Fertility
IVF2.0 explores the potential of the Internet of Things in IVF laboratories
Paper shows how IoT devices may allow labs to optimise processes, personalise treatment, and boost success rates

In a new paper published in Reproductive BioMedicine Online (RBMO), the IVF2.0 team, with first author Giles Palmer, examines the immense potential of integrating Internet of Things (IoT) technologies in IVF laboratories.
IVF2.O is one of the leading AI companies in the field of assisted reproduction with bespoke AI tools for assisting embryologists to choose the best sperm, oocytes and embryos in IVF treatment.
The UK company, with links to computer scientists in Mexico, has a strong research base and growing global customer list.
Palmer, a leading embryologist and director for global communication for the AI company, explains how connecting medical devices like incubators, microscopes, and imaging systems to an IoT network could enable real-time monitoring, automated data collection, and advanced analytics.
By gathering data on culture conditions, embryo development, and other IVF parameters, IoT devices may allow labs to optimise processes, personalise treatment, and boost success rates.
The paper highlights key supporting technologies like 5G, blockchain, fog computing, and miniaturised sensors that could facilitate secure, real-time IoT applications in the fertility field and in particular in the IVF lab.
Palmer says the ability of 5G networks to enable fast data transmission from lab equipment, while blockchain provides robust data privacy and integrity.
However, Dr Jacques Cohen, eminent IVF pioneer, product developer and co-founder of IVF2.0, points out challenges like cybersecurity risks and network reliability that must be addressed before realising the full benefits of IoT in healthcare settings.
He says robust measures to safeguard patient data and advancements in networking will be vital.
Ultimately, Cohen argues that while IoT integration offers major advantages, human expertise remains essential in IVF laboratories.

Skilled embryologists will continue overseeing safety and procedures even with technological innovations. The paper emphasises balancing cutting-edge tools like IoT with proven clinical principles.
“This research highlights the exciting possibilities of IoT in IVF, but also the need for continued diligence by clinicians,” he concludes.
“By harnessing the strengths of both humans and technology, we can usher in a new era of efficiency and personalisation in assisted reproduction.”
As co-founder and chairman of the board of directors, Dr Andrew Drakeley states: “Our recent publication on the application of AI in the IoT, exemplifies IVF2.0’s dedication to investing in innovative research that pushes the boundaries and advances technology in the fertility space.
“I am proud that our top scientists are producing groundbreaking studies that contribute meaningfully to the AI literature.
“This paper demonstrates our company’s commitment to developing cutting-edge intelligent systems and underscores our leadership in leveraging AI to drive progress.”
The paper can be accessed here.

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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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