JP-7856727-B2 - System and method for analyzing multiwell plates

Inventors

• マルティン・ホラト
• ヴォルフガング・ミューラー
• トーマス・レンツ

Assignees

• エフ． ホフマン－ラ ロシュ アーゲー

Dates

Publication Date

20260511

Application Date

20241025

Priority Date

20231031

Claims (15)

1. The steps include using a machine learning model (200) to analyze an image of a sample in a multiwell plate (MWP) inserted into an analyzer ( 1000 ) and determining whether the plate insert matches the MWP, The steps include: in response to the machine learning model (200) determining a mismatch between the plate insert and the MWP, the analyzer generates an alert and notifies the user of the mismatch; In response to the machine learning model (200) determining that the plate insert matches the MWP, the machine learning model is used to analyze an image of the sample to determine whether the MWP is sealed with foil. The steps include: generating the alert in the analyzer in response to the machine learning model (200) determining that the MWP is not sealed with foil, and enabling the analyzer to perform analysis of the sample itself in response to the machine learning model (200) determining that the MWP is sealed with foil; Methods that include...
2. The method according to claim 1, wherein the machine learning model (200) is trained using a combination of real images and augmented images.
3. The method according to claim 1, wherein the machine learning model (200) is trained using a training dataset containing images of MWPs with at least two different numbers of wells.
4. The method according to claim 3, wherein the image of the MWP having at least two different numbers of wells includes an image with foil on the MWP and an image without foil.
5. The method according to claim 1, wherein the machine learning model (200) is trained using a training dataset containing images of both correct and incorrect combinations of MWP and foil.
6. The method according to claim 1, wherein the machine learning model (200) is trained using a training dataset containing images of MWPs with multiple filling volumes, multiple dyes, and multiple foil types.
7. The method according to claim 1, wherein the machine learning model (200) is trained using a training dataset containing images of MWP with multiple different filling patterns.
8. The method according to claim 1, wherein the machine learning model (200) is trained using a training dataset containing images of MWP with user errors.
9. The method according to claim 1, wherein the step of analyzing the sample image using the machine learning model (200) to determine whether there is the mismatch between the plate insert and the MWP includes calculating a confidence score, and when the confidence score falls below a threshold, the alert is generated.
10. The method according to claim 1, wherein the step of analyzing an image of the sample using the machine learning model (200) to determine whether the MWP is sealed with foil includes calculating a confidence score, and when the confidence score falls below a threshold, the alert is generated.
11. The method according to claim 1, further comprising the step of receiving a command from the user to continue the operation of the analyzer when any of the alerts are generated.
12. The method according to claim 1, wherein the machine learning model includes a plurality of machine learning submodels (210, 220), the first machine learning submodel (210) of the plurality of machine learning submodels being trained to determine whether the plate insert matches the MWP, and the second machine learning submodel (220) of the plurality of machine learning submodels being trained to determine whether the sample is sealed with the foil.
13. The method according to claim 12, wherein the first machine learning submodel (210) and the second machine learning submodel (220) are trained using different datasets.
14. The method according to claim 2, wherein the augmented image is generated using a plurality of augmentation techniques.
15. Memory (420) and A processor (410) coupled to the memory (420), Using a machine learning model (200), the image of the sample in a multiwell plate (MWP) inserted into the analyzer (1000) is analyzed to determine whether the plate insert matches the MWP. In response to the machine learning model (200) determining a mismatch between the plate insert and the MWP, the analyzer generates an alert and notifies the user of the mismatch. In response to the machine learning model (200) determining that the plate insert matches the MWP, the machine learning model is used to analyze the image of the sample to determine whether the MWP is sealed with foil. In response to the machine learning model (200) determining that the MWP is not sealed with foil, the analyzer generates the alert, and in response to the machine learning model (200) determining that the MWP is sealed with foil, the analyzer is enabled to perform analysis of the sample itself . A processor (410) is configured as follows: A system equipped with these features.

Description

Embodiments of this disclosure relate to analyzers in general, and more specifically, to the analysis of multiwell plates used in analyzers. In some cases, the foil on a test sample, such as a multiwell plate (MWP), may not be properly applied before passing the sample through an analyzer, such as a polymerase chain reaction (PCR) analyzer, or the wrong plate insert may be used for a given MWP. For example, a laboratory technician may not properly apply the foil to an MWP before running the sample through the analyzer. Furthermore, some analyzers may be configured to analyze two or more types of MWPs. For example, an MWP may be a 96 (96) or 384 (396) well plate. However, in some cases, a plate insert for a 96-well plate may be applied to a 384-well plate, and vice versa. If the foil or plate insert is not properly applied, the sample material may evaporate during the thermal cycle, in which case the temperature may reach 95°C. This may lead to the loss of measured sample results, and the analyzer itself may be contaminated with the sample. Cleaning of the analyzer is necessary because contaminants in the optical path may lead to inaccurate results. Furthermore, spills caused by sample movement within the analyzer can lead to cross-contamination and, consequently, erroneous results. Therefore, it is desirable to provide new systems and methods for determining whether the correct insert was used or whether the foil was properly applied to the sample before using the analyzer. Embodiments of this disclosure relate to analyzers in general, and more specifically, to the analysis of multiwell plates used in analyzers. In one embodiment, the method includes using a machine learning model to analyze a sample inserted into an analyzer and determine whether the plate insert matches a multiwell plate (MWP). The method further includes generating an alert in the analyzer to notify the user of the mismatch in response to the machine learning model determining a mismatch between the plate insert and the MWP. The method also includes using the machine learning model to analyze a sample to determine whether the MWP is foil-sealed in response to the machine learning model determining that the plate insert matches the MWP. The method further includes generating an alert in the analyzer in response to the machine learning model determining that the MWP is not foil-sealed, allowing the analyzer to perform that analysis of the sample if the MWP is foil-sealed. In some embodiments, machine learning models are trained using a combination of real and augmented images. In some embodiments, the machine learning model is trained using a training dataset containing images of MWPs with at least two different numbers of wells. In some embodiments, the image of the MWP having at least two different numbers of wells includes images with and without foil on the MWP. In some aspects, machine learning models are trained using a training dataset containing images of both correct and incorrect combinations of MWP and foil. In some embodiments, machine learning models are trained using a training dataset containing images of MWPs with multiple filling volumes, multiple dyes, and multiple foil types. In some embodiments, machine learning models are trained using a training dataset containing MWP images with multiple different filling patterns. In some embodiments, machine learning models are trained using a training dataset containing MWP images with user errors. In some embodiments, analyzing a sample to determine a mismatch between the plate insert and the MWP involves calculating a confidence score, and an alert is generated when the confidence score falls below a threshold. In some embodiments, analyzing a sample to determine whether the MWP is foil-sealed includes calculating a confidence score, and an alert is generated when the confidence score falls below a threshold. In some embodiments, the method also includes receiving instructions from the user to continue the analyzer's operation when any of the alerts are generated. In some embodiments, the machine learning model includes multiple machine learning submodels, where a first machine learning submodel is trained to determine whether the plate insert matches the MWP, and a second machine learning submodel is trained to determine whether the sample is sealed with foil. In some embodiments, the first and second machine learning submodels are trained using different datasets. In some aspects, augmented images are generated using multiple augmentation techniques. In another embodiment, the system includes memory and a processor coupled to the memory. The processor is configured to use a machine learning model to analyze a sample inserted into an analyzer and determine whether the plate insert matches a multiwell plate (MWP). The processor is further configured to generate an alert in the analyzer in response to a machine learning model mismatch between the plate insert and the MWP, notifyi