Automated Cell Treatment Using Learned Model Laser Irradiation

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Solution Overview

Problem

Current methods for differentiating pluripotent cells, such as iPS cells and ES cells, into intended cells are labor-intensive and vary in quality due to manual removal of unintended cells, which requires skilled operation and is time-consuming.

Innovation Solution

A cell treatment apparatus equipped with an observation unit, laser irradiation unit, and control unit that uses a learned model to detect target cells through image data, setting a laser irradiation region, and applying laser treatment to the cells, along with a learning apparatus for generating and a proposal apparatus for selecting precise learned models for cell detection.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual removal of unintended cells is performed, then cell treatment can be carried out, but time and labor increase significantly

Engineering Contradiction:

Improvecell treatment efficiencyVSAvoidtime required for cell removal

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical cell removal with an automated laser irradiation system controlled by a learned model. The detection section captures cell images, the learned model identifies target cells, and the laser irradiation unit automatically treats them, eliminating the need for manual intervention and significantly reducing treatment time.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service cell treatment by automatically detecting target cells using the learned model and applying laser irradiation without human intervention. The control unit autonomously manages the entire process from image capture to laser treatment, allowing the system to serve itself.

Inventive Principle:

Principle #25Self-service

2Reliability

If manual cell removal is performed, then unintended cells can be removed, but quality varies depending on operator skill level

Engineering Contradiction:

Improveconsistency of cell treatment qualityVSAvoiddependence on operator skill

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The patent replaces skill-dependent manual operation with an automated learned model-based system. The detection section and learned model provide objective, consistent cell identification and targeting, eliminating variability introduced by different operator skill levels and ensuring reliable treatment quality.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system incorporates feedback through the learned model that continuously processes image data from the detection section to accurately identify and target cells. This feedback loop ensures consistent and reliable cell treatment regardless of operator skill level.

Inventive Principle:

Principle #23Feedback

3Reliability

If automated detection using learned model is implemented, then treatment consistency improves, but device complexity increases

Engineering Contradiction:

Improveconsistency of cell treatment qualityVSAvoidcomplexity of detection and control system

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent integrates multiple functions into a unified system where the detection section captures images, the learned model processes and identifies cells, and the laser irradiation unit executes treatment. This multi-functional integration, while increasing complexity, achieves reliable and consistent cell treatment automation.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

This approach enables efficient and consistent treatment of target cells, reducing treatment costs and variations in quality by automating the detection and removal of unintended cells using machine learning-based models.

Implementation Method 1

the laser irradiation unit is capable of applying a laser to an inside of the cell culture tool

Methodology Applied

Scientific EffectLaser: Laser

Implementation Method 2

applies a laser emitted from the laser irradiation unit to the laser irradiation region in the cell culture tool to treat the target cell

Methodology Applied

Scientific EffectPhotothermal conversion:

Data Source

PatentUS20230242862A1Cell treatment device, learning device, and learned model proposal device

Publication Date: 2023.08.03 KATAOKA

AI summary

The cell treatment apparatus of the present disclosure is a cell treatment apparatus including: an observation unit; a laser emitter; and a controller. The controller includes at least one processor that is configured to detect, using image data that includes the cell captured by an observation unit and a learned model capable of detecting a target cell or a non-target cell, a target cell or a non-target cell in the image data; set a region where the target cell is present or a region where the non-target cell is not present as a laser irradiation region to be subjected to laser irradiation by the laser emitter, and apply a laser emitted from the laser emitter to the laser irradiation region in the cell culture tool to treat the target cell.