Bringing AI to the fertility consultation room

Artificial intelligence (AI) applications have been emerging in different areas of assisted reproductive technology (ART). Image interpretation, for example, is potentially the most advanced area of AI in this field. Most patients have heard of time-lapse imaging, which monitors embryo development and assists in embryo selection. However, there is scope to bring AI outside the IVF lab and introduce it into every aspect of the patient journey. 

There is significant uncertainty in this patient journey. So much so that when patients begin fertility treatment, they never know which road they’ll take, which hurdles will come their way, or which destination they will get to. This is because patients can present with a multitude of clinical presentations, and healthcare professionals often have different treatment approaches. This results in inter-user variability in ART provision, poor outcomes and it is confusing for patients. AI can be used to reduce this variability, by learning from vast amounts of clinical data. Ultimately, automating and streamlining clinical pathways can assist clinicians in recommending treatment that is more likely to result in live birth, reduce overhead costs to fertility clinics and increase accessibility to care.

AI-driven patient pathways

Bringing AI to the consultation room opens the possibility of creating clear patient pathways. For example, pre-treatment assessment data processed by AI can be useful to assist clinicians with triaging patients who might need fertility treatment sooner, screen for potential problems or factors that can affect reproductive health and even be used for diagnosis of specific conditions like endometriosis, polycystic ovarian syndrome or reduced ovarian reserve.

AI can also be used to predict outcomes of fertility treatment. For example, Gil et al. looked at utilizing various AI networks to analyse the association between environmental factors and/or lifestyle habits in its possible effects on semen quality. In the context of patient undergoing fertility treatment, in a different study by ShuJie Liao et al, researchers reviewed 95 868 medical records and created a dynamic grading system that considered seven indicators age, body mass index, follicle-stimulating hormone level, antral follicle count, anti-Mullerian hormone level, number of oocytes, and endometrial thickness to predict outcomes with treatment.

Different patients respond to fertility drugs and treatment protocols differently. Personalized treatment has therefore significant potential to improve the outcomes that matter to patients. Artificial intelligence and machine learning can be used to automate the complex task of analysing all individual factors, compare against a benchmark and make personalised treatment protocol recommendations.

Enhanced Fertility Doctor’s Portal

The Enhanced Fertility team has been developing a new digital platform to bring AI to the consultation room. This platform allows clinicians to manage pre-treatment assessment remotely. 

• Clinicians request the tests and assessments their patients need, including blood tests, sperm test, serologies, genetic tests, scans and clinical history. 
• Clinical data is processed by the platform and clinicians access the Fertility Report for AI-driven clinical decision-making. 
• Machine learning and predictive analytics create clear patient pathways and improve patient outcomes.

Andreia Trigo  | Founder & CEO Enhanced Fertility