Patients suffering from infertility may seek assisted conception treatment. In a full treatment cycle, the sperm and eggs (gametes) from the intended parents will be looked after by skilled laboratory staff (embryologists) who will rely on manual laboratory processes to achieve fertilization. The most commonly employed method to achieve fertilization is intracytoplasmic sperm injection (ICSI), a treatment that involves the injection of a single sperm cell directly inside an egg. The embryos so obtained are then be maintained in an appropriate environment (incubator) for several days before deciding whether they can be immediately used to attempt to establish a pregnancy (embryo transfer), or should be frozen in preparation for a future treatment (cryopreservation).

The laboratory steps required to complete a full assisted conception treatment (from sperm and egg retrieval, to fertilization, and then to embryo transfer and/or cryopreservation) are often manual and time-consuming, and thus the success of the treatment is highly dependent on the skill of individual staff and outcomes can be affected by fatigue, stress, and workload.

The combination of robotics and artificial intelligence (AI) has the potential to provide improvements to, and standardize, the fertility laboratory, but such integration has not been achieved routinely. Other medical fields, such as regenerative medicine, have long benefited from the implementation of robotic solutions; however, modern automation has yet to find its way into the fertility laboratory.

The goal for Conceivable Life Sciences (the study sponsor) is the delivery of a suite of solutions that, collectively, will allow a fully autonomous ICSI cycle to take place (from sperm/egg preparation, to sperm injection, to embryo culture and cryopreservation) in an effort to reduce costs, assist laboratory staff, and possibly, improve outcomes. The purpose of this study is to deliver a core aspect of this project: the digital control and individual automation of all key steps of a complete laboratory workflow. The data generated in this study will help the future development of these automated systems.

Patients undergoing an ICSI treatment may be recruited in this study. Their gametes (sperm and/or eggs) may be distributed across two groups (test and control) and undergo one or more elements of an automated full assisted conception workflow (for the test group) or a standard assisted conception workflow (for the control group). Robotic automation may include any of the following steps: robotic sperm preparation, robotic oocyte selection and denudation, robotic sperm injection, automated embryo culture, and automated cryopreservation. The resulting embryos may be employed for embryo transfer.

The main hypothesis of this study is that the use of robotic assistants as part of a full assisted conception cycle may improve laboratory workflows without reducing the treatment success rates.

Inclusion Criteria:

• Medical indication to perform ICSI.
• Informed consent signed by the patients before treatment.
• Medical indication for the use of autologous or donor oocytes.
• Medical indication for the use of autologous or donor sperm.
• Motile sperm.

Exclusion Criteria:

• Recurrent pregnancy loss.
• Previous history of total fertilization failure.
• Surgical sperm retrieval.
• Severe male factor infertility.
• Known semen liquefaction problems.
• Any other case abnormalities that could potentially reduce success rates according to the criteria of the senior embryologist in charge.

Individual participant data that underlie the results reported in this article, after de-identification (text, tables, figures, and appendices).

Data sharing is subject to ethical, legal, and privacy considerations. We are committed to following established guidelines and best practices for data sharing.

This plan has been updated to provide more detailed information on access criteria.

Beginning 9 months and ending 36 months following article publication.

Researchers who provide a methodologically sound proposal, to achieve aims in the approved proposal.

Proposals may be submitted up to 36 months following article publication. Proposals should be directed to Jacques@Conceivable.life; to gain access, data requestors will need to sign a data access agreement.