To develop AI platforms that infer molecular information from pathology slides, making insights more accessible and faster for clinicians.
Approach:
Path2Omics: Predicts bulk molecular information from pathology images, focusing on inferred bulk transcriptomics and methylation across 30 tumor types.
Path2Space: Aims to infer spatial transcriptomics from digital pathology images by linking image morphology to spatial molecular data.
Key Findings:
AI can potentially reduce the cost and turnaround time for obtaining molecular insights from weeks to days.
Path2Omics provides inferred bulk omics data from standard pathology slides without the need for extensive molecular profiling.
Path2Space aims to infer spatial transcriptomics from digital pathology images, linking image morphology to spatial molecular data.
Interpretation:
Limitations:
The effectiveness of inferred spatial information in improving decision-making remains to be fully validated.
