# US 2019/0167178 A1 · Method of early detection of multiple sclerosis

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- Number: US 2019/0167178 A1
- Document ID: US20190167178A1
- Status: Published application
- Kind: A1
- Application number: 16/269,924
- Filed: 2019-02-07
- Published: 2019-06-06
- Earliest claimed: 2016-09-30
- Assignees: Vium, Inc.
- Applicants: Vium, Inc.
- Inventors: Daniel J. Ford; Laura Schaevitz
- Web page: https://patentgrep.com/patent/US20190167178A1

## CPC

- A61B 5/4076: {Diagnosing or monitoring particular conditions of the nervous system}
- A61B 5/7275: {Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor}
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G16H 50/30: for calculating health indices; for individual health risk assessment
- A61B 5/1118: {Determining activity level}
- A61B 5/7257: {using Fourier transforms}
- A61B 2503/40: Animals
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

A method of early detection of multiple sclerosis (MS) in an animal in a vivarium is described. Animal activity data is collected at multiple times during the night. Sequential time regions of the night are identified as high-activity, activity-drop, or low-activity regions. Embodiments are described to quantify a drop, during the night, of an animal's activity level. These quantified activity-drop scalars for consecutive nights are accumulated in an animal health dataset. Then, a health detection function is applied to this dataset that, in response to the level of activity change and the speed of activity change, predicts or detects MS in the animal. One embodiment quantifies an activity-drop by fitting straight-line curves through the data in the three nightly regions. Another embodiment uses a Fourier transform on a circle and a linear combination. Another embodiment compares areas under data curves in the regions. Animals may be housed in cages with other animals.

## Claims

### Claim 1

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 animals 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; and
(k) terminating the study when the termination condition is reached;

### Claim 2 (depends on 1)

The method claim 1 wherein:
the MS health detection function is responsive to the activity-drop value for at least one night.

### Claim 3 (depends on 2)

The method claim 2 wherein:
the MS health detection function is computed, at least in part, using the additional method steps:
(l) executing at least a subset of method steps (a) through (j) repeatedly using different animals and observing the actual MS onset of the each different animal; and
(m) modifying the MS health detection function using a best-fit method such that the MS health detection function best determines the observed actual MS onset of the each different animal.

### Claim 4 (depends on 1)

The method claim 1 wherein:
the MS health detection function is binary valued.

### Claim 5

A method of early detection of multiple sclerosis (MS) in an animal in a vivarium comprising the steps of:
(n) placing one or more study animals in one cage in a vivarium;
(o) 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;
(p) selecting a single “linear combination coefficient set” that generates a scalar linear combination value when applied responsively to a set of discreet transform values;
(q) selecting a “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 animals has MS;
(r) collecting an activity scalar of the animal repeatedly and continually for a night: a set of “nightly activity data”;
(s) computing a Fourier transform on a circle responsive to the nightly activity data, generating a set of discreet transform values;
(t) applying the linear combination coefficient set responsively to the set of discreet transform values; wherein the resulting scalar linear combination value is a nightly activity value;
(u) adding the nightly activity value into the animal health dataset; wherein the animal health dataset comprises the nightly activity values;
(v) applying the MS health detection function to the animal health dataset, generating an animal likelihood scalar for the animal;
(w) iterating steps (r) through (v) for sequential nights until a terminating condition is reached;
wherein the early detection of MS in the at least one animal comprises the animal likelihood scalars from steps (v).

### Claim 6 (depends on 5)

The method claim 5 wherein:
the MS health detection function further comprises an output that is a confidence value;
wherein in step (v) the MS health detection function further generates a confidence value for the at least one animal; and
wherein the early detection of MS in the at least one animal further comprises the confidence values from steps (v).

### Claim 7

A method of early detection of multiple sclerosis in an animal in a vivarium comprising the steps of:
(x) placing one or more study animals in one cage in a vivarium;
(y) 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;
(z) selecting a single “LASSO metric” with an associated scalar “activity-drop value”;
(aa) selecting an “MS health detection function” whose input comprises an animal health dataset and whose output comprises a likelihood scalar representing the likelihood that the one or more animals has MS;
(bb) collecting an activity scalar of the one or more animals repeatedly and continually for a night: a set of “nightly activity data”;
(cc) computing a LASSO best-fit of a first piece-wise linear function to the nightly activity data, generating a LASSO L0, L1 and L2;
(dd) applying the LASSO metric to the generated LASSO L0, L1 and L2, generating a nightly activity-drop value;
(ee) adding the nightly activity-drop value into the “animal health dataset,” wherein the animal health dataset comprises the resulting nightly activity-drop values;
(ff) applying the MS health detection function to the animal health dataset;
(gg) iterating steps (hh) through (ff) for sequential nights until a terminating condition is reached;
wherein the early detection of MS comprises the likelihood scalars from step (ff).

### Claim 8 (depends on 7)

The method of claim 7 wherein:
the first step-wise linear function comprises two linear pieces; and
wherein the nightly activity-drop value comprises a linear combination of LASSO L0, L1 and L2.

### Claim 9 (depends on 7)

The method of claim 7 wherein:
the first step-wise linear function comprises of three linear pieces; and
wherein the nightly activity-drop value comprises a linear combination of LASSO L0, L1 and L2.

### Claim 10

A method of early detection of multiple sclerosis in an animal in a vivarium comprising the steps of:
(hh) placing one or more study animals in one cage in a vivarium;
(ii) 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;
(jj) selecting a “MS health detection function” whose input comprises an animal health dataset and whose output comprises a likelihood scalar representing the likelihood that the animal has MS;
(kk) collecting an activity scalar of the at least one animal repeatedly and continually for a night: generating a set of “nightly activity data”;
(ll) computing a baseline low-activity level responsive to the nightly activity data;
(mm) modifying the set of nightly activity data by subtracting the baseline low-activity level from the elements of the set;
(nn) identifying automatically two sequential regions for each night in the nightly activity data: a “high-activity region,” and a “low-activity region”;
(oo) computing a high-activity value for each night equal to the integration of values in the set of nightly activity data taken within the time interval of the high-activity region;
(pp) adding the high-activity value into the “animal health dataset,” wherein the animal health dataset comprises the resulting nightly high-activity values;
(qq) applying the MS health detection function to the animal health dataset;
(rr) iterating steps (kk) through (qq) for sequential nights until a terminating condition is reached;
wherein the early detection of MS comprises the likelihood scalars from step (qq).

### Claim 11 (depends on 10)

The method of claim 10 wherein:
(ss) computing a low-activity value for each night equal to the integration of values in the set of nightly activity data within the time interval of the low-activity region;
(tt) adding the low-activity value into the “animal health dataset,” wherein the animal health dataset comprises the resulting nightly activity-drop values;
wherein step (ss) is performed before or after step (oo);
wherein step (tt) is performed before or after step (pp); and
wherein early MS health detection function is responsive to both the high-activity values and the low-activity values in the animal health dataset.

### Claim 12 (depends on 10)

The method of claim 10 wherein:
the MS health detection function is further responsive to an activity level of the at least one animal prior to an induction date for the at least one animal into the study.

### Claim 13 (depends on 12)

The method of claim 12 wherein:
the MS health detection function is normalized responsive to the activity level of the at least one animal prior to an induction date for the at least one animal into the study.