AI-Powered Biosensors for Precision Diagnostics: Nanomaterials, Machine Learning & Point-of-Care Testing

Aim

Workshop Objectives

Workshop Structure

Day 1: Nanomaterial-Based Biosensors & Diagnostic Signal Analysis

Focus: Understand biosensor design, nanomaterials, biomarker detection, and sensor-response characteristics.

Topics Covered

• Fundamentals of biosensors and precision diagnostics
• Biosensor components: bioreceptor, transducer, signal-processing system
• Electrochemical, optical, and other biosensing platforms
• Nanomaterials for enhanced biosensing
• Gold nanoparticles, silver nanoparticles, graphene, carbon nanotubes, and metal oxides
• Nanomaterial surface functionalisation and biomolecule immobilisation
• Biomarker recognition and assay design
• Sensitivity, selectivity, limit of detection, response time, and dynamic range
• Sensor calibration and response curves
• Sources of noise, interference, and experimental variability

 Hands-on:Analyse a curated biosensor dataset, generate calibration curves, calculate analytical performance parameters, and compare sensor responses.

Workflow:

Nanomaterial → Bioreceptor → Sensor Response → Signal Processing → Diagnostic Performance

Tools Covered

Python, pandas, NumPy, matplotlib, Google Colab, curated biosensor datasets

Day 2: Machine Learning for Biosensor Data & Diagnostic Prediction

Focus: Transform sensor signals and experimental parameters into predictive machine-learning models.

Topics Covered

• Biosensor dataset development and data preprocessing
• Signal cleaning, normalization, and feature extraction
• Sensor-response and biomarker features
• Exploratory data analysis and correlation analysis
• Classification and regression approaches for biosensor data
• Random Forest and XGBoost models
• Training, testing, and cross-validation
• Performance metrics: accuracy, precision, recall, F1-score, ROC-AUC, MAE, and RMSE
• Feature importance and sensor-variable selection
• Explainable AI for diagnostic predictions

 Hands-on:Build and evaluate a machine-learning model using biosensor data to classify diagnostic conditions or predict biomarker concentrations.

Workflow:

Sensor Data → Preprocessing → Feature Engineering → ML Model → Validation → Diagnostic Prediction

Tools Covered

Python, pandas, NumPy, scikit-learn, Random Forest, XGBoost, matplotlib, Google Colab

Day 3: AI-Enabled Point-of-Care Diagnostics & Sensor Optimisation

Focus: Integrate biosensor technology and AI for rapid, portable, and data-driven diagnostic applications.

Topics Covered

• Principles of point-of-care diagnostic systems
• Portable and miniaturised biosensor platforms
• Integration of sensors, data acquisition, and AI models
• Multiplexed biomarker detection
• AI-assisted disease classification and risk prediction
• Sensor optimisation using data-driven approaches
• Multi-parameter optimisation of sensitivity, selectivity, response time, and stability
• Candidate sensor comparison and performance ranking
• Explainable diagnostic decision-making
• Validation, reproducibility, and generalisation
• POC challenges: matrix effects, calibration, robustness, and usability
• Translational and regulatory considerations

 Hands-on:Screen biosensor designs, apply an ML model for diagnostic prediction, identify important sensor parameters, and develop a prioritised POC biosensor configuration.

Workflow:

Sensor Dataset → AI Prediction → Feature Interpretation → Optimisation → Sensor Ranking → POC Application

 Tools Covered

Python, scikit-learn, XGBoost, SHAP, pandas, Plotly, Google Colab

Who Should Enrol?

Important Dates

Workshop Outcomes

Fee Structure