Multimodal AI for Biomedical Signal & Imaging Analysis

Modeling with image/waveform data and multi-omics integration for disease/target research, perturbation studies, clinical applications, and digital twin development.

Outcomes Case Studies  
Context

From Biomedical Data to Biological Understanding

The goal of modern drug discovery is to understand physiology and derive actionable insights from biomedical data – spanning imaging, radiomics, and physiological waveforms. At its core, this comes down to answering a few key questions:

  • What does healthy physiology look like?
  • How does it change in disease?
  • What happens when we intervene (gene perturbations or therapeutics)?
  • Does the system return toward a healthy state, or diverge further?
  • Are there early signals of toxicity or unintended effects?

Biomedical signals, such as imaging and waveforms, enable a seamless progression from understanding physiology → simulating response → guiding intervention.

Outcomes

Actionable Biological Insights Across the R&D Continuum

Quantitative Tissue Phenotyping for Target & Biomarker Discovery

Move beyond visual histopathology to high-resolution, quantitative tissue characterization.

  • Cell-type detection, spatial distribution, and heterogeneity mapping
  • Tumor grading and microenvironment profiling
  • Extraction of morphology-derived biomarkers at scale

Impact:
 Enables identification of novel targets, disease signatures, and stratification biomarkers directly from tissue architecture – supporting early-stage discovery and precision medicine strategies.

Mechanism-of-Action (MoA) and Phenotypic Profiling from Cellular Imaging

Leverage high-content imaging (e.g., cell painting) to decode cellular responses at scale.

  • Phenotypic fingerprinting across perturbations (genetic or chemical)
  • MoA prediction via morphological clustering
  • Disease signature profiling and compound classification

Impact:
 Accelerates hit validation and functional understanding, enabling researchers to connect compound activity to biological pathways without relying solely on target-based assays.

High-Content Screening and Efficacy Assessment in Complex Models

Quantify drug response in organoids, 3D cultures, and physiologically relevant systems.

  • Growth kinetics, viability, and morphological response tracking
  • Toxicity screening and phenotypic shifts
  • Differentiation and structural organization analysis

Impact:
 Improves translational relevance of preclinical studies, enabling more reliable assessment of efficacy and toxicity before clinical stages.

Predictive Imaging Biomarkers for Clinical Translation

Extract high-dimensional features from radiology and imaging modalities to enable non-invasive prediction.

  • Radiomics-based feature extraction (shape, texture, intensity)
  • Disease phenotyping and progression modeling
  • Treatment response and risk prediction

Impact:
 Supports patient stratification, trial design, and endpoint prediction, bridging preclinical insights with clinical decision-making.

Gene Expression and Molecular Insight from Imaging (Spatial & FISH Analysis)

Decode molecular signals directly from imaging data.

  • Gene amplification and chromosomal abnormality detection
  • Spatial gene expression mapping
  • Integration with transcriptomics and multi-omics

Impact:
Enables spatially resolved biology, linking genotype to phenotype and unlocking insights not accessible through bulk omics alone.

Multimodal Integration: Imaging as a Core Layer in AI-Driven Biology

Integrate imaging with genomics, transcriptomics, and clinical data.

  • Imaging + CRISPR perturbation analysis
  • Imaging-driven reconstruction of gene networks
  • Multimodal AI models for prediction and discovery

Impact:
 Transforms imaging from a standalone modality into a central component of multi-scale biological modeling, enabling deeper mechanistic insights and improved predictive power.

Case Studies

Applied Case Studies

Myotube Fusion Analysis (Muscle Differentiation)

Quantitative analysis of skeletal muscle differentiation using iPSC-derived cells

  • Key readouts:
    • Myotube length
    • Nuclei per muscle fiber
    • Fusion index
  • Enables comparison of wild-type vs diseased states
  • Extends to 3D muscle models, capturing structural organization and maturation

Insight: Tracks how disease alters muscle formation and whether interventions restore normal differentiation

Adipocyte Phenotyping (Metabolic State Characterization)

Multi-channel imaging to capture cellular and metabolic state

  • Key features analyzed:
    • Lipid droplet accumulation (adipogenesis)
    • Mitochondrial morphology (energy utilization)
    • Cytoskeletal organization (cell shape changes)
    • Cell boundary + nuclear quantification (size, proliferation)

Insight: Links morphological features to metabolic function, enabling precise characterization of adipocyte health and response

H&E-Based Tissue Analysis (Disease Pathology)

  • ML-driven analysis of histopathology images (e.g., DMD vs healthy muscle)
  • Detects:
    • Fiber size variability
    • Degeneration and regeneration markers
    • Structural abnormalities (e.g., absence of tight junctions)

Insight: Differentiates healthy vs diseased tissue architecture, enabling objective and scalable pathology assessment.

Why Aganitha

The Aganitha Advantage

Biology-First AI

Our models are designed with domain expertise, trained and validated by scientists who understand the biological context, not just the pixel statistics.

2D to 3D Native

Native support for 2D and 3D imaging modalities, including organoid and tissue section analysis, without compromising on resolution or throughput.

Label-Free Intelligence

Brightfield and virtual staining pipelines reduce dependency on expensive fluorescent protocols while preserving analytical richness across phenotypic endpoints.

Multi-Modal Ready

Engineered for integration with genomics, transcriptomics, and proteomics, delivering a unified view of biological state that imaging alone cannot provide.

Discover our offerings across the biopharma value chain

Our Solutions

Customizable platforms and solutions across the drug discovery value chain from target discovery to therapeutic development
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Our Services

Offering services in computational sciences and technology to complement biopharma R&D
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Case Studies

Helping clients accelerate drug discovery and development by building computational biology, computational chemistry, technology, and cloud solutions
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Talk to our imaging AI team about your specific cell biology or discovery challenge.

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