- Patent Number:
12253,516
- Appl. No:
18/431875
- Application Filed:
February 02, 2024
- Abstract:
A method for automated, artificial-intelligence-based egg identification using optical coherence tomography (OCT) includes positioning an OCT imaging system head in proximity to a biological sample containing an oocyte, wherein the OCT is operatively coupled to an artificial intelligence/machine learning system (AI/ML system) and an imaging system, wherein the imaging system includes a camera system, and a lighting system. The method includes creating at least one three-dimensional image of the oocyte using the OCT, AI/ML system, and imaging system. The method includes using the AI/ML system to analyze the three-dimensional image, wherein an analysis includes detection of a polar body's presence or absence based at least in part on planar views of the oocyte.
- Inventors:
Conceivable Life Sciences Inc. (New York, NY, US)
- Assignees:
Conceivable Life Sciences Inc. (New York, NY, US)
- Claim:
1. A method for automated, artificial-intelligence-based egg identification using optical coherence tomography (OCT), the method comprising: positioning an OCT imaging system head in proximity to a biological sample containing an oocyte, wherein: the OCT imaging system head is operatively coupled to an artificial intelligence/machine learning system (AI/ML system) and an imaging system, and the imaging system includes a camera system and a lighting system; creating a first three-dimensional image of the oocyte using the OCT imaging system head, the AI/ML system, and the imaging system; using the AI/ML system to analyze the first three-dimensional image, wherein analyzing the first three-dimensional image includes detection of a presence or an absence of a polar body based on planar views of the oocyte; and in response to identifying the presence of the polar body: denuding the oocyte from the biological sample; after denuding the oocyte, creating a second three-dimensional image of the oocyte using the OCT imaging system head, the AI/ML system, and the imaging system; and using the AI/ML system to analyze the second three-dimensional image, wherein analyzing the second three-dimensional image includes automatically locating a presence or an absence of a meiotic spindle.
- Claim:
2. The method of claim 1 wherein the oocyte is a plurality of oocytes.
- Claim:
3. The method of claim 1 wherein the OCT imaging system head is a polarized sensitive OCT imaging system head.
- Claim:
4. The method of claim 1 wherein the OCT imaging system head is placed above the biological sample.
- Claim:
5. The method of claim 1 wherein the OCT imaging system head is placed beneath the biological sample.
- Claim:
6. The method of claim 1 wherein the OCT imaging system head is placed to a side of the biological sample.
- Claim:
7. The method of claim 1 wherein the OCT imaging system head is movable to a plurality of locations relative to the biological sample from which locations images may be produced.
- Claim:
8. The method of claim 1 wherein the lighting system includes polarized lighting.
- Claim:
9. The method of claim 1 wherein the first three-dimensional image is an amalgamation of a plurality of images that are combined into the first three-dimensional image.
- Claim:
10. The method of claim 9 wherein the amalgamation of the plurality of images includes image components from the OCT imaging system head and at least one other microscopy system.
- Claim:
11. The method of claim 1 wherein the first three-dimensional image includes virtual elements that are simulated.
- Claim:
12. The method of claim 1 wherein the AI/ML system and the imaging system automatically assess the meiotic spindle to form a predictive algorithm to determine a probability of an egg adequately maturing.
- Claim:
13. The method of claim 1 wherein the AI/ML system and the imaging system automatically assess the meiotic spindle to form a predictive algorithm to determine a probability of whether an egg will mature with further incubation.
- Claim:
14. The method of claim 1 wherein the AI/ML system and the imaging system automatically define a best positioning of the oocyte for use during an injection.
- Claim:
15. The method of claim 1 , wherein the AI/ML system and the imaging system automatically assess membrane integrity.
- Claim:
16. The method of claim 1 wherein the second three-dimensional image includes virtual elements that are simulated.
- Claim:
17. The method of claim 1 further comprising making a maturity assessment of the oocyte based on the detection of the presence or the absence of the meiotic spindle.
- Claim:
18. The method of claim 1 further comprising using polarized lighting directed towards the biological sample containing the oocyte.
- Patent References Cited:
11494578 November 2022 Chian
2013/0337487 December 2013 Loewke
2014/0297199 October 2014 Osten
2015/0252328 September 2015 Woodruff
2017/0140535 May 2017 Hamamah
2020/0305967 October 2020 Getman
2022/0189640 June 2022 Wessels Wells
2022/0358655 November 2022 Wessels Wells
2023/0093989 March 2023 Mahajan
2023/0303959 September 2023 Blanchard
112401994 February 2021
WO-0004929 February 2000
WO-2016001754 January 2016
- Other References:
Zhu et al. “Study of Robotic System for Automated Oocyte Manipulation” (2017), IEEE, 2017 Intern'l Conf on Manipulation, Automation, and Robotics at Small Scales (MARSS). cited by applicant
Abdullah et al., “Automation in ART: Paving the Way for the Future of Infertility Treatment,” Published online Aug. 2022, Reproductive Sciences, vol. 30: 1006-1016. cited by applicant
Casciani et al., “Are we approaching automated assisted reproductive technology? Sperm analysis oocyte manipulation, and insemination,” Fertil Steril, vol. 2, No. 3: 189-203, 2021. cited by applicant
Trottmann, et al., “Ex vivo investigations on the potential of optical coherence tomography (OCT) as a diagnostic tool for reproductive medicine in a bovine model,” vol. 9, No. 1-2: 129-137, 2016. cited by applicant
Fan, et al., “Optimized Optical Coherence Tomography Imaging with Hough Transform-Based Fixed-Pattern Noise Reduction,” IEEE Access, vol. 6, 32087-32096, 2018. cited by applicant
Zhai, et al., “Automated Denudation of Oocytes,” Micromachines, 2022. cited by applicant
Targosz, et al., “Semantic segmentation of human oocyte images using deep neural networks,” BioMedical Engineering, 20:40, 2021. cited by applicant
- Primary Examiner:
Tsai, Tsung Yin
- Attorney, Agent or Firm:
Miller Johnson
- Accession Number:
edspgr.12253516
Processing Request
No Comments.