SMART PACS WORKFLOW SYSTEMS AND METHODS DRIVEN BY EXPLICIT LEARNING FROM USERS

• US 20130129165A1
• Filed: 11/23/2011
• Published: 05/23/2013
• Est. Priority Date: 11/23/2011
• Status: Active Grant

• Current Assignee: General Electric Company
• Original Assignee: Tianyi Wang
• Inventors: Dekel, Shai, Ranjan, Sohan Rashmi, Sherman, Alexander, Avasarala, Viswanath, Liu, Xiaofeng, Iankoulski, Alexandre Nikolov, Wang, Tianyi
• Granted Patent: US 8,923,580 B2
• Time in Patent Office: Days
• US Class Current: 382/128
• CPC Class Codes: G16H 30/20 for handling medical images, e.g. DICOM, HL7 or PACS
• CPC Class Codes: G16H 30/40 for processing medical images, e.g. editing

Abstract

Certain embodiments of the present invention provide methods and systems for determining a hanging protocol for display of clinical images in a study. Certain embodiments provide a machine learning hanging protocol analysis system. The example system includes an image processing module to process image data to provide one or more features. The example system includes a learning engine to receive processed image data and additional data to learn and adapt a hanging protocol for repeated use by applying one or more machine learning algorithms to the processed image data and additional data. The learning engine is to continue to refine an available selection of candidate layouts based on the processed image data and additional data to provide one or more layout choices for selection to form a hanging protocol for display of image and other data.

Claims

1. A method to determine a hanging protocol for clinical image display, said method comprising:

monitoring, using a processor, user workflow in a first session;

accepting, using a processor, user input to record/teach at least a portion of the workflow for repeat setup in a second session;

developing a set of user preferences based on the monitoring and the user input; and

applying one or more machine learning algorithms to develop one or more candidate layouts for selection and application as a hanging protocol.

2. The method of claim 1, further comprising creating a snapshot of a layout and associated parameters to form a layout candidate for a hanging protocol.

3. The method of claim 1, further comprising facilitating user modification of a saved workflow element in a saved hanging protocol.

4. The method of claim 1, further comprising automatically adapting the hanging protocol based on a stored user layout of images.

5. The method of claim 1, further comprising outputting a hanging protocol including one or more parameters to generate a layout for display of images.

6. The method of claim 1, wherein the one or more machine learning algorithms comprises at least one of a lazy learning algorithm, a case based learning algorithm, and an eager learning algorithm.

7. The method of claim 1, further comprising applying image processing in addition to the one or more machine learning algorithms.

8. A tangible computer-readable storage medium including a set of instructions for execution by a processor, the instructions, when executed, to implement a method to determine a hanging protocol for clinical image display, said method comprising:

monitoring user workflow in a first session;

accepting user input to record/teach at least a portion of the workflow for repeat setup in a second session;

developing a set of user preferences based on the monitoring and the user input; and

applying one or more machine learning algorithms to develop one or more candidate layouts for selection and application as a hanging protocol.

9. The computer-readable medium of claim 8, further comprising creating a snapshot of a layout and associated parameters to form a layout candidate for a hanging protocol.

10. The computer-readable medium of claim 8, further comprising facilitating user modification of a saved workflow element in a saved hanging protocol.

11. The computer-readable medium of claim 8, further comprising automatically adapting the hanging protocol based on a stored user layout of images.

12. The computer-readable medium of claim 8, further comprising outputting a hanging protocol including one or more parameters to generate a layout for display of images.

13. The computer-readable medium of claim 8, wherein the one or more machine learning algorithms comprises at least one of a lazy learning algorithm, a case based learning algorithm, and an eager learning algorithm.

14. The computer-readable medium of claim 8, further comprising applying image processing in addition to the one or more machine learning algorithms.

15. A machine learning hanging protocol analysis system comprising:

an image processing module to process image data to provide one or more features; and

a learning engine to receive processed image data and additional data to learn and adapt a hanging protocol for repeated use by applying one or more machine learning algorithms to the processed image data and additional data, the learning engine continuing to refine an available selection of candidate layouts based on the processed image data and additional data to provide one or more layout choices for selection to form a hanging protocol for display of image and other data.

16. The system of claim 15, wherein the one or more layout choices are each associated with a computed confidence score.

17. The system of claim 15, wherein one or more selected candidate layouts are synthesized to form the one or more layout choices to form the hanging protocol.

18. The system of claim 15, wherein the additional data comprises at least one of image meta data and non-image data.

19. The system of claim 15, wherein the learning engine is to facilitate user creation of a snapshot of a layout and associated parameters to form a layout candidate for a hanging protocol.

20. The system of claim 15, further comprising a user interface to facilitate user modification of a saved workflow element in a saved hanging protocol.

21. The system of claim 15, wherein the one or more machine learning algorithms comprises at least one of a lazy learning algorithm, a case based learning algorithm, and an eager learning algorithm.

22. The system of claim 15, further comprising an option for user selection to trigger the system to learn a displayed layout or portion of a layout for a hanging protocol.