensor engineering, artificial intelligence, and machine learning for next-generation point-of-care (POC) diagnostics. Participants will explore nanomaterial-based sensing platforms, biosensor design principles, signal processing, and AI-driven approaches for improving diagnostic accuracy, sensitivity, and real-time decision-making. The workshop will cover computational analysis, machine-learning models, and practical workflows used in developing intelligent diagnostic systems.
Aim: To provide participants with an understanding of nanobiosensor technologies, AI/ML-based data analysis, and intelligent diagnostic workflows for developing advanced point-of-care diagnostic solutions.
Workshop Objectives:
- Understand the fundamental principles of nanobiosensors and point-of-care diagnostics.
- Explore different nanomaterials used for biosensing applications.
- Learn how biological recognition and nanomaterials improve diagnostic performance.
- Understand biosensor signal processing and data interpretation.
- Apply machine-learning approaches for diagnostic prediction and classification.
- Evaluate AI models using appropriate performance metrics.
- Explore AI-driven approaches for smart and portable diagnostic systems.
- Understand the challenges and future opportunities in AI-enabled healthcare technologies.
What you will learn?
📅 Day 1: Fundamentals of Nanobiosensors and Point-of-Care Diagnostics
- Focus: Understanding the principles of nanomaterials, biosensing platforms, and their role in developing rapid and sensitive point-of-care diagnostic technologies.
- Introduction to point-of-care diagnostics and emerging healthcare challenges.
- Principles of biosensors: biological recognition elements, transducers, and signal-generation mechanisms.
- Types of nanomaterials used in biosensing applications:Gold nanoparticles (AuNPs),Graphene and graphene-based nanomaterials,Carbon nanotubes,Quantum dots,Metal oxide nanomaterials
- Understanding nanomaterial–biomolecule interactions for enhanced sensing performance.
- Electrochemical, optical, and fluorescence-based nanobiosensor platforms.
- Evaluation of biosensor performance parameters:Sensitivity,Selectivity,Limit of Detection (LOD),Response time,Stability
- Applications of nanobiosensors in infectious disease detection, cancer biomarker analysis, food safety, and environmental monitoring.
🛠️ Learning Activity:
- Analyse a representative nanobiosensor design and interpret key sensing parameters.
🧰 Tools Covered: Nanobiosensor design resources, scientific databases, sensor-performance datasets
📅 Day 2: AI/ML Approaches for Biosensor Data Analysis and Prediction
- Focus: Applying artificial intelligence and machine learning approaches for intelligent interpretation of biosensor signals and diagnostic prediction.
- Role of AI and machine learning in modern diagnostic systems.
- Biosensor data acquisition, preprocessing, and quality assessment.
- Feature extraction and signal-processing approaches for biosensor datasets.
- Data normalization and preparation for machine-learning workflows.
- Machine-learning models for biosensing applications:Logistic Regression,Random Forest,Support Vector Machines (SVM),Gradient Boosting,Neural Networks
- Classification and regression approaches for diagnostic prediction.
- Model evaluation using accuracy, precision, recall, F1-score, and ROC-AUC.
- Explainable AI (XAI) approaches for interpreting diagnostic predictions.
🛠️ Learning Activity:
- Build a basic machine-learning workflow for classification of biosensor-generated diagnostic data.
🧰 Tools Covered: Google Colab, Python, Pandas, NumPy, Scikit-learn, Matplotlib
📅 Day 3: AI-Driven Smart Diagnostics and Future Applications
- Focus: Integrating nanobiosensors with AI platforms for next-generation healthcare, digital diagnostics, and intelligent monitoring systems.
- AI-enabled biosensor workflow:Data acquisition,Signal processing,Prediction,Decision support
- Deep-learning approaches for diagnostic signal interpretation.
- AI-based pattern recognition in biosensor datasets.
- Sensor fusion approaches and multi-parameter diagnostic systems.
- Digital health platforms and connected point-of-care devices.
- Smartphone-based and portable diagnostic technologies.
- Challenges in AI-enabled nanobiosensors:Data quality,Model reliability,Clinical validation,Regulatory considerations
- Future directions:Wearable biosensors,Personalized diagnosticsAutonomous diagnostic systems
🛠️ Learning Activity:
- Design a conceptual AI-enabled nanobiosensor workflow for a healthcare application.
🧰 Tools Covered: Google Colab, Python, Machine Learning Libraries, Diagnostic Data Analysis Workflows
Intended For :
Career Supporting Skills
Program Outcomes
- Explain the working principles of nanobiosensors and POC diagnostic platforms.
- Identify suitable nanomaterials for different biosensing applications.
- Interpret important biosensor performance parameters.
- Understand how AI improves diagnostic accuracy and automation.
- Develop basic machine-learning models for biosensor data classification.
- Analyse diagnostic datasets using computational workflows.
- Evaluate AI model performance using scientific metrics.
- Design conceptual AI-enabled diagnostic workflows.
- Understand current research trends in smart biosensors, digital health, and personalized diagnostics.
- Identify opportunities for research and innovation in AI-driven healthcare technologies.