Systems and methods for machine learning control of a surgical device

A system for control of a surgical device, the system comprising:

• a surgical stapling device including:
• a sensor configured to sense a signal, the signal configured to control a motor of the surgical stapling device;
• an end effector comprising a knife configured to cut tissue;
• at least one processor;
• and
• at least one memory including instructions stored thereon which, when executed by the at least one processor, cause the system to:
• access raw data captured by the sensor of the surgical device during a procedure wherein the knife advances through the tissue;
• filter the raw data to define filtered data, the filter including a moving minimum filter;
• generate a difference data based on a difference between the raw data and the filtered data;
• generate zero-crossing data based on determining a point in time where an amplitude of the difference data last crossed from a first non-zero amplitude value, through a zero-amplitude value, to a second non-zero amplitude value with an opposite sign of the first non-zero amplitude value;
• input the zero-crossing data to a machine learning classifier;
• and
• predict a probability of an end stop point based on the machine learning classifier, wherein the end stop point includes a point in time where the knife of the surgical device ceases to cut tissue.