Fusion Hematology
by Techcyte
· Based in United States →
— Our AI technology uses the power of digital differentials to aid the field of hematology in determining diagnoses quickly.
Contact for pricing
Regulatory Status Disclosed
Overview
Techcyte Fusion Hematology is an AI-powered digital differential platform designed to assist in the analysis of blood smears. It is intended for use by medical technologists and hematopathologists in clinical laboratory settings.
- What it does: The system utilizes deep machine learning to analyze 40x digital images of blood smears, performing a pre-classification of white blood cells, red blood cells, and platelets. This aims to streamline the review process for technologists.
- Who it is for: Fusion Hematology is for clinical laboratories and professionals involved in hematology, including medical technologists and hematopathologists. It is part of the broader Techcyte Fusion platform, which unifies anatomic and clinical pathology workflows.
- How it fits a clinical or practice workflow: After manual slide preparation and scanning with a compatible scanner, digital images are uploaded to the Techcyte platform for AI analysis. The AI provides proposed classifications of blood cells and objects of interest. Technologists then review these AI-driven results on a web-enabled device, confirm findings, and can send results to their Laboratory Information System (LIS). The platform is designed to integrate with existing LIS, EHR, and slide scanners.
- Notable capabilities: The system is designed to classify and count various blood components, including white blood cells, red blood cells, and platelets (including abnormal and aggregated forms). It is compatible with various scanners and does not require proprietary hardware. Techcyte Fusion Hematology is CE-IVDD marked for use in Europe and is for research use only in the United States.
Reviewed by Pouyan Golshani, MD — Interventional Radiologist
Key Features
- AI-based automated blood cell differential
- Pre-classification of blood cells (WBCs, RBCs, Platelets, nRBC, blasts, etc.)
- Aids in rapid diagnosis
- No proprietary hardware required
- Less sensitive to stain variations
- Technician review of AI-driven results for confirmation
- May reduce read times to 30 seconds
- Object counts and abnormality detection
- LIS integration
- Customizable workflows
Use Cases
- Automated blood cell differentials for efficiency and accuracy
- Assisting hematopathologists and medical technologists in diagnostics
- Reducing eye strain, physical fatigue, and human error in lab analysis
- Digital repository for valuable slides and educational purposes (via Fusion Education Suite)
- Streamlining laboratory workflows for high-throughput environments
- Remote collaboration and LIS reporting
What Physicians Need to Know
LIS/LIMS Integration
Techcyte Fusion Hematology offers seamless and bidirectional integration with Laboratory Information Systems (LIS) and Laboratory Information Management Systems (LIMS). It supports industry standards such as DICOM, FHIR, and HL7, enabling connectivity with EHR, grossing, and radiology systems for a unified pathology workflow.
Supported Assay Types
The primary focus is on Automated Blood Differential, classifying and counting white blood cells (including various types like neutrophils, lymphocytes, blasts, and nRBCs), red blood cells, and platelets (including abnormal and aggregated forms). The broader Fusion platform also supports parasitology, bacteriology, and cervical cytology.
QC/QA Features
The platform streamlines quality control workflows and includes AI-enhanced QC tools for tasks like focus checks and annotations. It allows for customization of QC metrics to align with specific laboratory protocols and preferences.
Result Interpretation AI
Utilizes AI-based algorithms and deep machine learning to analyze 40x images, identify the monolayer, and perform pre-classification of blood cells and other diagnostically significant objects. The AI proposes images grouped by class and sorted by confidence, aiding technologists in efficient review. It also supports AI-assisted reporting and annotation, integrating Techcyte's own AI, third-party AI, and custom institutional AI models.
Critical Value Alerts
While the platform integrates with LIS, which typically handles critical value reporting, Fusion Hematology's AI-driven pre-classification and rapid identification of abnormalities contribute to the timely recognition of potentially critical findings. The system helps in speeding triage and determining if further tests are needed, thereby supporting the critical value workflow.
Instrument Interfaces
Techcyte Fusion Hematology is a software-only solution compatible with any good-quality 40x image from a wide range of compatible scanners, supporting all major scanner vendors.
Reference Range Intelligence
The system allows for customization of semi-quantitative ranges for the peripheral blood smear classifier, enabling laboratories to match the AI's output with their specific protocols and patient populations.
Turnaround Time Impact
The AI-assisted screening tool significantly reduces technologist read time, potentially bringing it down to 15-30 seconds per slide from 5-10 minutes. This speeds up triage, makes cases available for review within 7 minutes after scanning, and generally improves overall turnaround time for faster, more confident diagnostic results.
Microbiology ID Support
The broader Techcyte Fusion platform includes a Fusion Bacteriology Suite, indicating support for microbiology identification workflows, which can be relevant for comprehensive laboratory diagnostics.
Physician Tip
For physicians, Fusion Hematology offers the potential for faster and more consistent blood differential results, reducing diagnostic delays. The AI's pre-classification helps prioritize cases, allowing pathologists to focus on complex or abnormal samples. The integrated platform facilitates collaboration and access to comprehensive patient data, leading to more informed diagnostic decisions. Be aware that while the AI assists in identification, final diagnostic sign-off remains with the pathologist.
Techcyte Fusion is designed as an open, standards-based, cloud-native SaaS platform, ensuring broad interoperability. It seamlessly integrates with existing LIS, EHR, and imaging systems (supporting DICOM, FHIR, HL7). The platform also features an AI marketplace, allowing for the integration of Techcyte's proprietary AI, third-party AI solutions, and custom institutional AI models, providing flexibility and scalability for evolving lab needs.
Details
| Category | Lab & Diagnostics, Pathology AI |
| Pricing | Contact for pricing — Custom pricing based on lab size, throughput needs, and specific modules required. |
| Deployment | Cloud-based, web-enabled platform accessible on any web-enabled device. |
| Compliance | |
| BAA Available | Unknown AI-estimated |
| HIPAA Compliant | Unknown AI-estimated |
| FDA Status |
Pending AI-estimated Currently for Research Use Only (RUO) in the U.S. for Fusion Hematology. Techcyte is in correspondence with the FDA for regulatory approval, and FDA submissions are in process. It is CE-IVDD marked in Europe. |
| Integrations | |
| EHR | Not specified |
| Specialties | Hematology, Laboratory Medicine, Pathology |
What the Web Says
Fusion Hematology by Techcyte receives generally positive feedback for its AI-powered digital morphology, aiming to improve efficiency and accuracy in hematology labs. Reviewers highlight its potential to reduce manual review time and standardize results, particularly benefiting labs with staffing shortages or high volumes. Some discussions point to the typical challenges of adopting new technology, such as integration and initial setup.
Overall: PositiveStrengths
- AI-powered digital morphology for increased accuracy
- Automates manual differential counts, saving time
- Standardizes results across different technicians
- Potential to address staffing shortages in labs
- Improved efficiency in high-volume laboratories
- Remote review capabilities for pathologists
Limitations
- Initial cost of implementation and hardware
- Integration challenges with existing LIS systems
- Learning curve for new technology adoption
- Reliance on internet connectivity for cloud-based features
- Potential for over-reliance on AI, requiring human oversight
- Specific validation required for each lab's workflow
Based on reviews from: Techcyte.com, G2, Capterra, Healthcare IT News, Laboratory Industry Publications, Reddit (general discussions on lab automation)
Last updated: 2026-08-10
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Press & Coverage
Techcyte Press Release
Techcyte unveils Fusion, the first unified anatomic and clinical digital pathology platform.
Techcyte announced the launch of Fusion, the first unified anatomic and clinical digital pathology platform, designed to revolutionize workflows and improve the lives of pathologists. This platform integrates seamless AI, smart workflows, and speed without compromise.
2025-03
Techcyte Publications
Detection of protozoan and helminth parasites in concentrated wet mounts of stool using a deep convolutional neural network.
This publication details research on the detection of protozoan and helminth parasites in concentrated wet mounts of stool using a deep convolutional neural network. This contributes to scientific progress in AI applications for pathology.
2025-10
Techcyte Press Release
BD, Techcyte Announce Strategic Collaboration to Offer AI-Based Digital Cervical Cytology System.
BD and Techcyte announced a strategic collaboration to offer an AI-based digital cervical cytology system. This partnership aims to enhance the identification of precancerous lesions and cancer cells.
2024-02
Techcyte Press Release
Mayo Clinic Platform and Techcyte Announce a Strategic Collaboration to Transform the Global Practice of Pathology.
Mayo Clinic Platform and Techcyte announced a strategic collaboration to transform the global practice of pathology. This collaboration focuses on creating new AI algorithms and workflows to enhance diagnostic testing.
2024-01
Techcyte Recent Articles
Techcyte Improves Digital Diagnostic Workflows For Clinical Pathology
Techcyte has added powerful platform workflow enhancements to improve the flexibility and efficiency of digital diagnostic workflows for various areas, including hematology. These improvements aim to address labor shortages and reduce microscopic analysis time.
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Techcyte Publications
ISLH Paper: White blood cell evaluation in hematological malignancies using a web-based digital microscopy platform.
This publication discusses the evaluation of white blood cells in hematological malignancies using a web-based digital microscopy platform, highlighting the application of Techcyte's technology in hematology.
2023-07
Techcyte Press Release
Techcyte Announces a Strategic Collaboration with Mayo Clinic to Create New Diagnostic Tests.
Techcyte announced a strategic collaboration with Mayo Clinic to develop new AI algorithms and workflows on the Techcyte Clinical Pathology AI Platform, aiming for increased accuracy and efficiency in diagnostic testing.
2023-03
Techcyte Publications
Techcyte's AI Platform Demonstrates High Sensitivity for Blast Identification in Malignant Films.
This publication showcases Techcyte's AI platform demonstrating high sensitivity for blast identification in malignant films, a crucial aspect of hematological diagnostics.
2021-06
Videos
Product demos, reviews, and walkthroughs for Fusion Hematology.
How Techcyte's Fusion Platform is Shaping the Future of Digital Pathology and AI Integration
Frequently Asked Questions
AI in Fusion Hematology integrates by analyzing vast, complex datasets from blood smears, flow cytometry, and genomic sequencing to identify subtle patterns and abnormalities, thereby improving diagnostic accuracy and efficiency. It assists in cell classification, early detection of malignancies, risk stratification, and personalizing treatment plans by predicting patient outcomes and responses to therapies.
The regulatory landscape for AI in healthcare is still evolving, with a critical need for clear guidelines and standards to ensure safety and efficacy. Data privacy is addressed through measures like encryption, anonymization, and privacy-preserving techniques to comply with regulations such as HIPAA and GDPR, given the extensive collection and processing of sensitive patient data.
Current alternatives primarily involve traditional manual analysis and interpretation of microscopic images, flow cytometry data, and genetic information by experienced human hematologists. These manual methods are often preferred or essential for validating AI outputs, reclassifying complex diseases, interpreting nuanced results, and particularly for rare conditions where AI models may lack sufficient training data.
The cost of AI solutions in Fusion Hematology varies significantly, ranging from approximately $50,000 to $500,000 annually for pre-built diagnostic tools, with full-system integrations potentially reaching $500,000 to $3,000,000 in the first year. Ongoing maintenance and updates typically add 15-25% of the initial integration cost per year.
Key limitations include the susceptibility of AI models to biases from their training datasets, which can restrict their generalizability and lead to underperformance in detecting rare pathologies or novel presentations. The 'black-box' nature of some algorithms also poses challenges for interpretability and transparency, hindering clinician trust and accountability in decision-making.
Patient data within AI platforms in Fusion Hematology is secured through robust measures such as real-time anomaly detection, automated response systems, and advanced data encryption for data both in transit and at rest. Additionally, techniques like data anonymization and federated learning are employed to protect sensitive patient information and ensure compliance with privacy regulations like HIPAA.
Rigorous clinical validation and evidence are essential for AI algorithms in Fusion Hematology, comparable to the standards for new therapeutic strategies in multicenter clinical trials. This includes testing on diverse, multi-institutional datasets to ensure generalizability and address potential algorithmic biases, as well as continuous validation due to the dynamic nature of hematologic diseases.
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| Last Verified: 2026-05-12 | First Added: 2026-05-12