Technology Bundle ID
NCI-E-084-2019

Automated Digital Pathology Device for High-Throughput Demand

Applications
Non-Medical Devices
Medical Devices
Lead Inventors
Zhengping Zhuang (NCI)
Co-Inventors
Anthony Cappadona (NCI)
Young-Won Moon (AIPATec Inc)
Development Status
Prototype
ICs
NCI

Computer and imaging technologies led to the development of digital pathology and the capture and storage of pathological specimens as digitally formatted images. The use of artificial intelligence (AI) in digital pathology, such as in three-dimensional (3D) reconstruction, requires analyses of high volumes of data. This resulted in increased demands for processing and acquisition of digital images of pathology samples. Increased usage cannot be met by the time-consuming, manual, and laborious methods currently used. Therefore, there is a need for automation of the techniques used in processing of pathology samples and acquisition of digital images to make them amenable with high-throughput approaches like AI analysis.

National Cancer Institute inventors are developing an automated device with integrated tissue sectioning, staining, scanning, and high-throughput capability. This device integrates pathology sample processing (e.g., sectioning, fixing, and staining) with optical scanning and digital image acquisition. This streamlines the entire process enabling high-throughput preparation of large volumes of samples and data for subsequent AI analysis. As a result of automation, the device saves time, minimizes errors, and reduces wasting reagents and supplies.

The NCI is seeking licensees to develop an automated digital pathology device compatible with high-throughput data analysis.

Commercial Applications

Biopsy sample processing in pathology labs, hospitals, research labs
Applicable to diagnoses of various disease indications, including cancer and infectious diseases

Competitive Advantages

Facilitates processing and imaging of large volumes of pathology samples
Automation saves time, increases reproducibility, and minimizes errors
Compatible with high-throughput processes, e.g., AI analysis of digital pathology images and 3D reconstruction

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