Reveal the Power of AI: Quantify.  Explore.  Predict. 

A new generation of data powered pathology to enhance research and improve global healthcare.

AI Pathology: Quantify specific cell types & tissue features.

Quantify.

Quantify specific cell types
& tissue features.

AI Pathology: Explore the tissue micro- environment with spatial data.

Explore.

Explore the tissue micro-environment
with spatial data.

AI Pathology: Predict responders & stratify patient groups.

Predict.

Predict responders & stratify
patient groups.

AI IN ACTION

Quantify.

AI-powered identification & classification tools generate data from brightfield and fluorescent images. Generate cell-by-cell data, quantify within subregions, and perform feature-based analysis across multiple applications.

Immunohistochemistry
Biomarker positivity data including percent positivity, intensity, and cell counts.
NASH / NAFLD
Automated NASH scoring in the cloud including steatosis, inflammation, fibrosis, and ballooning data.
In Situ Hybridization
Automated mRNA data including binned positive spot counts from brightfield and IF images.
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Explore.

Expand your dataset with spatial information including proximity analysis with heatmap visualization.  Discover highplex spatial insights with Akoya & Ultivue panels, explore the tumor stroma interface, and generate co-localization data.

Multiplex Immunofluorescence
Biomarker positivity data from multiplex IF assays with powerful co-localization data.
Spatial transcriptomics
Comprehensive cluster analysis to generate spatial data from high-plex stained images.
Multiplex IHC
Biomarker positivity data from multiplex IHC assays.
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Predict.

Apply the latest advances in predictive AI to uncover new data with validated, end-to-end models supporting regulatory compliance & scale.  Predict treatment outcomes, patient responders, genomic status, and more.

Predict Genomic Status
Color-coded classification of tiles from an H&E stained colon biopsy for dMMR prediction.
Predict Patient Outcomes
Triage large, multi-omic data sets to predict patient outcomes.
Virtual IHC Prediction
Predict positively stained IHC cells from an H&E stained slide without a lab experiment.
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Assay development & deployment workflow

Training Data Aggregation

  • Petabyte scale searchable data lake
  • Complete data traceability through unique DVC identifiers
  • Compliant data security & encryption with full data redundancy
  • Automated QC and performance tracking

Ground Truth

  • Comprehensive ground truth flexibility
  • Full suite of image annotation tools with administrator visibility & complete traceability
  • Board-certified pathologist oversight
  • Lab generated biomarker based options

Digital Assay Development

  • Defined inputs & outputs
  • Performance metrics with clinical utility
  • Full suite of validated operators
  • SDLC tracking with auditable history
  • Unit & integration testing at the data level

Validation & Deployment

  • Optimized workflow & global deployment in the cloud
  • CAP/CLIA sites in N. America, Europe & Asia
  • CDx development & validation through SELECT
  • Comprehensive QMS & single site PMA

Predict GENOMIC status FROM H&E SLIDES

Reveal’s attention-based multi-instance learning (MIL) model facilitates the accurate prediction of genomic status from H&E stained slides. This approach can be used to rapidly triage patient samples, reducing costs and turn around time.  Our high model performance metrics support clinical utility with examples shown below.  

Contact us to learn more about custom assay development using the MIL model.

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Model Specificity
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Model Accuracy

The Impact of AI: Detecting mis-match repair (MMR) deficiency is often critical to determine whether patients would benefit from immunotherapy. An AI model was developed to detect MMR status from H&E stained colon biopsies.  Download our poster to learn more.

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