Stem-Cell Image Screening for Presymptomatic Neurological Disease
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Solution Overview
Problem
Existing machine learning models for microscopic images of cells and tissues cannot predict whether a subject will develop an intractable neurological disease in the future, despite being able to determine the current state of cells and tissues.
Innovation Solution
An information processing device that acquires images of cells differentiated from pluripotent stem cells and uses a trained model to predict the onset of intractable neurological diseases based on convolutional neural networks (CNN) and ensemble learning, analyzing minute changes in cell structure and relative positional relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional machine learning models are used to analyze microscopic images of cells and tissues, then the current state of cells and tissues can be determined, but the ability to predict future disease onset is lost
Solution Approach 1:
The system performs preliminary analysis by differentiating pluripotent stem cells into disease-specific cell types and capturing images at an early stage before symptoms appear. This preliminary action enables prediction of future disease onset by analyzing cellular characteristics in the presymptomatic state, rather than waiting for manifest disease symptoms.
Solution Approach 2:
The system segments the diagnostic process into distinct stages: (1) differentiation of pluripotent stem cells into disease-specific cell types, (2) imaging of differentiated cells, (3) feature extraction from images, and (4) prediction modeling. This segmentation allows specialized analysis at each stage, improving both current state determination and future disease prediction capabilities.
2Loss of time
If early diagnosis of presymptomatic status is implemented, then early treatment becomes possible, but the complexity of the diagnostic system increases
Solution Approach 1:
Pluripotent stem cells serve as an intermediary medium that captures genetic and epigenetic information from the subject. By differentiating these stem cells into disease-specific cell types in vitro, the system creates a controllable intermediate state that reveals presymptomatic disease characteristics without requiring direct observation of subtle clinical symptoms in the living subject.
Solution Approach 2:
The system creates in vitro copies of disease-specific cell types from the subject's pluripotent stem cells. These copied cells replicate the genetic and cellular characteristics of the subject's potential disease state, allowing analysis and prediction without directly observing the subject's clinical condition. This copying approach simplifies the diagnostic process by working with cultured cells rather than complex clinical assessments.
Data Source
AI summary
An information processing device includes: an acquirer configured to acquire images obtained by imaging cells differentiated from pluripotent stem cells derived from a subject; and a predictor configured to input the images acquired by the acquirer to a model trained on data in which information indicating at least an intractable neurological disease is associated with an image obtained by imaging the cells of the intractable neurological disease differentiated from the pluripotent stem cells, and predict an onset of the intractable neurological disease of the subject based on output results of the model to which the images were input.