Fertility
Mira launches tool to predict menopause and monitor fertility
The tool measures hormone levels at home without the need for a blood test

The US Food and Drug Administration (FDA) has approved Mira’s new hormone tracker as an over-the-counter tool to predict menopause and evaluate fertility.
The Ovum Wand is an AI-powered device that provides accurate detection of the follicle-stimulating hormone (FSH) in urine to help monitor fertility and find possible causes of reproductive issues for women after 35.
From October, the product will be available to US customers. The tool aims to help women get an inside look into their full menstrual cycle, manage irregular cycles, PCOS, and hormone imbalances as well as monitor hormone levels for fertility treatments such as IVF and IUI.
Menopause has been a taboo subject for many generations. Studies have shown that one third of women do not research the topic before they experience menopause.
A survey of 1,039 American women aged 40-65 has found that 45 per cent did not know the distinction between perimenopause and menopause.
Mira, a mobile health platform for monitoring reproductive health, is hoping that its Ovum Wand will provide information to help women understand these life stages and address the symptoms early.
Tracking the FSH is essential for women in their 20s, 30s, or 40s because it directly impacts their ability to get pregnant. To predict approaching menopause or track changes in fertility, a person needs to test over several days for two or more cycles.
Additionally, FSH tracking could also help monitor the effectiveness of hormone replacement therapy (HRT) to make it more targeted and personalised.
Mira allows users to safely monitor their FSH levels at home without the need for a blood test. Data points are saved and analysed to give personalised health insights and the users can also share the information with their medical provider.
“The Mira Ovum Wand further expands our capabilities in at-home hormone testing,” says Sylvia Kang, Mira CEO. “The launch reinforces Mira’s goal to support women throughout their lives by helping them understand their hormones at any age, be it puberty, adulthood, or later years.”
She adds: “This knowledge grants the ability to make informed fertility decisions and eliminates the dread associated with the word ‘menopause’, so that women can go through this journey with confidence and awareness.”
The company offers a line of hormone test wands. Like its predecessors, the new Ovum Wand is compatible with the Mira’s analyser and uses machine learning to track changing hormone patterns over time. The analyser can be connected to Mira App to display clear numerical data along with personalised insights on the user’s reproductive health.
For more info, visit miracare.com.
Fertility
Gum disease may impair female fertility and egg quality – study
Cancer
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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