Video showing interaction with Dotplot
Video showing interaction with Dotplot
Our handheld device and charging dock
Prototype of the mobile app
Snippets from our process including concept generation, user testing and product testing
Dotplot's key features
What it does
Dotplot is an at-home breast health monitoring tool that offers guided self-checks on a monthly basis. It is designed to facilitate the early detection of breast cancer by enabling and encouraging women to stick to a regular breast-self check routine.
One of our co-founders, who is a female athlete, discovered an unusual knot in one of her breasts after a gym workout. She approached a gynecologist for a clinical breast exam, where a palpation test was performed, and she was advised to monitor the knot using her own fingers for a few months. Fortunately, the knot self-resolved. Using this event as a starting point, we sought to discover existing tools that routinely assist women in monitoring their breasts. Astonished by the lack of at-home solutions for the early detection of breast cancer, we were determined to build a tool that would address this need.
How it works
To use Dotplot, users are taken through one-time onboarding on the app which includes entering the details of their period cycle – if they have one – to offer the correct date for their self-check. They then build a personalised map of their torso by providing their bra size, breast shape and sliding the handheld device to rescale the baseline model. Once set up, the app guides women through the self-check by showing which areas they need to scan. The position of the device on the torso is determined by Dotplot’s pre-trained system which analyses the orientation of the device relative to the ground. A sound signal of a known frequency is emitted to record the tissue composition at the site. The point that the user needs to check flashes on the app until a reading has been taken. Each month's reading is compared to the previously recorded readings to highlight any abnormalities developing in the tissue. Users can choose to send reports directly to their GP.
We began by investigating existing solutions for instructing women on how to perform self-checks. We then conducted over 50 interviews both with women who do and do not have a family history of breast cancer to gather key insights on how they currently engage with self-checks. We presented women with various embodiments of the device including physical and digital prototypes to determine the ideal user interaction. Following this, we were able to conclude that women were looking for visual guidance and feedback. We narrowed down to active (handheld) and passive (wearable) interactions for self checks to which women responded that a handheld device instills greater trust in the process. For the technical development, we used sound waves to detect lumps within a breast surrogate and found clear differences in readings in areas with and without lumps. The prototype could detect lumps up to 15mm deep. We refined our prototype and used machine learning to sample different lump sizes at different depths and their features were extracted. Moreover, we were in constant discussion with breast cancer surgeons, radiologists, GPs, and other professionals for scientific validation, technological development and to determine how Dotplot would fit into the medical landscape.
How it is different
Existing scanners/devices and those in development are designed chiefly for clinical use. Dotplot however is targeted at home-use, as it looks to provide women with more autonomy over their breast health care. Our device is also roughly one-third of the size of existing scanners, making it less resource-heavy and desirable as a product. Moreover, there are no off-the-shelf products capable of providing real-time feedback of the areas covered during a breast self-check. This feature is critical for assuring women that they have checked over every region. Finally, current guidance for self-checks is limited to demos, tutorials, and pamphlets. The team found from the several women interviewed that even after a nurse or GP demonstrated a self-check, they were still not certain that they were doing them correctly. In short, the current methods result in considerable amounts of guess work which Dotplot aims to eliminate.
Our immediate next step is to create an MVP that would involve bringing the two aspects of our solution - lump detection and locating the device on the user’s body - into one compact device. We then aim to work with asymptomatic breast cancer clinics as well as medical regulatory advisors to run and audit our first clinical trial for an efficacy test. We are currently also looking at recruiting data engineers. Within the next few years, we hope to be selling Dotplot directly to consumers. Further ahead, we plan to apply our technology to the early detection of other cancers and illnesses including testicular cancer and soft tissue sarcoma.
Dotplot won the Venture Catalyst Challenge, Imperial College’s largest entrepreneurial competition and was also awarded the Helen Hamlyn Design Award for Digital Inclusion, sponsored by TATA Consultancy Services. It recently got accepted onto the MedTech Super Connector cohort (2022-23).