Method for integrating image analysis, longitudinal tracking of a region of interest and updating of a knowledge representation

Abstract

A method for integrating image analysis, longitudinal tracking of a region of interest and updating of a knowledge representation, said method comprising the steps of: retrieving an image representation of a sample structure from an image database; automatically selecting a generic structure from a database containing a plurality of generic structures based on an imaging modality of the sample structure, at least one knowledge representation stored in a second database, said knowledge representation associated with said selected generic structure, the knowledge representation being specific to the imaging modality; mapping the selected generic structure to the sample structure; automatically determining at least one region of interest within the sample structure or allowing the user to select a region of interest; automatically selecting at least one diagnostic finding or allowing the user to select at least one diagnostic finding from a focused set knowledge representations; retrievably storing the at least one diagnostic finding in the electronic record; and monitoring the electronic record for changes to the at least one diagnostic finding or new diagnostic findings and using such changes or new diagnostic findings to update the knowledge representation in the second database.

What is claimed is: 
     
         1 . A method for integrating image analysis, longitudinal tracking of a region of interest and updating of a knowledge representation, said method comprising the steps of:
 retrieving an image representation of a sample structure from an image database;   automatically selecting a generic structure from a database based on an imaging modality of the sample structure, at least one knowledge representation stored in a second database, said knowledge representation being associated with said selected generic structure, the knowledge representation being specific to the imaging modality;   mapping the selected generic structure to the sample structure;   automatically determining at least one region of interest within the sample structure or allowing the user to select a region of interest;   automatically selecting at least one diagnostic finding or allowing the user to select at least one diagnostic finding from a focused set knowledge representations;   retrievably storing the at least one diagnostic finding in the electronic record; and   monitoring the electronic record for changes to the at least one diagnostic finding or new diagnostic findings and using such changes or new diagnostic findings to update the knowledge representation in the second database.   
     
     
         2 . The method of  claim 1 , wherein the step of allowing the user to select at least one diagnostic finding from the focused set of knowledge representation includes allowing the user to enter the at least one diagnostic finding using free-form text. 
     
     
         3 . The method of  claim 1 , wherein the selected generic structure is related to the sample structure by imaging modality and one or more attributes selected from the group (size, dimensions, area, shape, volume, weight, density, location, anatomical organ, and orientation). 
     
     
         4 . The method of  claim 1 , wherein the selected generic structure has coordinate data defined therein. 
     
     
         5 . The method of  claim 1 , wherein the knowledge representation is specific to an anatomical organ in which the region of interest is located and the imaging modality. 
     
     
         6 . The method of  claim 4 , further comprising:
 using the coordinate data to generate natural language statements describing a location of the region of interest in the anatomy;   automatically generating a diagnostic report based on the at least one diagnostic finding, and including the natural language statements describing the location of the region of interest in the anatomy; and   storing the diagnostic report in the electronic record.   
     
     
         7 . The method of  claim 1 , wherein the step of automatically selecting a generic structure is based on the imaging modality and a comparison of content of the sample structure to the content of the generic structure. 
     
     
         8 . The method of  claim 1 , further comprising:
 for each at least one region of interest automatically selecting follow-up care or allowing the user to select from a focused set of follow-up care options; and   storing the selected follow-up care in the electronic record.   
     
     
         9 . The method of  claim 8 , wherein the step of monitoring the electronic record includes checking for changes to the selected follow-up care and using such changes to update the knowledge representation in the second database. 
     
     
         10 . The method of  claim 1 , wherein the step of monitoring the electronic record includes checking for changes to treatment outcome and using such changes to update the knowledge representation in the second database. 
     
     
         11 . A method for integrating image analysis, longitudinal tracking of a region of interest and updating of a knowledge representation, comprising the steps of:
 retrieving an image representation of a sample structure depicting at least a portion of an anatomical organ from an image database;   determining at least one region of interest within the sample structure or allowing the user to select a region of interest;   automatically selecting at least one diagnostic finding or allowing the user to select at least one diagnostic finding from a focused set of knowledge representations stored in a database, the specific focused set of knowledge representations being specific to the anatomical organ and an imaging modality used to capture the image representation;   retrievably storing the at least one diagnostic finding in the electronic record;   monitoring the electronic record for changes and/or additions to the at least one diagnostic finding and updating the knowledge representation to reflect the changes and/or additions to the at least one diagnostic finding.   
     
     
         12 . The method of  claim 11 , wherein the step of monitoring the electronic record includes checking for changes to treatment outcome and using such changes to update the knowledge representation in the database. 
     
     
         13 . A method for integrating image analysis, longitudinal tracking of a region of interest and updating of a knowledge representation, the method comprising the steps of:
 recording at least one diagnostic finding for a given region of interest in an electronic record;   monitoring the electronic record for changes to the at least one diagnostic finding for the region of interest; and   automatically updating a knowledge representation stored in a database to reflect the changes to the at least one diagnostic finding for the region of interest.   
     
     
         14 . The method of  claim 13 , further comprising:
 retrieving an image representation of sample structure depicting at least a portion of an anatomical organ from an image database;   automatically determining at least one region of interest within the sample structure or allowing the user to select a region of interest;   automatically selecting at least one diagnostic finding or allowing the user to select at least one diagnostic finding from a focused set of knowledge representations specific to at least one of the anatomical organ and an imaging modality used to capture the image representation; and   retrievably storing the at least one diagnostic finding in the electronic record.   
     
     
         15 . The method of  claim 13 , wherein the step of monitoring the electronic record includes checking for changes to treatment outcome and using such changes to update the knowledge representation in the database. 
     
     
         16 . A method for progressively updating a knowledge representation, said method comprising a sequence of machine learning models ML_t, where ML_{t+1} is trained later in time than ML_t, each model ML_t trained based on a set of training data D_t consisting of training samples s_{t,i} with respective training weights w_{t,i}, each sample s_{t,i} that is similar to a sample s_{t−1, k} having a reduced weight w_{t,i}<w_{t−1, k}, and samples with updated outcome having an increased weight w_{t,i}>w_{t−1, k}.