Estimating the adequacy of a procedure

A system for estimating adequacy of a capsule endoscopy (CE) procedure, the system comprising:

• a display device;
• 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 a plurality of images of at least a segment of a gastrointestinal tract (GIT) captured by a CE imaging device during a CE procedure;
• access a plurality of characteristic measures associated with the plurality of images;
• determine an adequacy measure for the CE procedure based on the plurality of characteristic measures, the adequacy measure indicating a measure of whether an imaging coverage captured by the plurality of images was adequate to capture an event of interest in the at least the segment of the GIT, whether or not such an event of interest actually exists in the at least the segment of the GIT;
• and
• display, on the display device, an adequacy indication for the CE procedure based on the adequacy measure,
• wherein determining the adequacy measure for the CE procedure is based on at least one of a classical machine learning technique, a deep learning technique, or a heuristic,
• wherein the at least the segment of the GIT includes a plurality of segments,
• wherein determining the adequacy measure for the CE procedure includes:
• determining an adequacy measure for each segment of the plurality of segments, and
• determining the adequacy measure for the CE procedure based on the adequacy measure for each segment of the plurality of segments,
• wherein in determining the adequacy measure for the CE procedure based on the adequacy measure for each segment of the plurality of segments, the instructions, when executed by the at least one processor, cause the system to:
• access a priori probabilities of occurrences of the event of interest in each segment of the plurality of segments, the a priori probabilities being empirically determined based on a patient population, and
• determine the adequacy measure for the CE procedure based on the a priori probabilities and based on the adequacy measure for each segment of the plurality of segments.

A non-transitory computer-readable medium storing instructions which, when executed by a processor, cause performance of a method comprising:

• accessing a plurality of images of at least a segment of a gastrointestinal tract (GIT) captured by a CE imaging device during a CE procedure;
• accessing a plurality of characteristic measures associated with the plurality of images;
• determining an adequacy measure for the CE procedure based on the plurality of characteristic measures, the adequacy measure indicating a measure of whether an imaging coverage captured by the plurality of images was adequate to capture an event of interest in the at least the segment of the GIT, whether or not such an event of interest actually exists in the at least the segment of the GIT;
• and
• displaying an adequacy indication for the CE procedure based on the adequacy measure,
• wherein determining the adequacy measure for the CE procedure is based on at least one of a classical machine learning technique, a deep learning technique, or a heuristic,
• wherein the at least the segment of the GIT includes a plurality of segments,
• wherein determining the adequacy measure for the CE procedure includes:
• determining an adequacy measure for each segment of the plurality of segments, and
• determining the adequacy measure for the CE procedure based on the adequacy measure for each segment of the plurality of segments,
• wherein determining the adequacy measure for the CE procedure based on the adequacy measure for each segment of the plurality of segments includes:
• accessing a priori probabilities of occurrences of the event of interest in each segment of the plurality of segments, the a priori probabilities being empirically determined based on a patient population, and
• determining the adequacy measure for the CE procedure based on the a priori probabilities and based on the adequacy measure for each segment of the plurality of segments.