Optical coherence tomography in an intelligent automated in vitro fertilization and intracytoplasmic sperm injection platform

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.

2. The method of claim 1 wherein the oocyte is a plurality of oocytes.

3. The method of claim 1 wherein the OCT imaging system head is a polarized sensitive OCT imaging system head.

4. The method of claim 1 wherein the OCT imaging system head is placed above the biological sample.

5. The method of claim 1 wherein the OCT imaging system head is placed beneath the biological sample.

6. The method of claim 1 wherein the OCT imaging system head is placed to a side of the biological sample.

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.

8. The method of claim 1 wherein the lighting system includes polarized lighting.

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.

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.

11. The method of claim 1 wherein the first three-dimensional image includes virtual elements that are simulated.

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.

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.

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.

15. The method of claim 1 , wherein the AI/ML system and the imaging system automatically assess membrane integrity.

16. The method of claim 1 wherein the second three-dimensional image includes virtual elements that are simulated.

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.

18. The method of claim 1 further comprising using polarized lighting directed towards the biological sample containing the oocyte.