DxH 520

Analyze DxH 520 Hematology Analyzer data with AI

Automated hematology analyzer for complete blood count and differential analysis

Test Parameters: 23+ reportable parameters
Throughput: Up to 120 samples per hour
Sample Volume: 13 μL sample volume
Technology: Volume, Conductivity, Scatter (VCS)
Data Export: CSV, LIS connectivity
Get Started For Free Fully compatible with Sourcetable

Quick Start Integration

Get your DxH 520 data into Sourcetable in 3 simple steps

  • Step 1

    Export Data

    Export complete blood count data from your DxH 520 analyzer to CSV or through LIS connectivity

  • Step 2

    Upload to Sourcetable

    Import your hematology data into Sourcetable for comprehensive analysis

  • Step 3

    Start Analysis

    Perform automated hematology analysis and blood cell population studies

File Format Compatibility

Optimized for hematology data analysis with complete blood count interpretation

.csv

Raw Data

Full Support CBC parameters, differential counts, cell population analysis, histogram interpretation

.xlsx

Fully Formatted

Full Support Hematology reports with reference range analysis

Pre-built Analysis Workflows

Clinical workflows for hematology analysis and blood cell population studies

🔬

Complete Blood Count

Comprehensive analysis of CBC parameters and differential counts

CBC Analysis Hematology Blood Count
📊

Cell Population Analysis

Analysis of blood cell populations and morphology patterns

Cell Populations Morphology Analysis Blood Cells
🏥

Clinical Hematology

Clinical interpretation of hematological parameters

Clinical Hematology Reference Ranges Diagnostic Analysis

Technical Requirements

Software Dependencies

  • DxH 520 Automated hematology analyzer with VCS technology
  • Control Software DxH SMS data management system
  • File Export Format CSV, LIS connectivity
  • Applications Clinical labs, hospitals

Sourcetable Libraries

  • Data Processing pandas, numpy
  • Statistical Analysis scipy, statsmodels
  • Visualization matplotlib, plotly
  • Hematology Analysis scikit-learn, seaborn

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Ask questions in plain English, generate charts instantly, use Python for custom logic, and tap into built-in research tools for deeper insights.

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