Every year, over 2 million women worldwide are diagnosed with breast cancer. For the majority, the prognosis depends on a single factor: how early it was caught. But early detection requires a specialist and a scanner — and millions of women don’t have access to either.
In rural areas, in developing regions, and in underserved communities, women wait months for an appointment, or travel hundreds of kilometres to a hospital. By the time they’re diagnosed, what could have been caught at Stage 1 is now Stage 3 or Stage 4.
This system brings IoT sensors and machine learning together to make early breast cancer detection possible anywhere — not just in major hospitals — giving clinicians everywhere the analytical power previously reserved for specialist centres.
Early-stage breast cancer is highly treatable. Late-stage breast cancer is often not. Yet access to screening and diagnostic expertise is deeply unequal: concentrated in cities, in wealthy countries, in well-resourced healthcare systems. The tools that save lives are not where the people most at risk actually are.
The system uses IoT-connected sensors to capture the relevant biological signals and feeds them into trained machine learning algorithms that can classify and flag abnormalities at a level of accuracy previously requiring specialist radiologist review — making quality breast cancer screening accessible in clinics, community health centres, and remote facilities.
A woman in a rural community can now receive a meaningful preliminary screening at her local clinic rather than waiting months for a referral to a distant specialist — with the same underlying analytical capability.
A GP without specialist oncology training can use the system to get an ML-backed assessment, confidently refer high-risk patients early, and avoid missing cases that would have been invisible to the naked eye.
By catching more cases earlier — when they are cheaper to treat — the system reduces the downstream burden on oncology departments, ICUs, and palliative care services.
The system collects biological and physiological data through IoT-enabled sensors, building a rich dataset from the patient without requiring specialist equipment or invasive procedures.
Like a smartwatch that monitors your heart — but designed specifically to detect what radiologists look for.
The captured signals are processed by ML algorithms trained on large datasets of confirmed breast cancer cases and healthy controls — identifying patterns and deviations that indicate risk with high accuracy.
Like a specialist who has reviewed a million scans and recognises the subtle signs that most clinicians would miss.
The system outputs a clear risk score — low, intermediate, or high — along with the data points that drove it. The clinician uses this to decide whether to refer, monitor, or reassure, with the AI’s reasoning fully transparent.
Like a second opinion that’s always available, always consistent, and never busy.
AI
IoT
MedTech
Most cancer detection innovation focuses on improving existing hospital equipment — better MRI machines, higher-resolution imaging. This system takes a fundamentally different approach: using IoT sensors and ML to bring the analytical capability of a specialist to settings where specialist equipment could never reach. It is a distributed screening system, not an upgraded hospital tool — and that distinction is what the patent protects.
