Systems, devices, and methods for detecting occlusions in a biological subject

• US 20090281413A1
• Filed: 04/30/2009
• Published: 11/12/2009
• Est. Priority Date: 12/18/2007
• Status: Abandoned Application

• Current Assignee: Gearbox LLC (Intellectual Ventures LLC)
• Original Assignee: Searete LLC (Intellectual Ventures LLC)
• Inventors: Boyden, Edward S., Leuthardt, Eric C.
• Application Number:
  US12/387,453
• Publication Number:
  US 20090281413A1
• Time in Patent Office: Days
• US Class Current: 600/407
• 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 5/02007 Evaluating blood vessel condition, e.g. elasticity, compliance
• CPC Class Codes: A61B 5/02028 Determining haemodynamic parameters not otherwise provided for, e.g. cardiac contractility or left ventricular ejection fraction
• CPC Class Codes: A61B 5/14556 by fluorescence A61B5/14555 takes precedence
• CPC Class Codes: A61B 5/412 Detecting or monitoring sepsis
• CPC Class Codes: A61B 5/4528 Joints A61B5/4533, A61B5/4538 take precedence
• CPC Class Codes: A61B 5/6824 Arm or wrist
• CPC Class Codes: A61B 5/6828 Leg
• CPC Class Codes: A61B 5/6831 Straps, bands or harnesses
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: A61B 6/481 involving the use of contrast agents
• CPC Class Codes: A61B 6/482 involving multiple energy imaging
• CPC Class Codes: A61B 6/504 for diagnosis of blood vessels, e.g. by angiography
• CPC Class Codes: A61B 8/06 Measuring blood flow measuring volume flow in general G01F, e.g. G01F1/66, G01F1/72; measuring speed of fluids in general G01P5/00
• CPC Class Codes: A61B 8/56 Details of data transmission or power supply

Abstract

Systems, devices, and methods are described for detecting an embolus, thrombus, or a deep vein thrombus in a biological subject.

Claims

1. A method, comprising:

performing a real-time comparison of a first detected optical energy absorption profile of a first region within a biological subject to characteristic spectral signature information, the detected optical energy absorption profile including at least one of an emitted optical energy or a remitted optical energy;

determining whether an occlusion event has occurred;

obtaining a second detected optical energy absorption profile of a second region within a biological subject, the second region having a different location from the first region;

performing a real-time comparison of the second detected optical energy absorption profile to characteristic spectral signature information; and

determining whether an occlusion event has occurred.

2. The method of claim 1, further comprising:

performing a real-time comparison of the first detected optical energy absorption profile to a statistical learning model associated with the biological subject; and

determining whether an occlusion event has occurred.

3. The method of claim 1, further comprising:

performing a real-time comparison of the second detected optical energy absorption profile to a statistical learning model associated with the biological subject; and

determining whether an occlusion event has occurred.

4. The method of claim 1, further comprising:

updating at least one parameter associated with the statistical learning model based at least in part on at least one parameter associated with the first detected optical energy absorption profile.

5. The method of claim 1, further comprising:

updating the statistical learning model based at least in part on at least one parameter associated with the second detected optical energy absorption profile.

6. The method of claim 1, further comprising:

updating the statistical learning model based at least in part on at least one parameter associated with the obtaining a second detected optical energy absorption profile.

7. The method of claim 1, further comprising:

updating the statistical learning model based at least in part on at least one parameter associated with the real-time comparison of the first detected optical energy absorption profile to characteristic spectral signature information.

8. The method of claim 1, further comprising:

updating the statistical learning model based at least in part on at least one parameter associated with the real-time comparison of the second detected optical energy absorption profile to characteristic spectral signature information.

9. The method of claim 1, further comprising:

activating at least one of a statistical leaning modeling protocol or a heuristic trend analysis protocol based on a result of the real-time comparison of the first detected optical energy absorption profile to at least one parameter associated with the statistical learning model.

10. The method of claim 1, further comprising:

activating at least one of a statistical leaning modeling protocol or a heuristic trend analysis protocol based on a result of the real-time comparison of the second detected optical energy absorption profile to at least one parameter associated with the statistical learning model.

13. The method of claim 1, wherein determining whether the embolic event has occurred includes generating time-varying spectral information based on the real-time comparison of the first detected optical energy absorption profile, the second detected optical energy absorption profile, or the difference of the at least one spectral component thereof to the statistical learning model associated with the biological subject.

11. A method, comprising:

performing a real-time comparison of at least a first detected optical energy absorption profile of a first location within a biological subject to a second detected optical energy absorption profile of a second location within a biological subject;

determining whether an embolic event has occurred;

performing a real-time comparison of at least one of the first detected optical energy absorption profile of the first location within a biological subject, the second detected optical energy absorption profile of the second location within the biological subject, or a difference of at least one spectral component thereof to a statistical learning model associated with the biological subject; and

determining whether an embolic event has occurred.

12. The method of claim 11, wherein determining whether the embolic event has occurred includes generating time-varying spectral information based on the real-time comparison of the first detected optical energy absorption profile of the first location within the biological subject to the second detected optical energy absorption profile of the second location within the biological subject.

14. A method, comprising:

performing a real-time comparison of at least a first detected optical energy absorption profile of a first location within a biological subject to a second detected optical energy absorption profile of a second location within a biological subject;

determining whether an embolic event has occurred;

performing a real-time comparison of at least one of the first detected optical energy absorption profile, the second detected optical energy absorption profile, or a difference of at least one spectral component thereof to characteristic spectral signature information; and

generating a response based at least in part on the comparison.

15. The method of claim 14, wherein generating the response includes generating a visual, audio, or tactile representation indicative of whether an embolic event has occurred.

16. The method of claim 14, wherein generating the response includes generating a visual, audio, or tactile representation indicative of at least one physical parameter associated with an embolus, a thrombus, or a deep vein thrombus.

17. The method of claim 14, wherein generating the response includes generating a visual, audio, or tactile representation indicative of at least one physical parameter indicative of at least one dimension of an embolus, a thrombus, or a deep vein thrombus.

18. The method of claim 14, wherein generating the response includes generating a visual, audio, or tactile representation of an embolus, a thrombus, or a deep vein thrombus.

19. The method of claim 14, wherein generating the response includes generating a visual, audio, or tactile representation of at least one spectral parameter associated with an embolus, a thrombus, or a deep vein thrombus.

20. The method of claim 14, wherein generating the response includes generating a visual, audio, or tactile representation indicative of at least one of blood spectral information, fat spectral information, muscle spectral information, or bone spectral information.

21. The method of claim 14, wherein generating the response includes automatically updating a statistical learning model.

22. The method of claim 14, wherein generating the response includes activating at least one of a statistical leaning modeling protocol or a heuristic trend analysis protocol.

23. A method, comprising:

performing a real-time comparison of at least a first detected optical energy absorption profile to a second detected optical energy absorption profile of a region within a biological subject;

determining whether an embolic event has occurred;

performing a real-time comparison of at least one of the first detected optical energy absorption profile, the second detected optical energy absorption profile, or a difference of at least one spectral component thereof to characteristic spectral signature information; and

generating a response based at least in part on the comparison.

24. A method, comprising:

performing a real-time comparison of a first detected optical energy absorption profile of a portion of a tissue within a biological subject to characteristic spectral signature information, the detected optical energy absorption profile including at least one of an emitted optical energy or a remitted optical energy;

determining whether an embolic event has occurred;

obtaining a second detected optical energy absorption profile of the portion of a tissue within a biological subject;

performing a real-time comparison of the second detected optical energy absorption profile to a statistical learning model associated with the biological subject; and

determining whether an embolic event has occurred.

25. The method of claim 24, further comprising:

updating at least one parameter associated with the statistical learning model based at least in part on at least one parameter associated with the first detected optical energy absorption profile.

26. The method of claim 24, further comprising:

updating the statistical learning model based at least in part on at least one parameter associated with the second detected optical energy absorption profile.

27. The method of claim 24, further comprising:

updating the statistical learning model based at least in part on at least one parameter associated with the obtaining a second detected optical energy absorption profile.

28. The method of claim 24, further comprising:

updating the statistical learning model based at least in part on at least one parameter associated with the real-time comparison of the first detected optical energy absorption profile to characteristic spectral signature information.

29. The method of claim 24, further comprising:

activating at least one of a statistical leaning modeling protocol or a heuristic trend analysis protocol based on a result of the real-time comparison of the second detected optical energy absorption profile to at least one parameter associated with the statistical learning model.