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EP-4739766-A2 - SYSTEMS AND METHODS FOR AUTOMATED ASSESSMENT OF ARTIFICIAL TISSUE

EP4739766A2EP 4739766 A2EP4739766 A2EP 4739766A2EP-4739766-A2

Abstract

Systems and methods are described herein for geometric encoding of tissue phenotype. At least in some aspects, the systems and methods comprise obtaining an image of an artificial tissue under a first set of conditions. The systems and methods further compromise extracting a first region from the image such that the first region circumscribes the artificial tissue within the image. The systems and methods further compromise extracting one or more geometric features from the first region, wherein the one or more geometric features provide a shape-based characterization of the artificial tissue. The system and methods further comprise generating a first tissue descriptor based on the one or more geometric features. The first tissue descriptor can encode a phenotype of the artificial tissue under the first set of conditions. Still further, in some aspects, the systems and method may comprise implementation of quality control (QC) of artificial tissue.

Inventors

  • BAYS, NATHAN
  • SCHRIVER, Brian

Assignees

  • Valo Health, Inc.

Dates

Publication Date
20260513
Application Date
20240702

Claims (20)

  1. 1. A method for geometric encoding of tissue phenotype, the method comprising: obtaining an image of an artificial tissue under a first set of conditions; extracting a first region from the image such that the first region circumscribes the artificial tissue within the image; extracting one or more geometric features from the first region, wherein the one or more geometric features provide a shape-based characterization of the artificial tissue; and generating a first tissue descriptor based on the one or more geometric features, wherein the first tissue descriptor encodes a phenotype of the artificial tissue under the first set of conditions.
  2. 2. The method of claim 1 further comprising: outputting the first tissue descriptor.
  3. 3. The method of claim 1 wherein the one or more geometric features comprise a plurality of tissue widths determined at a corresponding plurality of locations along a longitudinal axis of the first region, wherein each of the plurality of tissue widths is perpendicular to the longitudinal axis of the first region.
  4. 4. The method of claim 3 wherein the corresponding plurality of locations are evenly spaced along the longitudinal axis of the first region.
  5. 5. The method of claim 3 wherein the first tissue descriptor comprises an estimated smoothness of the artificial tissue determined from a variance of the plurality of tissue widths.
  6. 6. The method of claim 3 wherein the first tissue descriptor comprises an estimated volume of the artificial tissue determined from the plurality of tissue widths according to a slice volume model.
  7. 7. The method of claim 6 wherein the slice volume model estimates a volume of a slice of the artificial tissue at a location along the longitudinal axis based on an estimated cross-sectional area of the artificial tissue, wherein the estimated cross- sectional area is determined from a width of the artificial tissue at the location.
  8. 8. The method of claim 7 wherein the estimated cross-sectional area is determined according to a model parametrized by the width of the artificial tissue and a predetermined tissue elongation parameter.
  9. 9. The method of claim 8 wherein the predetermined tissue elongation parameter is in a range of from 1 to 5.
  10. 10. The method of claim 1 wherein the first tissue descriptor comprises an estimated area of the first region.
  11. 11. The method of claim 1 further comprising: obtaining a second tissue descriptor associated with the artificial tissue under a second set of conditions; and determining a phenotypic change in the artificial tissue based on a comparison of the first tissue descriptor and the second tissue descriptor.
  12. 12. The method of claim 11 further comprising: comparing the first tissue descriptor and the second tissue descriptor.
  13. 13. The method of claim 11 wherein the first set of conditions are reference conditions.
  14. 14. The method of claim 11 wherein the second set of conditions are perturbed conditions associated with a perturbation of the artificial tissue.
  15. 15. The method of claim 14 wherein the perturbation comprises one or more of: a drug treatment; a disease model; or a different cell line.
  16. 16. The method of claim 11 further comprising: comparing the phenotypic change to one or more predetermined phenotypic changes each of which associated with a corresponding effect; and determining an effect associated with the second set of conditions based on the comparing.
  17. 17. The method of claim 16 further comprising: outputting the effect.
  18. 18. The method of claim 16 wherein the effect comprises one or more of a mechanism of action or a toxicity.
  19. 19. The method of claim 1 wherein the first region is extracted from the image using a segmentation pipeline.
  20. 20. The method of claim 19 wherein the segmentation pipeline includes a plurality of sequential morphological operations.

Description

SYSTEMS AND METHODS FOR AUTOMATED ASSESSMENT OF ARTIFICIAL TISSUE RELATED APPLICATION This application claims the benefit of U.S. Provisional Application No. 63/525,451 (filed on July 7, 2023), which is incorporated by reference herein. BACKGROUND Quality control (QC) of an artificial, or engineered, tissue can be used during maturation of the artificial tissue to track the development of the artificial tissue determine whether to include or exclude the artificial tissue from further analysis. Existing QC approaches typically rely on manual inspection of the artificial tissue during the maturation process. Such manual inspection leads to user-induced bias which can result in high variability in the quality of the artificial tissue generated. This can have a detrimental impact on any downstream tasks which rely on such artificial tissue, such as drug discovery and development tasks. Moreover, manual QC inspection can inhibit the large scale production of artificial tissue. In addition, even when artificial tissue passes QC inspection, it is often difficult to identify or explain variability arising in experimental outcomes when artificial tissue is used within experimental settings or downstream tasks. Therefore, there is a need for new approaches to assessing the quality of artificial tissue (e.g., during maturation) and encoding the condition dependent phenotype of artificial tissue. SUMMARY OF DISCLOSURE According to an aspect of the present disclosure there is provided a method for geometric encoding of tissue phenotype. The method comprises obtaining an image of an artificial tissue under a first set of condition, extracting a first region from the image such that the first region circumscribes the artificial tissue within the image, and extracting one or more geometric features from the first region. The one or more geometric features provide a shape-based characterization of the artificial tissue. The method further comprises generating a first tissue descriptor based on the one or more geometric features, wherein the first tissue descriptor encodes a phenotype of the artificial tissue under the first set of conditions. According to a further aspect of the present disclosure there is provided a system for quality control (QC) of artificial tissue. The system comprises a bioreactor comprising a device configured for growing tissue, and a sensor assembly configured to obtain one or more images of a tissue within the device. The system further comprises a QC unit communicatively coupled to the bioreactor. The QC unit comprising one or more processors configured to obtain, from the bioreactor at a first predetermined time point, a first image of the tissue within the device and extract one or more image segments from the first image, wherein each of the one or more image segments comprise a region of interest of the tissue within the first image. The one or more processors of the QC unit are further configured to determine one or more tissue parameters from the one or more image segments, wherein the one or more tissue parameters are indicative of a physiological state of the tissue at the first predetermined time point. The one or more processors of the QC unit are further configured to output a QC report based on the one or more tissue parameters. According to an additional aspect of the present disclosure there is provided a system for engineered tissue attachment grading. The system comprises a multi-task learning (MTL) network comprising an input convolutional neural network configured to receive an input image, a plurality of independent output networks each configured to estimate an attachment score associated with the input image, and a shared network coupled between the input convolutional neural network and each of the plurality of independent output networks. The system further comprises a control unit communicatively coupled to the MTL network and comprising one or more processors configured to obtain an image of an engineered tissue grown within a device comprising a tissue scaffold for attachment to the engineered tissue and extract a first region from the image, wherein the first region of the image includes a first portion of the engineered tissue and a portion of the tissue scaffold. The one or more processors of the control unit are further configured to determine, using the MTL network, a plurality of attachment scores based on the first region and determine an attachment grading for the first region based on the plurality of attachment scores, wherein the attachment grading is indicative of a degree of attachment of the first portion of the engineered tissue to an attachment site of the device. The one or more processors of the control unit are further configured to output the attachment grading. In accordance with the above, and with the disclosure herein, the present disclosure includes applying certain features or aspects with or by use of, a particular machine, e.g., a bioreactor