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5 October 2023 Virtual biomarkers: an emerging high throughput research tool
Yair Rivenson
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Proceedings Volume PC12655, Emerging Topics in Artificial Intelligence (ETAI) 2023; PC1265506 (2023) https://doi.org/10.1117/12.2673868
Event: SPIE Nanoscience + Engineering, 2023, San Diego, California, United States
Abstract
Pathology underlies every facet of healthcare, influencing greater than 70% of all medical decisions, every phase of pre-clinical and clinical drug development, every tumor repository and biobank, and an ever-increasing majority of standard and companion diagnostics for precision cancer care. However, such studies, whether performed traditionally via visual microscopy or via newer Artificial Intelligence (AI) enhanced image analyses, are all limited by the number of markers – typically immunologic – which can be performed on dwindling, aging samples that must also be preserved for downstream multiomics analysis. In this talk, we’ll demonstrate how we intend on altering the centuries old practice of histopathology with a digitized process to in a non-destructive fashion, enabled by a machine-learning-based virtual staining technology, that enables fully digital, virtual multiplex tissue platform to substantially improve the quality and quantity of pathology samples by protecting sample integrity, minimizing pre-analytic degradation of target analytes, and revolutionizing storage and processing of cancer-relevant biospecimens. We’ll also discuss additional benefits of the technology, such as lab sustainability, and digital outputs could be seamlessly integrated into downstream AI image analysis software, thereby providing total characterization of cellular processes within minutes.
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© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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Yair Rivenson "Virtual biomarkers: an emerging high throughput research tool", Proc. SPIE PC12655, Emerging Topics in Artificial Intelligence (ETAI) 2023, PC1265506 (5 October 2023); https://doi.org/10.1117/12.2673868
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KEYWORDS
Artificial intelligence
Biological samples
Image analysis
Pathology
Statistical analysis
Sustainability
Tissues
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