RADIOLOGIST ASSISTED MACHINE LEARNING
A computerized medical diagnostic system uses a training dataset that is updated based on reports generated by a radiologist. AI and/or CAD is used to make an initial determination of no finding, finding, or diagnosis based on the training dataset. Normal results with a high confidence of no finding are not reviewed by the radiologist. Low confidence results, findings, and diagnosis are reviewed by the radiologist. The radiologist generates a report that associates terminology and weighting with marked 3D image volumes. The report is used to update the training dataset.
1 . A method comprising:
continuously updating a training dataset while analyzing medical image data with a medical image diagnostic computer having machine-learning capability, comprising the steps of:
using a three-dimensional cursor to select a sub-volume of a medical image, wherein the selected sub-volume corresponds to an item on a diagnostic checklist;
analyzing the selected sub-volume to create a human-generated analysis; and
using the human-generated analysis to update the training dataset.
2 . The method of claim 1 comprising analyzing the selected sub-volume using the training dataset to create a machine-generated analysis with the diagnostic computer before manually analyzing the selected sub-volume.
3 . The method of claim 2 comprising resolving disagreement between the human-generated analysis and the machine-generated analysis before using the human-generated analysis to update the training dataset.
4 . The method of claim 3 comprising generating a computer-made explanation for the machine-generated analysis.
5 . The method of claim 4 comprising updating the human-generated analysis based on the explanation before using the human-generated analysis to update the training dataset.
6 . The method of claim 3 comprising prompting a consensus review of the human-generated analysis and machine-generated analysis.
7 . The method of claim 6 comprising updating the human-generated analysis based on the consensus review before using the human-generated analysis to update the training dataset.
8 . The method of claim 1 comprising the diagnostic computer retrieving and presenting patient-specific data pertinent to the item on the checklist to facilitate creation of the human-generated analysis.
9 . The method of claim 1 wherein creating the human-generated analysis comprises creating at least one of: terminology describing findings or diagnosis; marked pixels or voxels; and an indication of certainty of the findings or diagnosis.
10 . The method of claim 1 wherein creating the machine-generated analysis comprises creating at least one of: terminology describing findings or diagnosis; marked pixels or voxels; and an indication of certainty of the findings or diagnosis.
11 . The method of claim 1 comprising filtering out tissue within the selected sub-volume that is not associated with a finding.
12 . The method of claim 11 comprising performing segmentation on tissue within the selected sub-volume.
13 . The method of claim 11 comprising automatically re-sizing the three-dimensional cursor to encompass tissue associated with the finding.
14 . The method of claim 3 wherein the checklist comprises multiple items, each of which is analyzed, and comprising generating a report based on the human-generated analysis.
15 . The method of claim 14 comprising including an indication of disagreement between the human-generated analysis and the machine-generated analysis.
16 . The method of claim 1 comprising the three-dimensional cursor visually indicating confidence or dangerousness of a diagnosis.
17 . The method of claim 11 comprising placing tissue associated with a finding in a virtual container.
18 . The method of claim 17 comprising selecting a virtual container from a normal finding container, a disease-specific container, and differential diagnosis container.
19 . An apparatus comprising:
a medical image diagnostic computer having machine-learning capability, the diagnostic computer comprising a non-transitory medium on which is stored computer program logic that continuously updates a training dataset while analyzing medical image data with, comprising:
item selection logic that selects a sub-volume of a medical image with a three-dimensional cursor, wherein the selected sub-volume corresponds to an item on a diagnostic checklist;
input logic that receives input that creates a human-generated analysis of the selected sub-volume; and
update logic that updates the training dataset based on the human-generated analysis.
20 . The apparatus of claim 19 comprising diagnostic logic that analyzes the selected sub-volume using the training dataset to create a machine-generated analysis before the human-generated analysis is generated.
21 . The apparatus of claim 20 comprising resolution logic that resolves disagreement between the human-generated analysis and the machine-generated analysis before the human-generated analysis is used to update the training dataset.
22 . The apparatus of claim 21 comprising virtual guru logic that generates a computer-made explanation for the machine-generated analysis.
23 . The apparatus of claim 22 wherein the resolution logic updates the human-generated analysis based on the explanation before using the human-generated analysis to update the training dataset.
24 . The apparatus of claim 21 wherein the resolution logic prompts a consensus review of the human-generated analysis and machine-generated analysis.
25 . The apparatus of claim 24 wherein the resolution logic updates the human-generated analysis based on the consensus review before using the human-generated analysis to update the training dataset.
26 . The apparatus of claim 19 comprising the diagnostic computer retrieving and presenting patient-specific data pertinent to the item on the checklist to facilitate creation of the human-generated analysis.
27 . The apparatus of claim 19 wherein the human-generated analysis comprises at least one of: terminology describing findings or diagnosis; marked pixels or voxels; and an indication of certainty of the findings or diagnosis.
28 . The apparatus of claim 19 wherein the machine-generated analysis comprises at least one of: terminology describing findings or diagnosis; marked pixels or voxels; and an indication of certainty of the findings or diagnosis.
29 . The apparatus of claim 19 comprising filtering logic that removes from an image tissue within the selected sub-volume that is not associated with a finding.
30 . The apparatus of claim 29 comprising segmentation logic that segments tissue within the selected sub-volume.
31 . The apparatus of claim 29 comprising logic that re-sizes the three-dimensional cursor to encompass tissue associated with the finding.
32 . The apparatus of claim 21 wherein the checklist comprises multiple items, each of which is analyzed, and comprising logic that generates a report based on the human-generated analysis.
33 . The apparatus of claim 32 wherein the logic that generates the report includes an indication of disagreement between the human-generated analysis and the machine-generated analysis in the report.
34 . The apparatus of claim 19 comprising the three-dimensional cursor visually indicating confidence or dangerousness of a diagnosis.
35 . The apparatus of claim 29 comprising a virtual container in which tissue associated with a finding is placed.
36 . The apparatus of claim 35 wherein the virtual container is selected from a normal finding container, a disease-specific container, and differential diagnosis container.