Verily Life Sciences LLC

Ilan Moshe Shimshoni

Yossi Matias

Ehud Benyamin Rivlin

Daniel Freedman

Amit Aides

Yacob Yochai Blau

Liran Katzir

The present disclosure is directed towards systems and methods that leverage machine-learned models to decrease the rate at which abnormal sites are missed during a gastroenterological procedure. In particular, the system and methods of the present disclosure can use machine-learning techniques to determine the coverage rate achieved during a gastroenterological procedure. Measuring the coverage rate of the gastroenterological procedure can allow medical professionals to be alerted when the coverage output is deficient and thus allow an additional coverage to be achieved and as a result increase in the detection rate for abnormal sites (e.g., adenoma, polyp, lesion, tumor, etc.) during the gastroenterological procedure.

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Haifa, Israel

Ilan Moshe Shimshoni