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Decision support system for medical therapy planning

A decision support and medical imaging system technology, applied in medical data mining, medical science, radiation therapy, etc., can solve the problem that radiological analysis cannot be maximized

Pending Publication Date: 2019-12-10
SIEMENS HEALTHCARE GMBH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Radiological analysis may not maximize the information obtained, where a large number of features are often extracted from images containing a large amount of redundant or irrelevant information
Manual radiology features are in predefined groupings, so it is likely that some predictive information is not fully captured by the predefined features

Method used

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  • Decision support system for medical therapy planning
  • Decision support system for medical therapy planning
  • Decision support system for medical therapy planning

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Embodiment Construction

[0073] Imaging-based artificial intelligence provides patient stratification and / or radiotherapy response prediction. This radiotherapy decision support can be based on pre-treatment CT or other modality scans. Therapy effects can be predicted based on imaging and / or non-imaging data, providing physician decision aid.

[0074] figure 1 One embodiment of a decision support system for generating a prognostic signature for therapy from radiological imaging data is shown. A signature is patient information or features of imaging data from medical images. The medical image is pre-processed, such as scaling, normalizing and / or segmenting, for a tumor or a region including a tumor. Instead of traditional radiology features, which are often hand-crafted, fully data-driven deep learning-based radiology features are used. Handmade radiology is used as ground truth because these features can be created from any image, allowing unsupervised learning or unlabeled ground truth for effec...

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Abstract

A decision support system for medical therapy planning is provided. For decision support in a medical therapy, machine learning provides a machine-learned generator for generating a prediction of outcome for therapy personalized to a patient. Deep learning may result in features more predictive of outcome than handcrafted features. More comprehensive learning may be provided by using multi-task learning, where one of the tasks (e.g., segmentation, non-image data, and / or feature extraction) is unsupervised and / or draws on a greater number of training samples than available for outcome prediction alone.

Description

technical field [0001] This patent document requires the filing of Provisional U.S. Patent Application Serial No. 62 / 677,716, filed May 30, 2018, and Provisional U.S. Patent Application Serial No. 62 / 745,712, filed October 15, 2018, under 35 U.S.C. § 119(e) Said Provisional United States Patent Application is hereby incorporated by reference for the benefit of the date hereof. Background technique [0002] This embodiment relates to decision support for therapy. A typical example is the application in radiation therapy. Radiation therapy is a useful and cost-effective treatment strategy for many types of cancer. Although radiation therapy is an effective cancer treatment, most patients subsequently experience radio-resistance and recurrence of their cancer. Physicians seek to select treatment based on the specific characteristics of the patient and their disease in order to avoid treatment resistance and relapse. [0003] Predictors of radiation response were largely lim...

Claims

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Application Information

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IPC IPC(8): G16H30/20G16H50/30
CPCG16H30/20G16H50/30G16H50/70G16H20/40G16H50/20A61B5/7267G06T2207/20081A61B6/032G06T7/0012G06T2207/20084G06T2207/10081A61N5/103G06N3/04
Inventor 娄彬A.卡门
Owner SIEMENS HEALTHCARE GMBH
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