Project Summary | Automated Cell Culture Monitoring

Project Summary: Automated Cell Culture Monitoring

ML-powered automated measurement and health classification for bioprocess cell culture monitoring.

Bioprocess cell culture monitoring system with ML classification

Project Snapshot

Role: ML Solution Lead
Domain: Life sciences / bioprocess monitoring
Stack: Python, computer vision, ML classification, image processing, automated measurement
Timeline: Research-to-production deployment

Intelligent process control

ML-Powered Bioprocess Control

Machine learning classifier enabling real-time bioprocess decisions based on automated culture health assessment.

Measurement automation

Automated Measurement

End-to-end automated image processing pipeline replacing manual measurement and subjective visual assessment.

Quality classification

Health Classification

Consistent, reproducible classification of cell culture health and performance trends across production batches.

Technical Architecture

graph TD
    subgraph Imaging
        A[Culture Bag Imaging] --> B[Image Preprocessing]
    end

    subgraph Feature_Engineering
        B --> C[Feature Extraction]
        C --> D[Morphological Features]
        C --> E[Density Features]
    end

    subgraph Classification
        D --> F[ML Classifier]
        E --> F
        F --> G[Health Classification]
    end

    subgraph Monitoring
        G --> H[Performance Trending]
        H --> I[Alerts]
        I --> J[Process Intervention]
    end

Architecture: Culture bag imaging feeds into image preprocessing and feature extraction. Morphological and density features are classified by a supervised ML model to determine culture health. Results drive performance trending and alert-based process intervention.

Decision Tradeoffs

Option Considered	Pros	Cons	Decision
Supervised ML Classifier	Interpretable, works with limited labeled data, fast inference	Requires feature engineering, may miss subtle patterns	Selected — interpretable and effective with limited training data
Deep CNN	Automatic feature learning, potentially higher accuracy	Needs more training data, black-box predictions	Considered — more powerful but requires more labeled data than available
Rule-Based Thresholds	Simple to implement, no training needed	Too brittle for biological variation, high false positive rate	Rejected — too brittle for the natural variation in biological systems

Problem

Cell culture bag monitoring was manual, subjective, and couldn't scale across production. Operators assessed culture health visually, leading to inconsistent evaluations and delayed interventions.

Approach

Built an ML system for end-to-end automated measurement and classification of cell culture health and performance trends from imaging data. The pipeline handles image preprocessing, feature extraction, and supervised classification with interpretable outputs.

Outcome

Automated monitoring replaced subjective manual assessment. Enabled consistent, scalable bioprocess quality control with reproducible health classifications across production batches.

Leadership Contribution

Architecture: Designed the imaging-to-classification pipeline and selected supervised ML over deep learning for interpretability with limited data.
Team: Coordinated with bioprocess scientists and quality engineers to define health classification criteria and validate model outputs.
Governance: Established model validation protocols including cross-validation against expert assessments and ongoing drift monitoring.
Outcomes: Tracked classification accuracy, measurement reproducibility, and reduction in subjective assessment variability.

Discuss Similar Work Back to Portfolio