Abstract

Abstract This study focuses on the integration of Artificial Intelligence (AI) with Spatially Resolved Laser-Activated Cell Sorting (SLACS) for enhanced pathological analysis and biomarker discovery. The primary approach involves using AI to automatically analyze slide images, identifying critical areas associated with diseases such as cancer or cells emitting specific signals. AI algorithms efficiently segment these significant regions or cellular groups, facilitating targeted analysis. Following the AI-driven segmentation, SLACS comes into play, isolating the identified cells from the slide for further examination. This precise sorting allows for the focused study of cells relevant to disease processes, enhancing the efficiency and accuracy of pathological analysis. The separated samples obtained through SLACS are then subjected to detailed analysis. This step is crucial for understanding the cellular mechanisms at play in disease states and for the identification of potential biomarkers. These biomarkers are vital for developing targeted therapeutic strategies and advancing personalized medicine. By combining AI's advanced image processing and pattern recognition capabilities with the precision of SLACS, this integrated approach significantly improves the identification and analysis of disease-related cellular characteristics. It streamlines the process of isolating and studying specific cell populations, contributing to a deeper understanding of disease mechanisms and progression. In conclusion, the synergistic application of AI and SLACS represents a significant advancement in pathological research and biomarker discovery. This methodology not only enhances the accuracy of disease marker identification but also opens new avenues for research and clinical application. Citation Format: Haewook Jang, Amos Chungwon Lee, Sumin Lee, Sunghoon Kwon. Spatially Resolved Laser-Activated Cell Sorting (SLACS) and AI integration for enhanced pathological analysis and biomarker discovery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3533.

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