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US-20260127848-A1 - EVALUATION OF AUTO-POSITIONING SOFTWARE PERFORMANCE WITH GENERATIVE ARTIFICIAL INTELLIGENCE

US20260127848A1US 20260127848 A1US20260127848 A1US 20260127848A1US-20260127848-A1

Abstract

One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to generative artificial intelligence (AI)-based large-scale automated evaluation of autonomous planning software (APS) performance. For example, a system can comprise a memory that can store computer executable components and a processor that can execute the computer executable components stored in the memory. The computer executable components can comprise an image generation component that can generate one or more diagnostic images. The computer executable components can further comprise an image evaluation component that can generate, based on defined image criteria, respective auto-positioning image matching scores for respective diagnostic images of the one or more diagnostic images by comparing the respective diagnostic images with respective sets of template images.

Inventors

  • Tisha Anie Abraham
  • Dattesh Dayanand Shanbhag
  • Chitresh Bhushan
  • Vanika Singhal
  • Apoorva Anil Agarwal
  • Bhavatharani S

Assignees

  • GE Precision Healthcare LLC

Dates

Publication Date
20260507
Application Date
20241107

Claims (20)

  1. 1 . A system, comprising: a memory that stores computer executable components; and a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise: an image generation component that generates one or more diagnostic images; and an image evaluation component that generates, based on defined image criteria, respective auto-positioning image matching scores for respective diagnostic images of the one or more diagnostic images by comparing the respective diagnostic images with respective sets of template images.
  2. 2 . The system of claim 1 , wherein the one or more diagnostic images are generated by an autonomous planning software (APS) or generated by reformatting non-reformatted outputs of an imaging device, wherein the APS further generates prescription plans for the one or more diagnostic images, and wherein the image evaluation component employs a set of foundation models and a vision transformer to generate the respective auto-positioning image matching scores.
  3. 3 . The system of claim 1 , wherein comparing a diagnostic image of the respective diagnostic images with a template image comprised in a set of template images comprises: extracting a first set of features from the template image; extracting a second set of features from the diagnostic image; and computing a similarity score based on the first set of features and the second set of features, wherein the similarity score indicates a degree of similarity between the diagnostic image and the template image.
  4. 4 . The system of claim 1 , wherein the image evaluation component performs majority voting based on similarity scores corresponding to the respective diagnostic images to determine whether the respective diagnostic images contain metal.
  5. 5 . The system of claim 1 , further comprising: a computation component that computes tolerance values based on the respective auto-positioning image matching scores, wherein the tolerance values are employable to define image quality metrics for APS.
  6. 6 . The system of claim 1 , further comprising: a data selection component that selects one or more datasets from a plurality of databases, wherein the one or more datasets are selected according to the defined image criteria, and wherein the one or more datasets are employable to generate the one or more diagnostic images.
  7. 7 . The system of claim 2 , further comprising: an image reformatting component that transforms respective non-reformatted outputs of the non-reformatted outputs into respective reformatted diagnostic images by employing an intelligent multi-planar (iMPR) reformatting tool.
  8. 8 . The system of claim 1 , wherein the respective auto-positioning image matching scores indicate performance of the APS in diagnostic imaging.
  9. 9 . A computer-implemented method, comprising: generating, by a system operatively coupled to a processor, one or more diagnostic images; and generating, by the system, based on defined image criteria, respective auto-positioning image matching scores for respective diagnostic images of the one or more diagnostic images by comparing the respective diagnostic images with respective sets of template images.
  10. 10 . The computer-implemented method of claim 9 , wherein the one or more diagnostic images are generated by an autonomous planning software (APS) or generated by reformatting non-reformatted outputs of an imaging device, wherein the APS further generates prescription plans for the one or more diagnostic images, and wherein the respective auto-positioning image matching scores are generated by employing a set of foundation models and a vision transformer.
  11. 11 . The computer-implemented method of claim 9 , wherein comparing a diagnostic image of the respective diagnostic images with a template image comprised in a set of template images comprises: extracting, by the system, a first set of features from the template image; extracting, by the system, a second set of features from the diagnostic image; and computing, by the system, a similarity score based on the first set of features and the second set of features, wherein the similarity score indicates a degree of similarity between the diagnostic image and the template image.
  12. 12 . The computer-implemented method of claim 9 , further comprising: performing, by the system, majority voting based on similarity scores corresponding to the respective diagnostic images to determine whether the respective diagnostic images contain metal.
  13. 13 . The computer-implemented method of claim 9 , further comprising: computing, by the system, tolerance values based on the respective auto-positioning image matching scores, wherein the tolerance values are employable to define image quality metrics for APS.
  14. 14 . The computer-implemented method of claim 9 , further comprising: selecting, by the system, one or more datasets from a plurality of databases, wherein the one or more datasets are selected according to the defined image criteria, and wherein the one or more datasets are employable to generate the one or more diagnostic images.
  15. 15 . The computer-implemented method of claim 10 , further comprising: transforming, by the system, respective non-reformatted outputs of the non-reformatted outputs into respective reformatted diagnostic images by employing an intelligent multi-planar (iMPR) reformatting tool.
  16. 16 . The computer-implemented method of claim 9 , wherein the respective auto-positioning image matching scores indicate performance of the APS in diagnostic imaging.
  17. 17 . A computer program product comprising a non-transitory computer readable memory having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to: generate one or more diagnostic images; and generate, based on defined image criteria, respective auto-positioning image matching scores for respective diagnostic images of the one or more diagnostic images by comparing the respective diagnostic images with respective sets of template images.
  18. 18 . The computer program product of claim 17 , wherein the one or more diagnostic images are generated by an autonomous planning software (APS) or generated by reformatting non-reformatted outputs of an imaging device, wherein the APS further generates prescription plans for the one or more diagnostic images, and wherein the program instructions are further executable by the processor to cause the processor to: employ a set of foundation models and a vision transformer to generate the respective auto-positioning image matching scores.
  19. 19 . The computer program product of claim 17 , wherein the program instructions are further executable by the processor to cause the processor to: compare a diagnostic image of the respective diagnostic images with a template image comprised in a set of template images by: extracting a first set of features from the template image; extracting a second set of features from the diagnostic image; and computing a similarity score based on the first set of features and the second set of features, wherein the similarity score indicates a degree of similarity between the diagnostic image and the template image.
  20. 20 . The computer program product of claim 17 , wherein the program instructions are further executable by the processor to cause the processor to: perform majority voting based on similarity scores corresponding to the respective diagnostic images to determine whether the respective diagnostic images contain metal.

Description

BACKGROUND The subject disclosure relates to artificial intelligence (AI) and, more specifically, to large-scale automated evaluation of Auto-positioning Software or Autonomous Planning Software (APS) performance with generative AI. SUMMARY The following presents a summary to provide a basic understanding of one or more embodiments described herein. This summary is not intended to identify key or critical elements, delineate scope of particular embodiments or scope of claims. Its sole purpose is to present concepts in a simplified form as a prelude to the more detailed description that is presented later. In one or more embodiments described herein, systems, computer-implemented methods, apparatus and/or computer program products that enable evaluation of APS performance with generative AI are provided. According to an embodiment, a system is provided. The system can comprise a memory that can store computer executable components. The system can further comprise a processor that can execute the computer executable components stored in the memory, where the computer executable components can comprise an image generation component that can generate one or more diagnostic images. The computer executable components can further comprise an image evaluation component that can generate, based on defined image criteria, respective auto-positioning image matching scores for respective diagnostic images of the one or more diagnostic images by comparing the respective diagnostic images with respective sets of template images. According to another embodiment, a computer-implemented method is provided. The computer-implemented method can comprise generating, by a system operatively coupled to a processor, one or more diagnostic images. The computer-implemented method can further comprise generating, by the system, based on defined image criteria, respective auto-positioning image matching scores for respective diagnostic images of the one or more diagnostic images by comparing the respective diagnostic images with respective sets of template images. According to yet another embodiment, a computer program product is provided. The computer program product can comprise a non-transitory computer readable memory having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to generate one or more diagnostic images. The program instructions can be further executable by the processor to cause the processor to generate, based on defined image criteria, respective auto-positioning image matching scores for respective diagnostic images of the one or more diagnostic images by comparing the respective diagnostic images with respective sets of template images. BRIEF DESCRIPTION OF THE DRAWINGS The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee. FIG. 1 illustrates a block diagram of an example, non-limiting system that can employ generative AI to evaluate the performance of an APS in accordance with one or more embodiments described herein. FIG. 2 illustrates another block diagram of an example, non-limiting system that can employ generative AI to evaluate the performance of an APS in accordance with one or more embodiments described herein. FIG. 3 illustrates a flow diagram of an example, non-limiting method that can employ generative AI to evaluate the performance of an APS in accordance with one or more embodiments described herein. FIG. 4 illustrates another flow diagram of an example, non-limiting method that can employ generative AI to evaluate the performance of an APS in accordance with one or more embodiments described herein. FIG. 5 illustrates example, non-limiting images associated with the performance evaluation of an APS. FIG. 6 illustrates a flow diagram of an example, non-limiting method to show the use of APS to generate multi-planar images of a human brain in accordance with one or more embodiments described herein. FIG. 7 illustrates a flow diagram of an example, non-limiting method to show the use of APS to generate multi-planar images of a human knee in accordance with one or more embodiments described herein. FIG. 8 illustrates a flow diagram of an example, non-limiting method that can be employed by an algorithm to perform template-based matching of diagnostic images in accordance with one or more embodiments described herein. FIG. 9 illustrates a flow diagram of an example, non-limiting method that can be employed by an algorithm to determine error tolerance limits for diagnostic images in accordance with one or more embodiments described herein. FIG. 10 illustrates a diagram of an example, non-limiting dashboard that can be employed in triaging in accordance with one or more embodiments described herein. FIG. 11 illustrates a diagram of an example, non-limiting dashboar