A new artificial intelligence-powered technology is fundamentally changing outcomes for millions of men diagnosed with azoospermia—a condition where sperm counts are so low they are considered absent—offering couples who have endured years of infertility another pathway to biological parenthood.
The Star (Sperm Track and Recovery) system, developed by Columbia University, uses machine learning to identify and isolate the few hidden sperm cells that men with this condition can produce. Male infertility contributes to up to 50% of all infertility cases, and about 1% of all men experience azoospermia, meaning potentially millions of men worldwide have been told they have no viable path to fatherhood using their own genetic material.
The first baby born using the Star system arrived at the end of last year, marking the culmination of five years of development. Since then, the technology has been deployed regularly at Columbia University Fertility Center, with a waiting list that has grown to hundreds of patients from around the world. Based on the latest 175 patients to use the technology, the system is finding sperm in just under 30% of cases—individuals who had previously been told they had no chance of conceiving with their own sperm.
Infertility affects millions of people worldwide, with around one in every six people of reproductive age experiencing problems getting pregnant at least once in their lifetime. For men facing azoospermia, the psychological and emotional toll is profound. Many are told they have virtually no chance of biological parenthood, a diagnosis that carries deep personal significance.
The Technology and Its Breakthrough Potential
Zev Williams, director of Columbia University Fertility Center, explained the system's remarkable capability: Star can find 40 times more sperm than a manual search by a trained human technician. The technology achieves what Williams described as a sensitivity rating of 100%, meaning it has the ability to find a single sperm in a sample if one is present.
The innovation emerged from an unexpected source. In 2020, Williams read about how artificial intelligence is used to analyze overwhelming amounts of astronomical data to identify new stars. Modern telescopes produce data so voluminous that human astronomers cannot manually process it, but machine learning algorithms accomplish the task in minutes. Williams recognized the parallel: "The picture of the sky was very reminiscent of what we're looking for, and what we see in men who are told they have no sperm."
The Star system uses microfluid chips—glass or polymer etched with channels as thin as a human hair—through which sperm samples flow. A high-powered imager scans the sample at 300 images per second, and a machine learning algorithm detects any sperm cells in real time. Williams explained the challenge: "Most of what we're seeing is just debris and fragments. It's not like it's an empty liquid. And you're trying to find that really rare sperm in a sea of all this other debris and cell fragments."
Once identified, a robotic system extracts the sperm cell or cells within milliseconds of discovery. Williams described the process: "The robotics on the microfluid chip sorts out that tiny little part of the fluid that has the sperm in it. You end up with a tube filled with the seminal fluid, but without any sperm in it, and a tiny droplet that has the sperm in it."
Real Outcomes: The Human Story
For Samuel and Penelope—names changed to protect privacy—the technology offered what had seemed impossible. Samuel was diagnosed with Klinefelter syndrome, a genetic condition affecting males born with an extra X chromosome, often not diagnosed until adulthood. Most people with Klinefelter syndrome produce little or no sperm in their ejaculate. Samuel had been told he had only a 20% chance of having a biological child.
After an agonizing two and a half years of trying, Penelope received a call in early November 2025: she was pregnant. Penelope recalled the emotional weight of the moment: "His face was just a wave of emotion. He cried… just to finally get to that point, because it took so much effort, time and research. And the fact that we only had one embryo, and it worked, we were just over the Moon."
Samuel described the fear he had carried: "I was scared. I thought that I wasn't going to be able to have my own kid, which is a really big part of my life. And that was a big slap in the face."
Samuel's case presented a unique challenge. With Klinefelter syndrome, there is no sperm in the ejaculate, so urologists needed to extract tissue directly from the testicle. Samuel underwent nine months of hormone therapy in preparation for a successful testicular extraction surgery at Cornell Medical Center. Specialists at Cornell could not find any sperm with the human eye, so the sample was sent to Williams' team at Columbia for analysis using the Star system.
The timing was critical. Penelope was undergoing her own egg retrieval procedure simultaneously, and fresh sperm samples offer the best chance of fertilization. Star isolated eight sperm from Samuel's sample, which were injected into Penelope's eggs. One developed into a full blastocyst, an advanced stage of embryo development. Their baby, likely to be the first boy born as a result of Star, is due at the end of July 2026. Penelope said, "It's starting to feel really real now, especially because I'm feeling movement. We had our anatomy scan and everything is just looking so great."
Zev Williams reflected on the broader significance: "Everyone was just jumping up and down with joy. There are so few things where the reward for all the effort that was put into it is something as wonderful and special as this. Now there's a baby girl and hopefully, God willing, many, many more."
Broader Applications and Ongoing Caution
AI is being deployed across multiple dimensions of fertility treatment beyond sperm detection. In ovarian stimulation—an essential process in IVF that helps the ovaries produce multiple eggs—machine learning is enabling more personalized dosages of the hormone gonadotropin. Deep learning tools are also assisting with more accurate selection of viable gametes and embryos.
However, experts emphasize the need for caution and further research. Siobhan Quenby, professor of obstetrics at The University of Warwick in the UK, acknowledged both the promise and the risks: "Couples who have long fertility journeys can become desperate to conceive and are vulnerable to being sold expensive treatments of unproven value. It is very exciting that advanced imaging, engineering and AI have been combined to develop a new solution for severe male factor subfertility. One successful pregnancy is an important start. However, further research on more patients is needed before the value of this new treatment can be fully assessed."
Experts agree that more large-scale clinical trials are needed to assess long-term outcomes. There are also concerns about how to handle sensitive medical data, confidentiality, and disputes around accountability and ownership. The field must also guard against overpromising outcomes, a concern that can particularly affect vulnerable couples desperate for solutions.
For Samuel, the prospect of future family expansion through this technology offers hope where there was none before. "Of course, now we're being greedy and we want another kid hopefully in the future, but this is something we're going to have to go through again because we don't have anything in reserve besides eggs," he said. "And they now have hope, where there previously was none."
Why This Matters:
This technology addresses a profound gap in reproductive medicine that has left millions of men and couples without viable options. Male infertility, which contributes to half of all infertility cases, has historically offered fewer treatment pathways than female infertility. The Star system's ability to locate hidden sperm in previously hopeless cases represents a significant expansion of reproductive autonomy and choice for affected individuals. However, the technology's emergence also raises critical questions about equitable access, informed consent, and the need for robust regulatory frameworks. As AI-powered fertility treatments proliferate, ensuring that vulnerable populations—couples who have endured years of emotional and financial strain—are protected from overpromising or unproven interventions becomes essential. Large-scale clinical trials, transparent data governance, and clear accountability structures must accompany technological innovation to ensure that advances in fertility medicine serve all populations equitably.