Smart PACS workflow systems and methods driven by explicit learning from users
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.
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 learn 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 the developed set of user preferences 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 non-transitory 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 learn 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 the developed set of user preferences 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.