Keisuke Goda: Decoding Platelet Aggregates with AI for Disease Identification
Keisuke Goda, Professor at The University of Tokyo, shared on LinkedIn about a recent article he and his colleagues co-authored, published in Thrombosis Research, adding:
”This is the 9th paper from the Goda Lab this year!
Diseases such as cancer, infectious diseases, and thrombosis can induce platelet aggregation, which may lead to complications such as stroke and heart attack.
However, these platelet aggregates look very similar, making it difficult to distinguish their underlying causes by appearance alone.
Using our high-speed optofluidic imaging technology, we imaged platelet aggregates in patient blood samples one by one and analyzed them with AI.
As a result, we were able to identify the associated diseases with more than 80% accuracy!
In the future, this approach may be developed for preventive medicine, including the prevention of stroke and heart attack.”
Title: Associating the phenotypic expression of platelets with disease type through image-based single-cell profiling
Authors: Huidong Wang, Masako Nishikawa, Yuqi Zhou, Ruoxi Yang, Junyu Chen, Hongqian Zhang, Yuma Ibayashi, Hiroshi Kanno, Makoto Kurano, Eriko Morishita, Yutaka Yatomi, Keisuke Goda
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