Femtech: technology enhancing women’s health

Topic: Femtech: technology enhancing women’s health

Femtech — technologies specifically designed to positively impact women’s health — has the potential to radically improve health outcomes for women globally. Incorporating artificial intelligence, machine learning, software, diagnostics, products and services, femtech is an emerging industry that is already shifting medical practice.

Despite working for 50% of the world’s population, femtech is an underserved industry, attracting only 10% of investment funding.

AI can have a profound impact on women’s health outcomes, in areas including ovarian, breast and cervical cancers, assistive reproduction, endometriosis and polycystic ovarian syndrome.

Adelaide-based company Presagen has demonstrated just what AI has to offer when it comes to women’s health. The company’s software-based scalable AI technology is designed to be delivered globally at low cost, to maximise reach to women across the world.

In the field of IVF, Presagen’s Life Whisperer technology uses AI to analyse images of embryos, selecting the healthiest ones to improve pregnancy outcomes for couples struggling with fertility. Built via machine learning using thousands of images, the product assesses two-dimensional or microscope images of embryos and provides a confidence score on the likelihood that a pregnancy will result. Life Whisperer was awarded Frost & Sullivan’s 2019 Global New Product Innovation Award.

Presagen’s AI Open Projects is an online platform that connects clinics from around the world to safely crowdsource globally diverse datasets needed to build AI medical products that are robust, scalable and unbiased. The financial value that is created from economies of scale can then be shared amongst contributing clinics via royalties. This enables clinics, particularly small and medium-sized clinics, to benefit and unlock the value of their data with AI without wearing the technical or commercial cost and risk.

“To build AI products that solve global problems, you need a global dataset which is diverse and represents different types of people and clinical settings. This is challenging because data privacy laws can prevent private medical data leaving the country of origin,” Presagen CEO Dr Michelle Perugini said. “As a result, many focus on building AI from local datasets that are not diverse, creating AI that will be biased and simply will not scale.”

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