Diagnosis
Simplifying complex women’s health issues with AI

By Laurent van Lerberghe, managing partner at KELES
I often ask myself if technology is the solution to solving complexities in women’s health.
Women’s health has been a Cinderella area of research for some time, but increasing investment and technology innovation is changing the game, creating a vibrant ecosystem of companies offering exciting and innovative tech – are we finally getting to the heart of these complex problems by utilising the power of AI and data science to address gaps in health care?
Detecting endometriosis quicker with pain-free saliva test – Ziwig
Companies like Ziwig are at the forefront of tackling the long-standing challenge of diagnosing serious women’s health issues like endometriosis, a condition that often takes years to identify, leaving millions of women suffering without answers.
Endometriosis affects 1 in 10 women, impacting fertility in 30-50 per cent of cases, and takes on average 7-8 years to receive a diagnosis.
By creating a technology that detects biomarkers of the disease in saliva, Ziwig aims to provide earlier and accurate detection, and reduces the need for invasive procedures such as laparoscopy.
Ziwig’s technology extracts and analyses salivary RNA, enabling precise detection of diseases such as endometriosis, women’s pathologies, cancers and neurodegenerative diseases.
The sample is processed through a robotic platform housed in the Brain Institute in Pitié-Salpêtrière Hospital, France, and analysed with the latest next-generation sequencing techniques.
Once the RNA is extracted from saliva samples, Ziwig employs advanced artificial intelligence (AI) techniques to detect and differentiate the complex biomarker patterns associated with endometriosis, improving both the accuracy and reliability of the diagnostic process.
Red, Amber or Green? Guiding breast cancer diagnosis with AI – Therapixel

Laurent van Lerberghe
AI is incredibly good at packaging complex data in simple categories, but how is that useful in breast cancer diagnosis?
Therapixel’s AI product uses deep learning and neural network algorithms to guide decisions about breast cancer diagnosis.
1 in 8 women will be diagnosed with breast cancer during their lifetime, with annual mammograms recommended for women over the age of 40.
Therapixel’s technology, known as mammoscreen, validates a clinician’s suspicions by detecting potential lesions and characterising their malignancy on a simple colour graded scale of 1 to 10.
- 1-4, green = are very unlikely to contain cancers
- 5-6, orange = rely on radiologists expertise
- 7-10, red = are very likely to be malignant
Therapixel’s AI platform can process the image and deliver a report to the radiologist in under five minutes, significantly speeding up the diagnostic process compared to the traditional analogue methods.
It can also be used to compare images over time, helping to distinguish between stable and evolving features, which may indicate the development or progression of cancer.
This technology helps clinicians detect an average of 50 per cent of breast cancers a year earlier than traditional methods.
AI’s potential to give women’s health the diagnostic tools it deserves
Thanks to the innovative work of companies like Ziwig and Therapixel, new solutions combining AI and data science are addressing two of the most important women’s health issues, leading to better patient outcomes.
These are not potential technologies of the future; they already exist, and the impact will be delivered when they can effectively scale and the technology is more accessible.
Women’s health, like many other areas of health, is complicated, but AI tools hold immense potential to make diagnoses easier and provide more treatment options, rectifying long-standing disparities in the healthcare system.
When developed and applied responsibly, AI can significantly accelerate progress and act as a catalyst for change.
To fully realise this potential, it is imperative to ensure that AI systems are trained on inclusive data sets and developed by teams that actively involve women at every stage of the process.
That way, we can create a more equitable health system for all.
Menopause
Menopause frequently missing from electronic health records – study

Menopause is often absent from women’s electronic health records, a study of nearly 396,000 women has found.
Researchers found menopause appeared almost seven times more often in participant surveys than in electronic health records (EHRs).
The findings suggest important reproductive health information, including age at menopause, may often be missing from health records used for research.
Audrey Hendricks, associate professor of bioinformatics at CU Anschutz and the study’s principal investigator, said: “Ultimately, we cannot study what we do not measure. We cannot treat what we do not know.
“Menopause has enormous implications for women’s health, but if we don’t consistently capture when menopause occurs and other important reproductive health information, we limit our ability to understand how this transition affects disease risk and health outcomes.”
Researchers at the University of Colorado Anschutz analysed data from women taking part in the National Institutes of Health’s All of Us Research Program.
They compared menopause information reported by participants in surveys with menopause diagnoses recorded in their electronic health records.
Around 193,000 menopause observations were identified in survey data, compared with approximately 28,000 diagnoses in EHR data.
Menopause was documented in electronic health records for only about 7 per cent of women in the dataset.
Nearly all participants with a menopause diagnosis recorded in their EHR also reported menopause in survey data. However, substantially fewer women had menopause documented in their health records.
Other important information was also frequently unavailable, including age at menopause, which researchers may use when examining links between menopause and chronic disease risk.
Menopause is a physiological transition that can affect cardiometabolic health and many other aspects of women’s health.
Researchers said relatively little is known about how factors including the timing and type of menopause influence health outcomes across diverse populations.
Large-scale programmes such as All of Us combine participant surveys, electronic health records and genomic data, but menopause-related research depends on relevant reproductive health information being available.
Missing menopause information can make it harder to investigate how the transition relates to health and disease.
The findings may also help researchers using All of Us data define menopause-related study populations, design studies and estimate how many participants are needed.
Hendricks said: “We have an enormous opportunity to use large-scale datasets to understand women’s health across the menopause transition and to identify who may be at greater risk for disease.
“But we need to make sure that the information researchers need is actually being collected.
“We must do a better job of capturing women’s health information, including reproductive health and measures related to menopause.”
Researchers said more complete and consistent collection of menopause and reproductive health information could help future studies examine factors such as age at menopause and their relationship with disease risk and health outcomes.
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