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Pharma Tech Outlook | Tuesday, July 21, 2020
Preclinical imaging is a robust research and diagnostic tool, and image analysis plays an essential role in it.
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Fremont, CA: Currently, studies leveraging MRI, PET, and optical imaging technologies among the others are playing a vital role in drug discovery and development. Preclinical imaging enables the pharmaceutical industry to save time and money by assessing drug candidates' effects earlier in the drug pipeline, which ultimately helps in getting new drugs to the patients in real-time. The incorporation of artificial intelligence in image data analysis is advancing the field, enabling the development of innovative solutions where biology, physics, and computer science intersect.
Machine learning meets biology
The adoption of machine learning in image analysis and visualization is rapidly advancing the value of preclinical imaging in drug development. A newer subset of machine learning, deep learning, leverages a network of multiple algorithms that may interpret the data in various ways; it is also known as an artificial neural network. Before the introduction of machine learning, they maximized automation of the routine imaging data processing pipelines for tasks like converting, exporting, grouping, and quantifying data, so that the imaging data can be batch-processed on a dedicated server with no user input. But all other data analysis tasks still need considerable amounts of manual work. With the introduction of new machine learning tools, it is possible to minimize human errors and bias, and fast-pace the process by several orders of magnitude.
[vendor_logo_first]Development in image analysis speed small animal imaging
Companies have developed a fully automated platform for fast cloud-based data analysis of small animal imaging. Bringing together whole animal imaging data sets is difficult due to differences in animal sizes and physical positions.
AI for image analysis in high content screening
With 3-dimensional cell cultures becoming more physiologically relevant disease models, their greater complexity means that the datasets are more extensive and more challenging to analyze than traditional 2-dimensional cell cultures. Companies are embracing the challenge of using AI to advance data analysis in their imaging software.
Pharmaceutical companies are using AI-based methods to automate analysis tasks like identifying sets of parameters, which helps optimize the segmentation and classification of data.
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