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MedTech Outlook | Wednesday, February 01, 2023
Predictive analytics has become increasingly important to doctors in the age of precision medicine.
FREMONT, CA: In scientific laboratories, digital pathology plays an increasingly vital role in clinical practice. Pathologists can now manage digital slide images and share them for clinical use easier with the advent of whole-slide imaging, faster networks, and cheaper storage solutions. AI and digital pathology now offer image-based diagnostic possibilities previously restricted to radiology and cardiology due to unprecedented advances in machine learning.
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As digital imaging advances and glass slides are rapidly digitized, deep learning and machine learning algorithms can now be used to create potentially novel and innovative diagnostic tools. By combining DL and AI tools with digital pathology images, digital pathology can be far more valuable than it is today and quantified above. Using AI tools in diagnostic pathology will help transform diagnostic pathology, and this is the real value of digital pathology.
Application in digital pathology
There was a great deal of effort put into overcoming the challenges with traditional pathology methods. As a result of technological advancements, digital pathology-based quantitative assessment methods have been developed, including WSIs scanning and AI-based models that assist pathologists in extracting information beyond their visual perceptions. Most unimodal research is conducted using pathology images and corresponding clinical information, and clinical records alone provide the label for modeling without any complex wet experiments. AI-based models for clinical applications and translations are best suited to unimodal research with clear and simple goals.
AI:As AI is applied to digital pathology, its application scenarios have gradually expanded. Computer vision techniques in classification, segmentation and detection have been widely used on various topics and organs. An important part of the research involves auxiliary functions that enhance image quality, identify cells, type tissues, store data, and direct applications, including diagnosis, stratification by the patient, prognosis, treatment response, and survival prediction. Artificial intelligence research focuses on developing clinical application platforms that make it possible to select and stratify patients for treatment based on algorithms.
Digital imaging:As digital imaging advances and glass slides are rapidly digitized, deep learning and machine learning algorithms can now be used to create potentially novel and innovative diagnostic tools. By combining DL and AI tools with digital pathology images, digital pathology can be far more valuable than it is today and quantified above. Using AI tools in diagnostic pathology will help transform diagnostic pathology, and this is the real value of digital pathology.
In the delivery of precision medicine, digital pathology coupled with artificial intelligence offers great potential. Multimodal data cooperation is gradually replacing understanding unimodal data as the research problem.
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