Method of early detection of multiple sclerosis

A method of early detection of multiple sclerosis (MS) in an animal in a vivarium comprising the steps of:

• (a) placing one or more study animals in one cage in a vivarium;
• (b) electronically observing one or animal activities in real-time of the one or more study animals using a combination of electronic cameras, hardware, infrared (IR) lighting of the study animals, and electronic hardware including computation and communication hardware;
• (c) selecting a single “activity-drop metric” with an associated scalar “activity-drop value,” responsive to the electronically observed one or more animal activities in step (b);
• (d) selecting an “MS health detection function” whose input comprises an “animal health dataset” and whose output comprises a likelihood scalar representing the likelihood that at least one of the study animals has MS;
• (e) collecting a nightly activity scalar of the at least one of the study animals repeatedly and continually for a night and placing the nightly activity scalar into a set of “nightly activity data”;
• (f) identifying automatically three consecutive time regions in the nightly activity data:
• a “high-activity region,” an “activity-drop region,” and a “low-activity region”;
• (g) applying the activity-drop metric to the three consecutive regions, generating a nightly activity-drop value for the at least one of the study animals;
• (h) adding the nightly activity-drop value into the animal health dataset, wherein the animal health dataset comprises the resulting nightly activity-drop values;
• (i) applying the MS health detection function to the animal health dataset, generating a likelihood scalar for each iteration;
• (j) iterating steps (e) through (i) for sequential nights until a terminating condition is reached;
• wherein the early detection of MS comprises the likelihood scalars from step (i), of the at least one of the study animals;
• (k) terminating the study when the terminating condition is reached.