IP Library Granted Patent US 12,640,266
Granted Patent B2
US 12,640,266 · App. 17/530,805 · Granted May 26, 2026

Diagnostic tool for analyzing results of flow mediated dilation

Inventors: Qianhong Wu (Malvern, PA); Sridhar Santhanam (Collegeville, PA); Bchara Sidnawi (Villanova, PA); Chandra M. Sehgal (Wayne, PA)
Assignee: Villanova University
G16H50/20A61B5/0285G06T7/0012G16H30/20G06T2207/10132G06T2207/20084G06T2207/30104
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,640,266
App. No.
17/530,805
Granted
May 26, 2026
Kind
B2
Abstract

A diagnostic tool includes a communications interface, a memory storing instructions, and at least one processor coupled to the communications interface and to the memory. The at least one processor is configured to execute the instructions to perform operations including: receiving measurement data of a response to a flow-mediated dilation (FMD) test; determining a value for an FMD parameter based on the received measurement data; establishing a diagnostic threshold based on patient medical information; determining a diagnostic result of the FMD test by comparing the FMD parameter value to the diagnostic threshold; and providing the diagnostic result of the FMD test through a digital interface of the diagnostic apparatus.

Claims (94)

1 . A diagnostic system comprising:

a measurement tool configured to collect image data of an artery subject to a flow-mediated dilation (FMD) test; and

a diagnostic tool comprising:

a communications interface;

a memory storing instructions; and

at least one processor coupled to the communications interface and to the memory, the at least one processor being configured to execute the instructions to perform operations comprising:

receiving measurement data comprising a change in diameter of the artery subject to the FMD test over time from an initiation of the FMD test through the artery's substantial recovery to baseline, the measurement data derived from the image data;

determining an FMD parameter value by applying a finite difference model to the received measurement data, wherein the FMD parameter value comprises one or more of a sensitivity of the artery to softening in response to a changing wall shear stress (WSS), resistance of the artery to softening in response to a changing WSS, and integrity of mechanotransduction of a tissue-softening signal throughout the artery, each parameter value being associated with an arterial wall subject to the FMD test;

establishing a diagnostic threshold based on patient medical information;

determining a diagnostic result of the FMD test by comparing the FMD parameter value to the diagnostic threshold; and

providing the diagnostic result of the FMD test through an output module of the diagnostic apparatus.

2 . The diagnostic system of claim 1 , wherein the FMD parameter value comprises a patient-specific measurement resulting from the FMD test.

3 . The diagnostic system of claim 1 , further comprising a data analysis tool configured to receive the image data and derive the measurement data from the received image data.

4 . The diagnostic system of claim 1 , wherein the measurement tool comprises an ultrasound imaging device.

5 . The diagnostic system of claim 1 , wherein the diagnostic threshold comprises a static threshold or a dynamic threshold.

6 . The diagnostic system of claim 1 , wherein the patient information corresponds to at least one of age, family medical history, smoking history, and sex.

7 . The diagnostic system of claim 1 , wherein the at least one processor is further configured to execute the instructions to perform operations comprising:

retrieving medical history of a patient of the FMD test; and

determining the FMD parameter value based on the medical history and measurement data.

8 . The diagnostic system of claim 1 , wherein the FMD test is a brachial artery flow-mediated dilation test.

9 . The diagnostic system of claim 1 , wherein the diagnostic threshold comprises a range of thresholds corresponding to the patient medical information, and wherein providing the diagnostic result through the output module further comprises presenting a graphical representation of the range of thresholds and a marker denoting the diagnostic result, the marker being presented within the graphical representation in relation to the range of thresholds.

10 . The diagnostic system of claim 1 , wherein the diagnostic result comprises a prediction in a physical status of a patient of the respective FMD test.

11 . The diagnostic system of claim 1 , wherein the at least one processor is further configured to execute the instructions to perform operations comprising:

applying at least one of a machine learning processing or neural network processing to adjust one or more algorithms used to determine the FMD parameter value and the diagnostic threshold from the measurement data.

12 . The diagnostic system of claim 1 , wherein the sensitivity of the artery to softening in response to a changing wall shear stress is

E

min

*

=

E

E

0

,

the resistance of the artery to softening in response to a changing WSS is B=βs 0 , and the integrity of mechanotransduction of a tissue-softening signal throughout the artery is

γ

=

a

s

r

in

2

ξ

where E ∞ is the modulus of elasticity of an artery wall for a shear stress approaching infinity, E 0 is the modulus of elasticity of the artery wall for a shear stress of zero, β is a property indicative of the artery wall's resistance to a changing shear stress,

s

0

=

4

μ

q

π

r

in

3

,

where μ is the dynamic viscosity of the blood within the artery, q is the blood flow rate, a s is diffusivity, r in is the inner radius of the artery, and ξ is a property indicative of the artery's responsiveness to a changing sheer stress.

13 . The diagnostic system of claim 12 , wherein the at least one processor is further configured to execute the instructions to perform operations comprising:

determining a plurality of FMD parameter values based on the received measurement data, wherein the FMD parameter values comprise each of

E

min

*

,

B

,

and

γ

,

14 . A method comprising:

collecting image data of an artery subject to a flow-mediated dilation (FMD) test;

deriving measurement data from the image data, wherein the measurement data comprises a change in diameter of the artery subject to the FMD test over time from an initiation of the FMD test through the artery's substantial recovery to baseline;

determining an FMD parameter value by applying a finite difference model to the measurement data, wherein the FMD parameter value comprises one or more of a sensitivity of the artery to softening in response to a changing wall shear stress (WSS), resistance of the artery to softening in response to a changing WSS, and integrity of mechanotransduction of a tissue-softening signal throughout the artery, each parameter value being associated with an arterial wall subject to the FMD test;

establishing a diagnostic threshold based on patient medical information;

determining a diagnostic result of the FMD test by comparing the FMD parameter value to the diagnostic threshold; and

providing the diagnostic result of the FMD test through an output module of a diagnostic apparatus.

15 . The computer-implemented method of claim 14 , wherein the FMD parameter value comprises a patient-specific measurement resulting from the FMD test.

16 . The computer-implemented method of claim 14 , wherein the diagnostic threshold comprises a static threshold or a dynamic threshold.

17 . The computer-implemented method of claim 14 , further comprising:

retrieving medical history of a patient of the FMD test; and

determining the FMD parameter value based on the medical history and measurement data.

18 . The computer-implemented method of claim 14 , further comprising applying at least one of a machine learning process or neural network processing to adjust one or more algorithms used to determine the FMD parameter value and the diagnostic threshold from the measurement data.

19 . A tangible, non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform a method, comprising:

collecting image data of an artery subject to a flow-mediated dilation (FMD) test;

deriving measurement data from the image data, wherein the measurement data comprises a change in diameter of the artery subject to the FMD test over time from an initiation of the FMD test through the artery's substantial recovery to baseline;

determining an FMD parameter value by applying a finite difference model to the measurement data wherein, the FMD parameter value comprises one or more of a sensitivity of the artery to softening in response to a changing wall shear stress (WSS), resistance of the artery to softening in response to a changing WSS, and integrity of mechanotransduction of a tissue-softening signal throughout the artery, each parameter value being associated with an arterial wall subject to the FMD test;

establishing a diagnostic threshold based on patient medical information;

determining a diagnostic result of the FMD test by comparing the FMD parameter value to the diagnostic threshold; and

providing the diagnostic result of the FMD test through an output module of a diagnostic apparatus.

20 . The tangible, non-transitory computer-readable medium of claim 19 , containing further stored instructions that, when executed by at least one processor, cause the at least one processor to further perform:

applying at least one of a machine learning process or neural network processing to adjust one or more algorithms used to determine the FMD parameter value and the diagnostic threshold from the measurement data.

Assignments (3)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 23, 2021
From: SEHGAL, CHANDRA SANDY
To: THE TRUSTEES OF THE UNIVERSITY OF PENNSYLVANIA
Reel/Frame 058468/0908 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 7, 2021
From: WU, QIANHONG; SANTHANAM, SRIDHAR; SIDNAWI, BCHARA
To: VILLANOVA UNIVERSITY
Reel/Frame 058322/0686 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 7, 2021
From: SEHGAL, CHANDRA SANDY
To: THE TRUSTEES OF THE UNIVERSITY OF PENNSYLVANIA
Reel/Frame 058323/0128 →
Continuity (2)
Provisional Application 63116420 · Nov 20, 2020
Related Publication 20220165420A1 · May 26, 2022
References Cited (65)
US 10292597B2 · Maltz · 2019 [cited by examiner]
US 10485429B2 · Lenehan · 2019 [cited by examiner]
US 10568582B2 · Lenehan · 2020 [cited by examiner]
US 10758130B2 · Mullin · 2020 [cited by examiner]
US 20100081941A1 · Naghavi · 2010 [cited by examiner]
US 20140066738A1 · Kassab · 2014 [cited by examiner]
US 20160029972A1 · Lenehan · 2016 [cited by examiner]
US 20190159728A1 · Pritchard · 2019 [cited by examiner]
Zieliński, B., Dróżdż, A., Frołow, M. Fully-Automatic Method for Assessment of Flow-Mediated Dilation. Sep. 10, 2016. Springer, Cham. Computer Vision and Graphics. ICCVG 2016. Lecture Notes in Computer Science( ), vol. … [cited by examiner]
Wang H, et al. “Effects of size and elasticity on the relation between flow velocity and wall shear stress . . . A lattice Boltzmann-based computer simulation study.” PLoS One 15(1): e0227770. https://doi.org/10.1371/jo… [cited by examiner]
Alaraj, A., et al., 2015. Changes in wall shear stress of cerebral arteriovenous malformation feeder arteries after embolization and surgery. Stroke 46 (5), 1216-1220. https://doi.org/10.1161/strokeaha.115.008836. [cited by applicant]
Birk, Gurpreet K., et al., 2012. Brachial artery adaptation to lower limb exercise training: role of shear stress. J. Appl. Physiol. 112 (10), 1653-1658. https://doi.org/10.1152/ japplphysiol.01489.2011. American Physio… [cited by applicant]
Capell, Brian C., et al., 2007. Mechanisms of cardiovascular disease in accelerated aging syndromes. Circ. Res. 101 (1), 13-26. https://doi.org/10.1161/ circresaha.107.153692. Ovid Technologies (Wolters Kluwer Health). [cited by applicant]
Carter, Howard H., et al., 2016. Evidence for shear stress-mediated dilation of the internal carotid artery in humans. Hypertension 68 (5), 1217-1224. https://doi.org/ 10.1161/hypertensionaha.116.07698. Ovid Technologie… [cited by applicant]
Carter, Howard H., et al., 2017. Differential impact of water immersion on arterial blood flow and shear stress in the carotid and brachial arteries of humans. Physiol. Rep. 5, 10. https://doi.org/10.14814/phy2.13285. [cited by applicant]
Celermajer, D.S., et al., 1993. Cigarette smoking is associated with dose-related and potentially reversible impairment of endothelium-dependent dilation in healthy young adults. Circulation 88 (5), 2149-2155. https://d… [cited by applicant]
Chang, Audrey N., Potter, James D., 2005. Sarcomeric protein mutations in dilated cardiomyopathy. Heart Fail. Rev. 10 (3), 225-235. https://doi.org/10.1007/s10741- 005-5252-6. [cited by applicant]
Chen, Zhen, et al., 2019. Brachial flow-mediated dilation by continuous monitoring of arterial cross-section with ultrasound imaging. Ultrasound 27 (4), 241-251. https:// doi.org/10.1177/1742271x19857770. SAGE Publicati… [cited by applicant]
Cheng, Caroline, et al., 2006. Atherosclerotic lesion size and vulnerability are determined by patterns of fluid shear stress. Circulation 113 (23), 2744-2753. https://doi.org/10.1161/circulationaha.105.590018. [cited by applicant]
Chien, Shu, 2007. Mechanotransduction and endothelial cell homeostasis: the wisdom of the cell. Am. J. Physiol. Heart Circ. Physiol. 292 (3) https://doi.org/10.1152/ ajpheart.01047.2006. [cited by applicant]
Cibis, Merih, et al., 2016. Relation between wall shear stress and carotid artery wall thickening MRI versus CFD. J. Biomech. 49 (5), 735-741. https://doi.org/10.1016/j. jbiomech.2016.02.004. Elsevier BV. [cited by applicant]
Delmas, Patrick, 2004. Polycystins. Cell 118 (2), 145-148. https://doi.org/10.1016/j.cell.2004.07.007. [cited by applicant]
Dong, Cheng, et al., 2005. Melanoma cell extravasation under flow conditions is modulated by leukocytes and endogenously produced interleukin 8. Mol. Cell. BioMech. 2 (3), 145-159. [cited by applicant]
Farag, Emile S., et al., 2018. Aortic valve stenosis and aortic diameters determine the extent of increased wall shear stress in bicuspid aortic valve disease. J. Magn. Reson. Imag. 48 (2), 522-530. https://doi.org/10.1… [cited by applicant]
Garcia-Cardena, G., et al., 2001. Biomechanical activation of vascular endothelium as A determinant of its functional phenotype. Proc. Natl. Acad. Sci. Unit. States Am. 98 (8), 4478-4485. https://doi.org/10.1073/pnas.07… [cited by applicant]
Giantsos-Adams, Kristina M., et al., 2013. Heparan sulfate regrowth profiles under laminar shear flow following enzymatic degradation. Cell. Mol. Bioeng. 6 (2), 160-174. https://doi.org/10.1007/s12195-013-0273-z. [cited by applicant]
Gouverneur, M., et al., 2006. Vasculoprotective properties of the endothelial glycocalyx: effects of fluid shear stress. J. Intern. Med. 259 (4), 393-400. https://doi.org/ 10.1111/j.1365-2796.2006.01625.x. Wiley. [cited by applicant]
Hashimoto, M., et al., 1998. The impairment of flow-mediated vasodilatation in obese men with visceral fat accumulation. Int. J. Obes. 22 (5), 477-484. https://doi.org/10.1038/sj.ijo.0800620. [cited by applicant]
Huang, Sui, Ingber, Donald E., 1999. The structural and mechanical complexity of cell-growth control. Nat. Cell Biol. 1 (5) https://doi.org/10.1038/13043. [cited by applicant]
Huang, Sui, Ingber, Donald E., 2005. Cell tension, matrix mechanics, and cancer development. Canc. Cell 8 (3), 175-176. https://doi.org/10.1016/j. ccr.2005.08.009. Elsevier BV. [cited by applicant]
Johnstone, Murray A., 2004. The aqueous outflow system as a mechanical pump. J. Glaucoma 13 (5), 421-438. https://doi.org/10.1097/01. ijg.0000131757.63542.24. [cited by applicant]
Kazmierski, M., et al., 2010. Diagnostic value of flow mediated dilatation measurement for coronary artery lesions in men under 45 years of age. J. Cardiol. 17 (3), 288-292. [cited by applicant]
Burger, et al., 2003. Microgravity and bone cell mechanosensitivity. Adv. Space Res. 32 (8), 1551-1559. https://doi.org/10.1016/s0273-1177(03)90395-4. Elsevier BV. [cited by applicant]
Knobelsdorff-Brenkenhoff, Von, Florian, et al., 2016. Aortic flow and wall shear stress in aortic stenosis is associated with left ventricular remodeling. J. Cardiovasc. Magn. Reson. 18 (S1) https://doi.org/10.1186/1532… [cited by applicant]
Koo, Andrew, et al., 2013. Hemodynamic shear stress characteristic of atherosclerosis-resistant regions promotes glycocalyx formation in cultured endothelial cells. Am. J. Physiol. Cell Physiol. 304 (2) https://doi.org/… [cited by applicant]
Lammerding, Jan, et al., 2004. Lamin A/C deficiency causes defective nuclear mechanics and mechanotransduction. J. Clin. Invest. 113 (3), 370-378. https://doi.org/ 10.1172/jci19670. [cited by applicant]
Liang, Shile, Cheng, Dong, 2008. Integrin VLA-4 enhances sialyl-lewisx/A-negative melanoma adhesion to and extravasation through the endothelium under low flow conditions. Am. J. Physiol. Cell Physiol. 295 (3), C701-C70… [cited by applicant]
Liang, Shile, et al., 2008. Hydrodynamic shear rate regulates melanoma-leukocyte aggregation, melanoma adhesion to the endothelium, and subsequent extravasation. Ann. Biomed. Eng. 36 (4), 661-671. https://doi.org/10.100… [cited by applicant]
Loth, Francis, et al., 2003. Transitional flow at the venous anastomosis of an arteriovenous graft: potential activation of the ERK1/2 mechanotransduction pathway. J. Biomech. Eng. 125 (1), 49-61. https://doi.org/10.111… [cited by applicant]
McCully, Kevin K., 2012. Flow-mediated dilation and cardiovascular disease. J. Appl. Physiol. 112 (12), 1957-1958. https://doi.org/10.1152/japplphysiol.00506.2012. American Physiological Society. [cited by applicant]
Nauli, Surya M., et al., 2003. Polycystins 1 and 2 mediate mechanosensation in the primary cilium of kidney cells. Nat. Genet. 33 (2), 129-137. https://doi.org/ 10.1038/ng1076. [cited by applicant]
Ooij, Pim Van, et al., 2014. A methodology to detect abnormal relative wall shear stress on the full surface of the thoracic aorta using four-dimensional flow MRI. Magn. Reson. Med. 73 (3), 1216-1227. https://doi.org/10… [cited by applicant]
Paszek, Matthew J., et al., 2005. Tensional homeostasis and the malignant phenotype. Canc. Cell 8 (3), 241-254. https://doi.org/10.1016/j.ccr.2005.08.010. [cited by applicant]
Pyke, Kyra E., Tschakovsky, Michael E., 2005. The relationship between shear stress and flow-mediated dilatation: Implications for the assessment of endothelial function. J. Physiol. 568 (2), 357-369. https://doi.org/10… [cited by applicant]
Restaino, Robert M., et al., 2016. Endothelial dysfunction following prolonged sitting is mediated by A reduction in shear stress. Am. J. Physiol. Heart Circ. Physiol. 310 (5), H648-H653. https://doi.org/10.1152/ajphear… [cited by applicant]
Abad, et al., AIP Advances 10, 025033 (2020); Simulation strategies for the food and drug administration nozzle using Nek5000. AIP Adv. 10 (2). https://doi. org/10.1063/1.5142703. Accessed Mar. 5, 2020. [cited by applicant]
Shi, Zhong-Dong, et al., 2011. Heparan sulfate proteoglycans mediate interstitial flow mechanotransduction regulating MMP-13 expression and cell motility via FAK-ERK in 3D collagen. PloS One 6 (1), e15956. https://doi.o… [cited by applicant]
Stoner, Lee, et al., 2004. Relationship between blood velocity and conduit artery diameter and the effects of smoking on vascular responsiveness. J. Appl. Physiol. 96 (6), 2139-2145. https://doi.org/10.1152/japplphysiol… [cited by applicant]
Suresh, S., 2007. Biomechanics and biophysics of cancer Cells☆. Acta Biomater. 3 (4), 413-438. https://doi.org/10.1016/j.actbio.2007.04.002. [cited by applicant]
Tan, J.C.H., 2006. Mechanosensitivity and the eye: cells coping with the pressure. Br. J. Ophthalmol. 90 (3), 383-388. https://doi.org/10.1136/bjo.2005.079905. BMJ. [cited by applicant]
Verstraeten, Valerie L.R. M., et al., 2008. Increased mechanosensitivity and nuclear stiffness in hutchinson-gilford progeria cells: effects of farnesyltransferase inhibitors. Aging Cell 7 (3), 383-393. https://doi.org/… [cited by applicant]
Vollrath, Melissa A., et al., 2007. The micromachinery of mechanotransduction in hair cells. Annu. Rev. Neurosci. 30 (1), 339-365. https://doi.org/10.1146/annurev.neuro.29.051605.112917. [cited by applicant]
Wang, Y., et al., 2006. A model for the role of integrins in flow induced mechanotransduction in osteocytes. J. Biomech. 39, S238. https://doi.org/10.1016/ s0021-9290(06)83892-3. Elsevier BV. [cited by applicant]
Weinbaum, Sheldon, et al., 2007. The structure and function of the endothelial glycocalyx layer. Annu. Rev. Biomed. Eng. 9 (1), 121-167. https://doi.org/10.1146/ annurev.bioeng.9.060906.151959. Annual Reviews. [cited by applicant]
Wolf, Katarina, et al., 2007. Multi-step pericellular proteolysis controls the transition from individual to collective cancer cell invasion. Nat. Cell Biol. 9 (8), 893-904. https://doi.org/10.1038/ncb1616. [cited by applicant]
Cui, Wei, et al., 2004. Changes in gene expression in response to mechanical strain in human scleral fibroblasts. Exp. Eye Res. 78 (2), 275-284. https://doi.org/10.1016/j. exer.2003.10.007. Elsevier BV. [cited by applicant]
Heydemann, Ahlke, Mcnally, Elizabeth M., 2007. Consequences of disrupting the dystrophin-sarcoglycan complex in cardiac and skeletal myopathy. Trends Cardiovasc. Med. 17 (2), 55-59. https://doi.org/10.1016/j.tcm.2006.12… [cited by applicant]
Liu, Zhendong, et al., 2016. Low carotid artery wall shear stress is independently associated with brain white-matter hyperintensities and cognitive impairment in older patients. Atherosclerosis 247, 78-86. https://doi.… [cited by applicant]
Li, Yi-Shuan, J., et al., 2005. Molecular basis of the effects of shear stress on vascular endothelial cells. J. Biomech. 38 (10), 1949-1971. https://doi.org/10.1016/j.jbiomech.2004.09.030. Elsevier BV. [cited by applicant]
Martens, Remy J.h., et al., 2013. Sublingual microvascular glycocalyx dimensions in lacunar stroke patients. Cerebrovasc. Dis. 35 (5), 451-454. https://doi.org/10.1159/000348854. [cited by applicant]
Nakamura, Takamitsu, et al., 2011. Endothelial vasomotor dysfunction in the brachial artery predicts the short-term development of early stage renal dysfunction in patients with coronary artery disease. Int. J. Cardiol.… [cited by applicant]
Palmer, Bradley M., 2005. Thick filament proteins and performance in human heart failure. Heart Fail. Rev. 10 (3), 187-197. https://doi.org/10.1007/s10741-005-5249-1. [cited by applicant]
Sarntinoranont, Malisa, et al., 2003. Interstitial stress and fluid pressure within A growing tumor. Ann. Biomed. Eng. 31 (3), 327-335. https://doi.org/10.1114/1.1554923. Springer Nature. [cited by applicant]
Sidnawi, Bchara, et al., 2019. Characterization of blood velocity in arteries using A combined analytical and Doppler maging approach. Phys. Rev. Fluids 4 (5). https://doi.org/10.1103/physrevfluids.4.053101. American Ph… [cited by applicant]
Timmins, Lucas H., et al., 2014. Focal association between wall shear stress and clinical coronary artery disease progression. Ann. Biomed. Eng. 43 (1), 94-106. https://doi.org/10.1007/s10439-014-1155-9. [cited by applicant]