Noninvasive method for determining the presence of systemic hypertension in a subject
A non-invasive method for determining and characterizing the presence of damage or abnormalities resulting from or concomitant with systemic hypertension in subject. This method is comprised of the acquisition of ocular image(s) and subsequent evaluation, classification and/or interpretation of these image(s). The ocular image(s) may be acquired by photography. Evaluation, classification, and/or interpretation may be automated or involve the active participation of a human. The ocular images may be classified into either a normal or clinical group or compared to an algorithm of hypertensive symptoms. The ocular images may, additionally, be processed and have the pertinent characteristics extracted to make the classification more exact.
1. A non-invasive method for diagnosing hypertension in a subject, the method comprising:
a) acquiring one or more ocular images;
b) processing said ocular images;
c) extracting one or more ocular characteristics from said ocular images; and
d) classifying, by a processor, said ocular characteristics, wherein the classifying comprises:
measuring said ocular characteristics;
assigning a numerical character corresponding to markers of end organ damage from said measured ocular characteristics;
comparing the numerical character to a normal numerical character based on a normal eye under similar conditions;
converting the comparison of the numerical character corresponding to the markers of end organ damage from said measured ocular characteristics and the normal numerical character based on a normal eye under similar conditions to a classification of average diastolic and systolic blood pressure;
indicating a presence or absence of a hypertensive state; and
if a hypertensive state exists, indicating a severity of the hypertensive state by grouping based on average diastolic and systolic blood pressure.
2. The method of claim 1 , wherein the processing further comprises:
pre-processing of said ocular image;
identifying ocular characteristics;
tracking, identifying and analyzing blood vessels; and
identifying abnormalities and exudates.
3. The method of claim 1 , wherein the one or more ocular images are from one eye of the patient.
4. The method of claim 1 , wherein the ocular characteristics are selected from the group consisting of: blood vessel architecture color and reflectance, ischemic spots, nerve fiber layer loss, choroidal infarcts, Eischnig's spots exudates, hemorrhages, and combinations thereof.
5. The method of claim 1 , wherein the ocular characteristics are selected from the group consisting of: vessel crossovers, vessel tortuosity, evidence of exudates, and combinations thereof.
6. The method of claim 1 , wherein the markers of end organ damage from said ocular characteristics are selected from the group consisting of:
size, length, diameter, branching characteristics, color, reflectance, and combinations thereof.
7. The method of claim 1 , wherein the classifying further comprises employing a decision tree or algorithm.
8. A non-invasive method of determining the progression of hypertension in a subject comprising:
acquiring one or more first ocular images;
processing said first ocular images;
extracting a first ocular characteristic from said first ocular images;
acquiring one or more second ocular images;
processing said second ocular images;
extracting a second ocular characteristic from said second ocular images;
assigning a numerical character corresponding to markers of end organ damage from said first ocular characteristic;
assigning a numerical character corresponding to markers of end organ damage from said second ocular characteristic;
comparing the numerical character corresponding to the markers of end organ damage from said first ocular characteristic and the numerical character corresponding to the markers of end organ damage from said second ocular characteristic;
converting, by a processor, the comparison of the numerical character corresponding to the markers of end organ damage from said first ocular characteristic and the numerical character corresponding to the markers of end organ damage from said second ocular characteristic into a classification of average diastolic and systolic blood pressure,
wherein the first ocular characteristic and the second ocular characteristic are acquired, processed and extracted sequentially over time and wherein differences in the ocular characteristics indicate the relative change in hypertension status in said subject, and
if a hypertensive state exists, indicating a severity of the hypertensive state by grouping based on average diastolic and systolic blood pressure.
9. The method of claim 8 , further comprising re-classifying hypertension status in said subject based on differences from the first ocular characteristic to the second ocular characteristic.
10. The method of claim 8 , wherein the one or more ocular images are from one eye of the patient.
11. The method of claim 8 , wherein the ocular characteristics are selected from the group consisting of: blood vessel architecture color and reflectance, ischemic spots, nerve fiber layer loss, choroidal infarcts, Eischnig's spots exudates, hemorrhages, and combinations thereof.
12. The method of claim 8 , wherein the ocular characteristics are selected from the group consisting of: vessel crossovers, vessel tortuosity, evidence of exudates, and combinations thereof.
13. The method of claim 8 , wherein the markers of end organ damage from said ocular characteristics are selected from the group consisting of:
size, length, diameter, branching characteristics, color, reflectance, and combinations thereof.
14. The method of claim 8 , wherein the classifying further comprises employing a decision tree or algorithm.
15. A non-invasive method of determining the progression of hypertension in a subject comprising:
acquiring one or more first ocular images;
processing said first ocular images;
extracting a first ocular characteristic from said first ocular images;
assigning a numerical character corresponding to markers of end organ damage from said first ocular characteristic; and
comparing the numerical character corresponding to the markers of end organ damage from said first ocular characteristic to a database of numerical characters corresponding to standard ocular characteristics obtained from a non-hypertensive population,
converting, by a processor, the comparison of the numerical character corresponding to the markers of end organ damage from said first ocular characteristic and the database of numerical characters corresponding to standard ocular characteristics obtained from a non-hypertensive population into a classification of average diastolic and systolic blood pressure,
wherein differences between the numerical character corresponding to the markers of end organ damage from said first ocular characteristic to a database of numerical characters corresponding to standard ocular characteristics indicates a presence or absence of a hypertensive state, and
if a hypertensive state exists, indicating a severity of the hypertensive state by grouping based on average diastolic and systolic blood pressure.
16. The method of claim 15 , wherein the one or more ocular images are from one eye of the patient.
17. The method of claim 15 , wherein the ocular characteristics are selected from the group consisting of: blood vessel architecture color and reflectance, ischemic spots, nerve fiber layer loss, choroidal infarcts, Eischnig's spots exudates, hemorrhages, and combinations thereof.
18. The method of claim 15 , wherein the ocular characteristics are selected from the group consisting of: vessel crossovers, vessel tortuosity, evidence of exudates, and combinations thereof.
19. The method of claim 15 , wherein the markers of end organ damage from said ocular characteristics are selected from the group consisting of: size, length, diameter, branching characteristics, color, reflectance, and combinations thereof.
20. The method of claim 15 , wherein the classifying further comprises employing a decision tree or algorithm.