Fertility
AI patch could detect hidden hormone disruptions behind unexplained infertility

Even when standard clinical tests show normal hormone levels, men and women may have hidden problems in how their reproductive hormones are timed and coordinated, potentially affecting fertility, new research suggests.
The findings suggest reproductive health may depend not only on hormone levels in the bloodstream but also on the rhythm, timing and synchronisation of hormone changes across hours, days and the menstrual cycle.
Researchers said a wearable skin sensor patch, combined with artificial intelligence, could help detect endocrine dysfunction earlier and support more personalised fertility care.
Unexplained infertility affects about 15 to 30 per cent of couples and is diagnosed when standard investigations reveal no clear cause.
In men, current tests for infertility or hypogonadism, defined clinically as low testosterone, often include a single morning serum testosterone measurement.
In women, fertility assessment typically examines menstrual cycle characteristics and reproductive hormones such as luteinising hormone, follicle-stimulating hormone, oestradiol and progesterone.
However, reproductive hormones are not static markers. They are dynamic biological signals that rise and fall in regulated patterns throughout the day and across the menstrual cycle.
Testosterone, for example, follows a diurnal rhythm, meaning it changes across the day, while female reproductive hormones act through coordinated feedback loops involving the hypothalamic, pituitary and ovarian systems.
A single blood test may therefore miss clinically important disruption in hormonal timing.
In one study, Dr Tinatin Kutchukhidze, from the University of Oxford, examined 102 men in Georgia and the UK.
The participants were aged 22 to 38 and had normal morning total testosterone levels, measured at 12 to 35 nanomoles per litre, with or without infertility or symptoms of hypogonadism.
Hypogonadism is a condition in which the body produces too little testosterone or other sex hormones.
Kutchukhidze and colleagues used wearable AI-enabled skin sensor patches to measure testosterone levels every 15 minutes across four days.
The team found that men with symptoms had significantly disrupted testosterone rhythms, despite standard laboratory tests showing normal testosterone levels.
These previously undetected rhythm abnormalities were also associated with reduced sperm concentration and symptoms of androgen deficiency.
Androgens are hormones, including testosterone, that play an important role in reproductive health.
Kutchukhidze said: “For the first time, we have been able to track androgen patterns in real time across several days with a novel, non-invasive, continuous, AI-driven testosterone monitoring patch, compatible with Android and iPhone mobile devices.
“Previous research suggests that a normal morning testosterone level is sufficient to exclude clinically significant androgen deficiency. However, our findings challenge that assumption by demonstrating that men with normal serum testosterone may still exhibit marked disturbances in hormonal rhythmicity associated with reproductive dysfunction.”
According to the abstract, the study compared 54 men with infertility or hypogonadal symptoms with 48 age-matched healthy controls.
Mean morning serum testosterone did not differ significantly between symptomatic men and controls, at 22.4 ± 3.1 compared with 23.1 ± 3.5 nanomoles per litre.
Continuous AI-assisted monitoring, however, revealed significant differences in androgen dynamics.
Men with symptoms had lower diurnal amplitude than controls, at 5.2 ± 1.1 compared with 8.7 ± 1.4 nanomoles per litre.
The AI-derived rhythm indices predicted subclinical dysfunction with an area under the curve of 0.87, compared with 0.61 for static serum testosterone testing.
In diagnostic research, the area under the curve is used to assess how well a test distinguishes between groups, with higher values indicating stronger discrimination.
A second study by Kutchukhidze’s team examined female reproductive hormone rhythms.
The researchers developed an AI-driven metric called Endocrine Rhythm Integrity to assess whether reproductive hormones were changing in the correct pattern, at the correct time and in the correct relationship to one another across the menstrual cycle.
Endocrine refers to the hormone system, while endocrine dysfunction means hormones are not being produced or regulated in a typical way.
The team analysed data from 312 women aged 18 to 22 who had self-reported regular menstrual cycles.
Participants included fertile controls and women with unexplained infertility.
The researchers assessed key reproductive hormones during the luteal phase, including luteinising hormone, follicle-stimulating hormone, oestradiol and progesterone.
The luteal phase is the part of the menstrual cycle after ovulation. Ovulation is the release of an egg from the ovary.
They also incorporated physiological data such as basal body temperature, heart rate and sleep patterns.
Basal body temperature is the body’s resting temperature and can shift slightly around ovulation.
The study found that women with unexplained infertility had lower Endocrine Rhythm Integrity scores even when conventional hormone levels appeared normal.
Lower scores predicted infertility and were also associated with a higher incidence of implantation failure, when an embryo does not successfully attach to the womb lining.
Kutchukhidze said: “Our study reveals that a woman may have a seemingly healthy menstrual cycle and normal hormone levels but still experience hidden endocrine dysfunction that affects her ability to conceive.
“Rather than analysing hormone levels as isolated values, Endocrine Rhythm Integrity evaluates whether reproductive hormones are changing in the correct pattern, at the correct time and in the correct relationship to one another across the menstrual cycle.”
In the female study, mean cycle length did not differ significantly between fertile and infertile groups, at 28.9 ± 2.3 compared with 28.9 ± 2.5 days.
Endocrine Rhythm Integrity scores, however, were lower in the infertility group, at 0.61 ± 0.12 compared with 0.78 ± 0.10.
Disrupted endocrine rhythm integrity was observed in 64 per cent of infertile participants despite hormonally normal mid-luteal progesterone levels.
The metric independently predicted infertility status after adjustment for age, body mass index and anti-Müllerian hormone.
Anti-Müllerian hormone is made by reproductive tissues and is best known as a marker of ovarian reserve, meaning an estimate of the number of eggs remaining in the ovaries.
Receiver operating characteristic analysis indicated that Endocrine Rhythm Integrity identified infertility more effectively than cycle length or single-time-point progesterone assessment.
Lower Endocrine Rhythm Integrity scores were also associated with a higher incidence of implantation failure.
Kutchukhidze said: “Our AI-driven rhythm analyses were significantly better at identifying subclinical reproductive dysfunction than conventional testing, suggesting that both female and male endocrine disorders may not simply be disorders of hormone quantity, but rather disorders of hormonal timing, synchronisation and biological rhythm.”
The team will next assess whether the tool can reliably predict fertility outcomes across different reproductive conditions in larger and more diverse populations.
Wellness
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Fertility
AI could transform ovarian care through personalisation, study finds

AI could transform ovarian care by personalising cancer and fertility treatment, but more clinical validation is needed before routine use.
A systematic review and meta-analysis found AI models showed high diagnostic accuracy for ovarian cancer when combining data such as ultrasound scans and blood test results.
Across 81 studies, AI models correctly identified ovarian cancer in around nine out of 10 cases, with pooled rates of 89 to 94 per cent.
They were also highly accurate at ruling out ovarian cancer when it was not present, with specificity of 85 to 91 per cent.
The analysis also found that explainable AI tools could predict complete surgical cytoreduction in advanced ovarian cancer.
Complete surgical cytoreduction means removing all visible cancer during surgery, which can be an important goal in treatment planning.
The tools achieved a pooled AUC of 0.87. AUC is a measure of how well a model distinguishes between different outcomes, with higher scores showing stronger performance.
In reproductive medicine, AI algorithms helped physicians optimise ovarian stimulation protocols and predict follicular growth during IVF.
Ovarian stimulation is the use of hormones to encourage the ovaries to produce eggs, while follicles are the small sacs in the ovaries where eggs develop.
The review found AI could reliably model ovarian response in IVF with a pooled AUC of 0.81.
However, researchers said challenges remain in translating promising research findings into routine clinical practice.
They identified substantial variation across studies, driven by retrospective study designs, variable AI systems and a lack of standardised validation.
Only 22 per cent of analysed studies reported prospective, multicentre external validation, where models are tested forward in time across multiple healthcare settings.
The authors called for rigorous validation to help close the gap between research and routine clinical practice, alongside standardised methodological and reporting frameworks, smooth integration with clinical workflow and robust governance to support responsible and ethical AI use.
They concluded: “Artificial intelligence is a transformative force in the management of ovarian conditions.
“In gynaecologic oncology, AI enhances every phase of care, from early detection and accurate diagnosis to prognostic stratification and surgical planning.”
In reproductive medicine, AI personalises ovarian stimulation and refines the diagnosis of heterogenous endocrine disorders such as PCOS.
PCOS, or polycystic ovary syndrome, is a hormonal condition that can affect periods, skin, weight and fertility.
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