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Updated: Jan 22, 2026
09:11
Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
Published on: January 27, 2023
2.7K
Artificial intelligence, machine (deep) learning and radio(geno)mics: definitions and nuclear medicine imaging
Dimitris Visvikis1, Catherine Cheze Le Rest2,3, Vincent Jaouen2
1LaTIM, INSERM UMR 1101, IBRBS, Faculty of Medicine, Univ Brest, 22 avenue Camille Desmoulins, 29238, Brest, France. dimitris@univ-brest.fr.
Summary
Artificial intelligence (AI) and deep learning methods are revolutionizing medical imaging. This review explores AI applications in nuclear medicine, particularly for radio(geno)mics.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Nuclear Medicine
Background:
- Machine learning, a subset of artificial intelligence, is central to recent advancements in medical imaging.
- These AI techniques are applied across various imaging tasks, including reconstruction, denoising, segmentation, analysis, and predictive modeling.
Purpose of the Study:
- To introduce and define key artificial intelligence and machine learning concepts.
- To discuss the application of these AI techniques in nuclear medicine imaging.
- To focus specifically on the integration of AI with radiogenomics.
Main Methods:
- Review of current literature on artificial intelligence in medical imaging.
- Definition and explanation of machine learning and deep learning principles.
- Exploration of AI's role in nuclear medicine workflows and radiogenomics.
Main Results:
- AI and deep learning are integral to modern medical imaging development.
- These methods offer solutions for image reconstruction, processing, analysis, and prediction.
- Significant potential exists for applying AI in nuclear medicine, especially for radiogenomics.
Conclusions:
- Artificial intelligence and deep learning are transformative in medical imaging.
- Nuclear medicine can greatly benefit from the adoption of AI techniques.
- AI-powered radiogenomics holds promise for enhanced diagnostic and prognostic capabilities.
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