Scopio Labs


Provider of full-field, high-magnification digital microscopy and AI-driven image analysis for hematology. Offers integrated slide scanning hardware and software for full-slide high-resolution capture, machine-learning models for cell detection, segmentation and classification, browser-based remote review and laboratory interoperability with standard interfaces. Publishes multicenter clinical validation data for peripheral blood smear and bone marrow aspirate workflows; some autonomous reporting features are described as under development and subject to regional regulatory review.

Industries

Artificial Intelligence
Artificial Intelligence (AI)
Health Diagnostics
Image Recognition
Information Technology
Medical Device
Medical Equipment Manufacturing

Nr. of Employees

medium (51-250)

Scopio Labs

Tel Aviv-Yafo, Tel Aviv District, Israel


Patents

Compressed acquisition of microscopic images

US-12489872-B2

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Multi/parallel scanner

US-12061207-B2

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Multi/parallel scanner

US-11549955-B2

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Adaptive sensing based on depth

US-11482021-B2

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Digital microscope which operates as a server

US-10935779-B2

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System for image reconstruction using a known pattern

US-10558029-B2

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Products

Peripheral blood smear imaging and analysis application

End-to-end digital peripheral blood smear workflow providing full-field high-magnification imaging, AI-assisted detection, classification and quantitation of blood cells, and draft report generation for LIS integration; availability is region-dependent and subject to regulatory clearance for certain use cases.

Bone marrow aspirate imaging and analysis application

Full-field digital imaging workflow for bone marrow aspirate slides with AI decision support for region selection, cell pre-classification and structured draft reporting; includes support for AI-assisted differential counts (reported support for 500-cell differentials) and remote review capabilities; regional availability varies.

High-throughput slide scanning platforms

Slide scanner platforms with automated handling and configurable throughput options designed for clinical laboratory integration and digitization.

Autonomous morphology reporting pipeline (development-stage)

Under-development autonomous analysis pipeline combining full-field imaging and AI to auto-report routine peripheral blood smear results downstream of CBC analyzers; described as development-stage and pending regulatory clearance for some regions.


Services

Implementation and operational deployment of full-field slide imaging and AI-assisted morphology workflows, including network integration, LIS connectivity, staff training and staged rollouts.

Expertise Areas

  • Full-field digital microscopy and telehematology
  • AI and deep-learning development for blood-cell detection, segmentation and classification
  • Clinical validation and multicenter performance evaluation
  • Bone marrow aspirate digitization and AI-assisted differential counts
  • Show More (4)

Key Technologies

  • Full-field high-magnification imaging (100x-equivalent)
  • Computational-photography image reconstruction
  • GPU-accelerated image processing
  • Deep convolutional neural networks for cytomorphology
  • Show More (6)

News & Updates

Overview of operational and organizational benefits of laboratory digitization, including workload balancing, remote review, throughput improvements and case examples from clinical sites.

Discussion of full-field imaging, AI decision support, and case-based examples demonstrating diagnostic impact and the benefits of comprehensive high-magnification slide review.

Overview of full-field digital morphology, its advantages over manual methods, remote review benefits, and references to clinical studies demonstrating workflow and turnaround-time improvements.

Summary of digital pathology concepts including full-field imaging, AI integration, telepathology, and opportunities for analytics and biomarker discovery.

Description of AI-enabled RBC morphometric decision support that analyzes large cell populations, platelet-clump detection and full-field capture of feathered edges to improve detection of rare events.

Discussion of opportunities and challenges for clinical AI in hematology, including data-quality, user interface design, validation needs and staged clinical adoption.

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