View source for 20240054640. System, Method, and Computer Program Product for Classification of Diseases Based on Expansion Microscopic Images simplified abstract (CARNEGIE MELLON UNIVERSITY)

=System, Method, and Computer Program Product for Classification of Diseases Based on Expansion Microscopic Images=


==Organization Name==

[[:Category:CARNEGIE MELLON UNIVERSITY|CARNEGIE MELLON UNIVERSITY]]


[[Category:CARNEGIE MELLON UNIVERSITY]]

==Inventor(s)==

[[:Category:Yongxin Zhao of Sewickley PA (US)|Yongxin Zhao of Sewickley PA (US)]][[Category:Yongxin Zhao of Sewickley PA (US)]]

[[:Category:Christopher Byungjun Ahn of Loveland OH (US)|Christopher Byungjun Ahn of Loveland OH (US)]][[Category:Christopher Byungjun Ahn of Loveland OH (US)]]

==System, Method, and Computer Program Product for Classification of Diseases Based on Expansion Microscopic Images - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240054640 titled 'System, Method, and Computer Program Product for Classification of Diseases Based on Expansion Microscopic Images

==Simplified Explanation==

The method described in the patent application involves classifying diseases using image data and machine learning. 

* Image data associated with an image is received at a first resolution.
* The image is processed, which may include removing background, deconstructing into layers, and segmenting into single-cell images.
* Single-cell images are processed by applying a filter to decrease resolution to a second resolution and assigning a label.
* A machine learning model is trained to predict the classification of single-cell images.
* The trained model can be used to predict treatment outcomes.

**Potential Applications:**
- Medical diagnosis and treatment prediction
- Disease classification in pathology and radiology

**Problems Solved:**
- Efficient and accurate disease classification
- Predicting treatment outcomes based on image data

**Benefits:**
- Improved accuracy in disease classification
- Personalized treatment predictions based on image analysis


==Original Abstract Submitted==

provided is a method for classification of diseases including receiving image data associated with an image at a first resolution. the image may be processed, for example by removing a background from the image, deconstructing the image into separate layers, and segmenting the image to define a plurality of single-cell images. a single-cell image may be processed, for example, by applying a filter to the single-cell image to decrease a resolution of the single-cell image as compared to the first resolution, to a second resolution. a label may be assigned to the single-cell image. a machine learning model is trained to predict a classification of the single-cell image based on inputting a plurality of single-cell images into the model. the trained machine learning model may be used to predict the outcome of a treatment. systems and computer program products are also provided.



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