System and method for real-time feature sensitivity analysis based on contextual information

• US 7,087,018 B2
• Filed: 11/06/2003
• Issued: 08/08/2006
• Est. Priority Date: 11/13/2002
• Status: Active Grant

• Current Assignee: Siemens Healthineers AG (Siemens AG)
• Original Assignee: Siemens Medical Solutions USA Incorporated (Siemens AG)
• Inventors: Krishnan, Arun, Zhou, Xiang Sean, Comaniciu, Dorin, Paine, Diane, Duggirala, Bhavani
• Primary Examiner(s): Jaworski, Francis J.
• Application Number:
  US10/702,984
• Publication Number:
  US 20040133083A1
• Time in Patent Office: 1,006 Days
• Field of Search: 600300-301, 600/407, 600/409, 600/425, 600/427, 600/437, 600/443, 600/447, 600/473, 600/476, 600/481, 600/483, 600/485, 600/500, 600/504, 600/509, 600/513, 600/529, 600/544, 600/549
• US Class Current: 600/300
• 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 8/02 Measuring pulse or heart rate
• CPC Class Codes: A61B 8/065 to determine blood output from the heart in general A61B5/029
• CPC Class Codes: A61B 8/0858 involving measuring tissue layers, e.g. skin, interfaces
• CPC Class Codes: A61B 8/0883 for diagnosis of the heart
• CPC Class Codes: A61B 8/463 characterised by displaying multiple images or images and diagnostic data on one display composite display in devices for short-range imaging with acoustic waves G01S7/52074
• CPC Class Codes: G06F 18/2113 by ranking or filtering the set of features, e.g. using a measure of variance or of feature cross-correlation
• CPC Class Codes: G06F 18/2115 by evaluating different subsets according to an optimisation criterion, e.g. class separability, forward selection or backward elimination
• CPC Class Codes: G06V 10/768 using context analysis, e.g. recognition aided by known co-occurring patterns
• CPC Class Codes: G06V 2201/031 of internal organs
• CPC Class Codes: G16H 40/63 for local operation

Abstract

A system and method for assigning feature sensitivity values to a set of potential measurements to be taken during a medical procedure of a patient in order to provide a medical diagnosis is disclosed. Data is received from a sensor that represents a particular medical measurement. The received data and context data are analyzed with respect to one or more sets of training models. Feature sensitivity values are derived for the particular medical measurement and other potential measurements to be taken based the analysis, and the feature sensitivity values are outputted.

Claims

1. A method for assigning patient-specific feature sensitivity values to a set of measurements to be taken during a medical procedure of a patient in order to provide a medical diagnosis, the method comprising;

receiving data from a sensor representing a particular medical measurement for the patient;

analyzing the particular medical measurement and context data with respect to one or more sets of training models;

deriving patient-specific feature sensitivity values for the particular medical measurement and other currently unknown measurements which may be taken for the patient based on the analysis;

outputting the patient-specific feature sensitivity values for the particular measurement and the other currently unknown measurements based upon the derived feature sensitivity values for said particular medical measurement; and

recommending additional measurements for one or more of the currently unknown measurements to be taken for the patient that have high feature sensitivity values based upon said outputted feature sensitivity values.

2. The method of claim 1 wherein the medical procedure is an echocardiogram examination.

3. The method of claim 1 wherein the context data comprises vital statistics relating to the patient.

4. The method of claim 3 wherein the vital statistics include the patient'"'"'s age.

5. The method of claim 3 wherein the vital statistics include the patient'"'"'s height.

6. The method of claim 3 wherein the vital statistics include the patient'"'"'s weight.

7. The method of claim 3 wherein the vital statistics include the patient'"'"'s blood pressure measurements.

8. The method of claim 1 wherein the context data comprises the patient'"'"'s symptoms.

9. The method of claim 8 wherein the symptoms include indications of pain.

10. The method of claim 8 wherein the symptoms include shortness of breadth.

11. The method of claim 1 wherein context data comprises baseline test data for the patient.

12. The method of claim 1 wherein the medical sensor is an ultrasound transducer.

13. The method of claim 12 wherein the received data is image data.

14. The method of claim 1 wherein the analyzing step further comprises the step of:

analyzing the received data, context data and training models using an integral model.

15. The method of claim 1 wherein the analyzing step further comprises the step of:

analyzing the received data, context data and training models using a sample expectation model.

16. The method of claim 1 wherein the analyzing step further comprises the step of:

analyzing the received data, context data and training models using both an integral model and a sampling expectation model.

17. The method of claim 1 wherein the step of outputting the patient-specific feature sensitivity values further comprises the step of:

providing an indication of a probability for one or more medical diagnosis; and

providing a listing of potential measurements for the patient wherein a feature sensitivity value is assigned to each currently unknown measurement.

18. The method of claim 17 further comprising the steps of:

receiving input representing a selection of one or more currently unknown measurements for the patient for which measurements are to be taken;

receiving data from the medical sensor corresponding to the selected one or more currently unknown measurements for the patient;

analyzing the received data for the selected one or more currently unknown measurements, data corresponding to any prior measurements and context data with the training models;

deriving patient specific feature sensitivity values for the set of currently unknown measurements; and

outputting the patient specific feature sensitivity values.

19. The method of claim 1 wherein a user uses the outputted feature sensitivity values to assist in making a medical diagnosis.

20. The method of claim 1 wherein the outputted feature sensitivity values are provided in real time.

21. A system for assigning patient-specific feature sensitivity values to a set of currently unknown measurements to be taken during a medical procedure of a patient in order to provide a medical diagnosis, the system comprising:

a medical sensor that provides medical measurements taken of a patient;

a processor connected to the medical sensor, the processor receiving the medical measurements for the patient taken by the medical sensor and context data relating to the patient, the processor analyzing the sensor measurements and context data and determining patient specific feature sensitivity values for the set of currently unknown measurements which may be taken by the sensor based upon the determined feature sensitivity values of the analyzed sensor measurements and context data, the processor further recommending which additional measurements of the currently unknown measurements should be taken for the patient based on the patient specific feature sensitivity values; and

a display device for displaying the sensor data and determined feature sensitivity values.

22. The system of claim 21 further comprising a database associated with the processor, the database including a set of training models that are compared to the context data and sensor measurements for the patient in order to obtain patient specific feature sensitivity values for the set of currently unknown measurements.

23. The system of claim 21 wherein the medical sensor is an ultrasound transducer.

24. The system of claim 21 wherein the medical procedure is an echocardiogram examination.

25. The system of claim 21 wherein the context data comprises vital statistics relating to the patient.

26. The system of claim 25 wherein the vital statistics include the patient'"'"'s age.

27. The system of claim 25 wherein the vital statistics include the patient'"'"'s height.

28. The system of claim 25 wherein the vital statistics include the patient'"'"'s weight.

29. The system of claim 25 wherein the vital statistics include the patient'"'"'s blood pressure measurements.

30. The system of claim 21 wherein the context data comprises the patient'"'"'s symptoms.

31. The system of claim 30 wherein the symptoms include indications of pain.

32. The system of claim 30 wherein the symptoms include shortness of breadth.

33. The system of claim 21 wherein context data comprises baseline test data for the patient.

34. The system of claim 21 wherein a user uses the determined feature sensitivity values to assist in making a medical diagnosis.

35. The system of claim 21 wherein the determined feature sensitivity values are provided in real time.

36. The system of claim 21 wherein context data comprises medical measurements from a patient'"'"'s prior medical examinations.