System for the noninvasive estimation of relative age

• US 6,501,982 B1
• Filed: 01/19/2000
• Issued: 12/31/2002
• Est. Priority Date: 01/22/1999
• Status: Expired due to Fees

• Current Assignee: GLT Acquisition Corporation
• Original Assignee: Sensys Medical, Inc. (Sensys Medical Limited)
• Inventors: Ruchti, Timothy L., Thennadil, Suresh, Malin, Stephen F., Rennert, Jessica
• Primary Examiner(s): Lateef, Marvin M.
• Assistant Examiner(s): Shah, Devaang
• Application Number:
  US09/487,236
• Time in Patent Office: 1,077 Days
• Field of Search: 600/310, 600/473, 600/476, 600/477, 250/338.1, 250/340, 250/341.5, 250/341.8
• US Class Current: 600/473
• CPC Class Codes: A61B 2560/0223 of calibration, e.g. protocols for calibrating sensors
• CPC Class Codes: A61B 5/0059 using light, e.g. diagnosis by transillumination, diascopy, fluorescence A61B5/0093, A61B5/1455, A61B5/02416 and A61B5/0261 take precedence
• CPC Class Codes: A61B 5/0075 by spectroscopy, i.e. measuring spectra, e.g. Raman spectroscopy, infrared absorption spectroscopy A61B5/0071 takes precedence
• CPC Class Codes: A61B 5/1075 for measuring dimensions by non-invasive methods, e.g. for determining thickness of tissue layer A61B8/0858 takes precedence
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/14546 for measuring analytes not otherwise provided for, e.g. ions, cytochromes
• CPC Class Codes: A61B 5/1455 using optical sensors, e.g. spectral photometrical oximeters
• CPC Class Codes: A61B 5/441 Skin evaluation, e.g. for skin disorder diagnosis
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: G01N 21/359 using near infrared light
• CPC Class Codes: G01N 21/4785 Standardising light scatter apparatus; Standards therefor
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

Noninvasive instrumentation and procedures have been developed for estimating the apparent age of human and animal subjects based on the irradiation of skin tissue with near-infrared light. The method of age estimation provides additional information about primary sources of systematic tissue variability due to chronological factors and environmental exposure. Therefore, categorization of subjects on the basis of the estimated apparent age is suitable for further spectral analysis and the measurement of biological and chemical compounds, such as blood analytes. Furthermore, age determination of subjects has particular benefit in assessment of therapies used to reduce the effects of ageing in tissue and measurement of tissue damage.

Claims

1. A method for noninvasively determining the apparent age of a tissue sample in vivo, comprising the steps of:

measuring the NIR absorption spectrum of said tissue sample;

detecting outliers, wherein said outliers are invalid measurements caused by spectral variation due to any of instrument malfunction, poor sampling, and subjects outside of a calibration set;

preprocessing, wherein said preprocessing step includes one or more transformations that attenuate noise, and instrumental variation without affecting the signal of interest, including wavelength selection, scaling, normalization smoothing derivatives, and filtering; and

estimating the apparent age of said tissue sample based on said NIR spectrum.

2. The method of claim 1, wherein said NIR region comprises a range of about 700 to about 2500 nm.

3. The method of claim 1, wherein said spectrum is denoted by a vector mε

^N of absorbance values pertaining to a set of N wavelengths λ

ε

^N.

4. The method of claim 1, wherein said outlier detection step employs principal components analysis and residual analysis to detect spectral outliers.

5. The method of claim 4, wherein said outlier detection step further comprises the steps of:

projecting a spectrum m onto a plurality of eigenvectors, contained in a matrix o, said matrix o being previously developed through principal components analysis of said calibration set, where ${\mathrm{xpc}}_o=\underset{k=1}{\overset{7}{\sum <br><br>}}<br><br>\text{\hspace{1em}<br><br>}<br><br>{\mathrm{mo}}_k,$

and where o_k is the k^th column of the matrix o;

determining the residual q, according to

6. The method of claim 1, wherein said preprocessed measurements are denoted by a vector Xε

^N, and wherein said vector is determined according to

7. The method of claim 1, wherein said age estimation step further comprises the step of:

providing a calibration model to map said preprocessed spectrum through a mapping to an estimate of age.

8. The method of claim 7, wherein said calibration model comprises NIR tissue measurements and chronological ages of an exemplary subject population.

9. The method of claim 7, wherein said mapping is linear.

10. The method of claim 7, wherein said age estimate is determined according to $\hat{y}=\sum _{}^{}$

k=1N

 



wc,k

xk;

given the preprocessed spectrum x, and the calibration model w_c, where W_c,k is the k^th element of w_c and is the age estimate.

11. The method of claim 10, wherein said calibration model employs factor analysis to decompose a high-dimensional (redundant) data set comprising absorbance, intensity or reflectance measurements at a plurality of wavelengths to significant factors representing the majority of variation within said data set;

and

wherein said calibration model includes factors that capture variation in said spectra correlated with variation in subject age.

12. The method of claim 10, further comprising the steps of;

projecting said samples into a resulting factor space to produce a set of scores for each sample; and

applying multiple linear regression to model the relationship between said scores and apparent age of said subject.

13. The method of claim 7, wherein said mapping is non-linear.

14. The method of claim 13, wherein said non-linear mapping is specified through any of artificial neural networks and non-linear partial least squares regression.

38. The method of claim 1, further comprising the step of performing a blood analyte prediction based on said age estimate.

15. A method for non-invasively determining the apparent age of a tissue sample in vivo comprising the steps of:

providing a calibration set of exemplary measurements;

measuring the NIR absorption spectrum of said tissue sample;

detecting outliers, wherein said outliers are invalid measurements caused by spectral variation due to any of instrument malfunction, poor sampling, and subjects outside of said calibration set; and

estimating the apparent age of said tissue sample based on said NIR absorption spectrum.

39. The method of claim 15, further comprising the step of performing a blood analyte prediction based on said age estimate.

16. A method of classifying a subject according to age based on noninvasive NIR measurements, comprising the steps of:

providing a calibration set of exemplary measurements;

measuring the NIR absorption spectrum of said tissue sample;

detecting outliers, wherein said outliers are invalid measurements caused by spectral variation due to any of instrument malfunction, poor sampling, and subjects outside of said calibration set;

preprocessing, wherein said preprocessing step includes at least one transformation that attenuates noise and instrumental variation without affecting the signal of interest, including wavelength selection, scaling, normalization, smoothing, derivatives, and filtering,

extracting features, whereby factors of measurements relevant to age classification are determined; and

classifying said sample according to predefined age groups.

17. The method of claim 16, wherein said feature extraction step comprises any mathematical transformation that enhances a quality or aspect of said sample measurement for interpretation to represent concisely the properties and characteristics of the tissue measurement site for age classification.

18. The method of claim 17, wherein said feature extraction step employs scores from factor analysis.

19. The method of claim 17, wherein said feature extraction step employs partial least squares regression.

20. The method of claim 17, wherein said features are represented in a vector, zε

^M that is determined from a preprocessed measurement through;

21. The method of claim 20, wherein individual features are divided into two categories comprising:

abstract features that do not necessarily have a specific interpretation related to a physical system; and

simple features that are derived from an a priori understanding of a sample and that can be related directly to a physical phenomenon.

22. The method of claim 21, wherein spectral variations due to changes in dermal thickness corresponding with age are extracted and enhanced, and wherein said variations serve as a feature for age classification and measurement of tissue properties.

23. The method of claim 18, further comprising the step of:

employing factor-based methods to build a model capable of representing variation in a measured absorbance spectrum related to subject age, wherein projection of a measured absorbance spectrum onto said model constitutes a feature that represents spectral variation related to subject age.

24. The method of claim 16, wherein said classification step further comprises the steps of:

measuring the similarity of at least one feature to predefined age categories; and

assigning membership to one of said predefined categories.

25. The method of claim 24, wherein said assigning step uses mutually exclusive classes and assigns each sample to one class.

26. The method of claim 24, wherein said assigning step uses a fuzzy classification system that allows class membership in more than one class simultaneously.

27. The method of claim 25, wherein said assigning step further comprises the steps of:

mapping said sample to one of said predefined classes; and

applying a decision rule to assign class membership.

28. The method of claim 27, wherein said mapping step is given by:

29. The method of claim 28, wherein said age categories are “

old” and

“

young” and

where L_old corresponds to a representative value for said “

old”

class and L_young corresponds to a representative value for said “

young class”

; and

wherein said class assignment is based on the closeness of L to L_old and L_young.

30. The method of claim 29, wherein a distance d_old of L to L_old is measured by

31. The method of claim 29, wherein said decision rule is:

if d_old<

d_young, then the apparent age of the sample is classified as “

old;

” and

if d_old≧

d_young, then the apparent age of the sample is classified as “

young”

.

32. The method of claim 27, wherein limits for said mapping and decision rule are determined from a calibration set of exemplary measurements and corresponding apparent age reference values through a classification calibration procedure.

33. The method of claim 32, wherein said classification calibration procedure comprises any of linear Discriminant analysis, SIMCA, k nearest neighbor, fuzzy classification and artificial neural networks.

34. The method of claim 26, wherein class membership is defined by a continuum of grades, and wherein a set of membership functions map a feature space into an interval [0,1] for each class and wherein an assigned grade represents a degree of class membership, and wherein a grade of “

1”

represents the highest degree of class membership.

35. The method of claim 34, wherein the mapping from the feature space to a vector of class memberships is given by:

36. The method of claim 35, wherein a membership function is represented by $y={}^{}$

-12

σ

2

(z-z_)2,

where y is the degree of membership in a fuzzy sub-set, z is the feature used to determine membership is the center of a fuzzy subset, and σ

is the standard deviation.

37. The method of claim 35, wherein said membership vector provides the degree of class membership in each of said predefined classes.

40. The method of claim 16, further comprising the step of performing a blood analyte prediction based on said age classification.

41. An apparatus for non-invasively estimating the relative age of a subject comprising:

means for generating near infrared (NIR) energy;

means for separating said generated NIR energy into a plurality of wavelength regions;

an optical interface comprising;

means for transmitting said NIR energy from said wavelength separating means towards a target measurement site on a subject; and

means for collecting NIR energy emanating from said measurement site;

means for detecting said collected energy and converting said collected energy to a voltage;

means for converting said voltage to a digital value; and

means for analyzing said digital value whereby said analysis results in an estimate of said subject'"'"'s relative age.

42. The apparatus of claim 41, wherein said energy source transmits light in the wavelength range of about 700 nm to 2500 nm.

43. The apparatus of claim 42, wherein said energy source is an LED array or a quartz halogen lamp.

44. The apparatus of claim 41, wherein said wavelength separating means is a monochromator or an interferometer.

45. The apparatus of claim 41, wherein said wavelength separating means comprises successive illumination through an LED array.

46. The apparatus of claim 41 wherein said transmission means is a light pipe, a fiber-optic probe, a lens system, or a light-directing mirror system.

47. The apparatus of claim 41, wherein said energy collecting means comprises at least one starring optical detector.

48. The apparatus of claim 41, wherein said energy collecting means comprises at least one fiber-optic probe.

49. The apparatus of claim 41, wherein said energy detecting means comprises InGaAs detectors.

50. The apparatus of claim 41, wherein said digitizing means is a 16-bit A/D converter.

51. The apparatus of claim 41, wherein said optical interface is positioned at optimally determined distances from said target measurement site.

52. The apparatus of claim 51, wherein said distances are specified according to layer of the skin tissue to be sampled.

53. The apparatus of claim 52, wherein said optimal distance is approximately 0-300 μ

m for the upper dermis.

54. The apparatus of claim 52, wherein said optimal distance is approximately 0.3-3 mm for the lower dermis.

55. The apparatus of claim 51, wherein a point of illumination is set through any of a focusing lens and a fiber-optic probe.

56. The apparatus of claim 51, wherein a point of detection is set through any of a starring optical detector or a fiber-optic probe.

57. The apparatus of claim 41 wherein said means for analysis comprises a digital processor programmed to perform an age estimation procedure;

wherein said digital value is passed to said relative age estimation procedure and whereby a relative age estimation is performed.

58. In an apparatus for non-invasively estimating the relative age of a subject, an optical interface comprising:

means for transmitting NIR energy towards a target measurement site on a subject; and

means for collecting NIR energy reflected from said measurement site;

wherein said optical interface is adapted to be positioned at optimally determined distances from said site.

59. The optical interface of claim 58, wherein said transmission means is a light pipe, a fiber-optic probe, a lens system, or a light-detecting mirror.

60. The optical interface of claim 58, wherein said energy collecting means comprises at least one starring optical detector.

61. The optical interface of claim 58, wherein said energy collecting means comprises at least one fiber-optic probe.

62. The optical interface of claim 58, wherein said optimal distances are specified according to layer of the skin tissue to be sampled.

63. The optical interface of claim 62, wherein said optimal distance is approximately 0-300 μ

m fore the upper dermis.

64. The optical interface of claim 62, wherein said optimal distance is approximately 0.3 mm-3 mm for the lower dermis.

65. The optical interface of claim 58, wherein a point of Illumination is set through any of a focusing lens and a fiber-optic probe.

66. The optical interface of claim 58, wherein a point of detection is set