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Imaging Biological Samples with Optical Microscopy

Imaging Biological Samples with Optical Microscopy

Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...

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Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

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MI-Common Data Model: Extending Observational Medical Outcomes Partnership-Common Data Model (OMOP-CDM) for

Varvara Kalokyri¹, Haridimos Kondylakis¹, Stelios Sfakianakis¹

¹Institute of Computer Science, Foundation of Research and Technology Hellas, Heraklion, Greece.

JCO Clinical Cancer Informatics

|December 7, 2023

Summary

A new Medical Imaging Common Data Model (MI-CDM) extends OMOP-CDM to integrate imaging and clinical data. This harmonization facilitates AI model development and other data-driven research by standardizing imaging metadata.

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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Area of Science:

Health informatics
Medical imaging analysis
Artificial intelligence in healthcare

Background:

The proliferation of big data and AI necessitates harmonized health data.
Common Data Models (CDMs) like OMOP-CDM standardize clinical information.
Integrating medical imaging metadata with clinical data remains a significant challenge.

Purpose of the Study:

To introduce a novel extension of the OMOP-CDM for medical imaging data.
To enable the seamless integration of imaging metadata with clinical data.
To support AI model development and other data-dependent use cases.

Main Methods:

Developed the Medical Imaging Common Data Model (MI-CDM) as an extension of OMOP-CDM.
Collaborated with clinical and AI experts to refine modeling choices.
Applied MI-CDM to a prostate cancer use case within the ProCAncer-I project.

Main Results:

Successfully implemented MI-CDM for prostate cancer, integrating imaging, curation, and clinical data.
Demonstrated the capability to link imaging metadata with clinical information using OMOP-CDM and its oncology extension.
Validated the MI-CDM's effectiveness in a real-world clinical research context.

Conclusions:

The proposed MI-CDM extension effectively addresses the challenge of integrating medical imaging data.
Standardized attributes and terminologies within MI-CDM facilitate the integration of diverse imaging modalities.
This approach enables harmonized, quality-checked, and annotated imaging and clinical data for advanced research.