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US 2026/0237069 A1Published application
Ai-based cell classification method and system
AssigneeVisiongate, Inc.
InventorsMichael G. Meyer, Alan C. Nelson
Life of this patent
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Abstract
A system and method for AI-based cell classification of cells from a patient sample to determine if cells indicative of lung cancer are present. In the system and method, 2D imaging is used to eliminate cells not likely to be indicative of lung cancer from subsequent 3D imaging, while 3D imaging is conducted for cells likely to be indicative of lung cancer. The method further provides training 2D cell classifiers for use in the system and method for AI-based cell classification.
Claims20
A method of training an AI-based cell classification system comprising:
- a) operating an optical tomography system to generate representative 2D images of cells;
- b) designating a known identifier for each of a plurality of the cells using the representative 2D image of such cell and a known cell identification method, wherein the known identifier is i) abnormal or having abnormal features;
- ii) Bronchial Epithelial Cell (BEC) or having BEC-like features, or iii) normal or non-BEC or not having abnormal features or BEC-like features;
- c) computing a plurality of cell feature measurements for each of the plurality of cells from the representative 2D image of such cell;
- d) performing a regression between the cell feature measurements and the known identifier of each cell to designate an abnormal 2D cell classification score and a BEC 2D cell classification score for each cell;
- e) designating a test identifier for each cell of i) having abnormal features;
- ii) having BEC-like features, or iii) not having abnormal features or BEC-like features based on the abnormal 2D cell classification score and the BEC 2D classification score the cell;
- and
- f) comparing the respective test identifiers to the known identifiers for each cell to calculate accuracy of the abnormal cell 2D classifier and the BEC 2D classifier.
6 more elements
The method of 12, further comprising:
- h) for a subset of cells within the plurality of cells to generate patient sample data that reflects a total number of enumerated analyzed cells having abnormal features, a total number of enumerated analyzed cells having BEC-like features, or a total number of enumerated analyzed cells, repeating the following operations:
- generating, by an optical tomography system, a representative 2D image of a cell from a patient sample comprising a plurality of cells;
- evaluating the representative 2D image using an abnormal cell 2D classifier to determine if the cell has abnormal features;
- evaluating the representative 2D image using a BEC 2D classifier to determine if the cell has BEC-like features;
- and
- generating, by the optical tomography system, a 3D image of the cell if the cell is determined to have abnormal features or to have BEC-like features, wherein a 3D image of the cell is not generated if the cell is determined to not have abnormal features and to not have BEC-like features.
3 more elements
A system for training an AI-based cell classifier, the system comprising:
- a memory that stores computer-executable instructions;
- and
- a processor that executes the computer-executable instructions and causes the processor to:
- operate an optical tomography system to generate representative 2D images of cells;
- designate a known identifier for each of a plurality of the cells using the representative 2D image of such cell and a known cell identification method, wherein the known identifier is i) abnormal or having abnormal features;
- ii) BEC or having BEC-like features, or iii) normal or non-BEC or not having abnormal features or BEC-like features;
- compute a plurality of cell feature measurements for each of the plurality of cells from the representative 2D image of such cell;
- perform a regression between the cell feature measurements and the known identifier of each cell to designate an abnormal 2D cell classification score and a BEC 2D cell classification score for each cell;
- designate a test identifier for each cell of i) having abnormal features;
- ii) having BEC-like features, or iii) not having abnormal features or BEC-like features based on the abnormal 2D cell classification score and the BEC 2D classification score the cell;
- compare the respective test identifiers to the known identifiers for each cell to calculate accuracy of the abnormal cell 2D classifier and the BEC 2D classifier;
- and
- generate a 3D image of the cell if the cell is determined to have abnormal features or to have BEC-like features, wherein a 3D image of the cell is not generated if the cell is determined to not have abnormal features and to not have BEC-like features.
10 more elements
Dependencies are inferred from the claim text.