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Flow cytometry is a laboratory method used to detect, to diagnose hematologic malignancies and monitor response to therapy. Current manual analysis/interpretation of flow cytometry data is laborious and time consuming, and the results are dependent on the expertise of the user. This causes a backlog for data analysis and delays in reporting the flow cytometry results (current report roll-out time requires 7-14 days). Long waiting for the flow cytometry report causes delays in the implementation of therapy for life threatening disease, prolongs hospitalization and may impact disease outcomes.
We developed an AI-enabled flow cytometry interpretation algorithm (AHEAD-flow platform) that serves as a rapid triage tool to speed up initial data analysis 100 times faster than current approach (30 mins → 7 seconds). This permits a faster diagnosis for patients and reduces treatment delays.
We developed an AI-enabled flow cytometry interpretation algorithm (AHEAD-flow platform) that serves as a rapid triage tool to speed up initial data analysis 100 times faster than current approach (30 mins → 7 seconds). This permits a faster diagnosis for patients and reduces treatment delays.
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Jason Liang
Jeremy Lee
Shanglin Tsai
Kevin Ko
Andrea (Yu-Fen) Wang
We are a team focusing on using artificial intelligence to develop solutions to solve challenges in treating hematologic malignancies.
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