Image recoggnition system for cross-contamination prevention

TWI940352BActive Publication Date: 2026-09-21LOCUS CELL CO LTD
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Patent Information

Application Number
TW114139766
Authority / Receiving Office
TW · TW
Patent Type
Patents
Current Assignee / Owner
Filing Date
2025-10-15
Publication Date
2026-09-21
Estimated Expiration
2045-10-14

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Abstract

An image recognition system for cross-contamination prevention includes an input module, a camera module, and an artificial intelligence module. The input module generates an instruction for the type of cells to be extracted. The camera module captures images of cell cryovials along the movement path of multiple culture containers and cryopreservation devices within an incubator, generating first image data for the placement of culture containers and second image data for the cell cryovials. The artificial intelligence module analyzes the cell types in the first image data and compares them with those in the second image data to determine if they match the extracted cell types specified in the cell extraction instruction, thus generating an erroneous extraction signal. An alarm module generates an alert notification based on the erroneous extraction signal.
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Claims

1. An image recognition system for cross-contamination prevention, comprising: an input module for generating an indication of extracted cell type; and a camera module disposed in at least one of an incubator and a cryopreservation device along a cryotube movement path, for capturing images of at least one of a plurality of culture containers in the incubator and a plurality of cell cryopreservation devices along the cryotube movement path, to generate first image data regarding the placement of one of the culture containers and second image data regarding the cell cryopreservation devices located along the cryotube movement path, wherein the cryoprotection device comprises liquid nitrogen; one person An AI module, connected to the input module and the camera module, pre-stores a culture container placement dataset. This dataset is used to analyze the cell type extracted from one of the culture containers in the first captured data, and compares one of the cell types from the extracted culture container in the first captured data and one of the cell types from the cell freezers in the second captured data with one of the cell types in the extracted cell type indicator to determine if a mistake has occurred and generate an erroneous extraction signal. An alarm module, connected to the AI ​​module, generates an alarm notification based on the erroneous extraction signal.

2. The image recognition system for cross-contamination prevention as claimed in claim 1, wherein the camera module further includes at least one first camera disposed on the inner surface of one of the doors of the incubator and configured corresponding to a plurality of culture racks inside the incubator, each of the culture containers including a container identification code, and the first image data including a current spatial distribution map of each container identification code inside the incubator.

3. The image recognition system for cross-contamination prevention as claimed in claim 2, wherein the culture container placement dataset contains a standard spatial distribution map of each of the container identification codes in the incubator, and the artificial intelligence module is used to compare the culture container placement dataset with the first image data to identify the missing container identification code in order to generate the cell category of the extracted culture container.

4. The image recognition system for cross-contamination prevention as described in claim 3, wherein the input module further includes a first input unit for generating a first extracted cell category indication, and when the AI ​​module compares the first extracted cell category in the first extracted cell category indication with the cell category of the extracted culture container, the AI ​​module generates a first erroneous extraction signal.

5. The image recognition system for cross-contamination prevention as described in claim 2, wherein the artificial intelligence module is used to compare the first captured data with the culture container placement data set to analyze different container identification codes to generate a first misplacement signal, and the warning module generates the alarm notification based on the first misplacement signal.

6. The image recognition system for cross-contamination prevention as claimed in claim 1 further includes a plurality of sensors disposed within the cryopreservation device, and each of the sensors is disposed below each of the cell cryovials for sensing the storage status of one of the cell cryovials.

7. The image recognition system for cross-contamination prevention as claimed in claim 6, wherein the camera module further includes a second camera disposed on the movement path of the cryopreservation device, each of the cell cryopreservation tubes includes a cryopreservation tube identification code, and the second image data includes the cryopreservation tube identification code.

8. The image recognition system for cross-contamination prevention as claimed in claim 7, wherein the artificial intelligence module further includes a cryotube placement dataset, the cryotube placement dataset including a standard spatial distribution map of a standard placement location of each of the cryotube identification codes in the cryopreservation device, the artificial intelligence module being used to generate a placement location of the cryotube based on the cryotube identification code of a placed cryotube in the second captured data and the storage state sensed by the sensor.

9. The image recognition system for cross-contamination prevention as described in claim 8, wherein the artificial intelligence module is used to compare the freezing pipe identification code of the placed freezing pipe with the placement location to the standard placement location of the freezing pipe identification code in the freezing pipe placement database, and when the placement location is different from the standard placement location, the artificial intelligence module generates a second incorrect placement signal, and the warning module generates the alarm notification based on the second incorrect placement signal.

Citation Information

Patent Citations

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  • Organism specimen imaging method and organism specimen imaging device

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  • Instructional point of care device for sample handling

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