Revolutionary toilet sensor for cancer

OutSense has developed a novel technology capable of accurately analysing and diagnosing human waste from within a standard toilet bowl, offering a potential early screening option for colorectal cancer, as well as other medical conditions.

The OutSense IoT (Internet of Things) sensor clips onto the toilet and operates automatically, non-invasively, discreetly and without active user intervention. Using multispectral optical sensors, an illumination module and an autonomous controller with a Wi-Fi receiver, it scans solid waste for even tiny traces of blood – a possible sign of disease that can be missed by the lab-based faecal immunological test (FIT). 

The sensor ‘knows’ who is sitting on the toilet based on the closest smartphone, as well as other ways to identify the user that the company cannot disclose at the moment. The OutSense platform was clinically tested, and using advanced algorithms of image analysis and machine learning, demonstrated an accuracy of 90% in blood detection vs the gold standard.

Covid risk app for cancer patients 

A new app, designed for high-risk cancer patients, helps them and their physicians weigh the risks and benefits of delaying chemotherapy should the risk of contracting Covid-19 while getting their treatment be high. The app, OncCOVID, can show oncologists and patients whether immediate treatment could improve or hurt their chances of survival post-treatment. Developed by the University of Michigan in Ann Arbor, the app is meant to help those involved make critical decisions more easily by presenting them with available data. The researchers also hope to soothe the apprehensions of people regarding the timing of their cancer treatment and prognosis. The app can be downloaded on a smartphone or computer.

AI may improve survival in glioblastoma

In the first study of its kind in cancer, researchers have applied artificial intelligence (AI) to measure the amount of muscle in patients with glioblastoma to help improve prognosis and treatment. Dr Ella Mi, a clinical research fellow at Imperial College London, UK, told the recent NCRI Virtual Showcase that using deep learning to evaluate MRI brain scans of a muscle in the head was as accurate and reliable as a trained person, and was considerably quicker. She also showed that the amount of muscle measured in this way could be used to predict how long a patient might survive their disease as it was an indicator of a patient’s overall condition. The study focused on the temporalis muscle on either side of the head that are used for chewing food, which have been identified as a good way to estimate skeletal muscle mass in the body.

Box that detects breast cancer at home

A device that detects breast cancer by analysing a patient’s urine from the comfort of their own home has been awarded the international James Dyson Award 2020. 

While the majority of current breast cases are detected by patients reporting symptoms to their GP or detection during a mammogram or breast ultrasound, Judit Giró Benet’s device performs chemical analysis on a urine sample. The Blue Box device, invented by the 23-year-old student from Spain, sends its results to the cloud, where an artificial intelligence (AI) algorithm reacts to specific metabolites within the sample that are likely to indicate the presence of cancer. Its diagnosis is then relayed to an app on the user’s smartphone.

The pain-free, non-invasive method could encourage greater numbers of women to test themselves, removing the need to travel to a GP surgery or hospital.

Ms Giró Benet started developing the prototype during her biomedical engineering degree at the Universitat de Barcelona in 2017, before moving to the US and starting work on a second version while studying at the University of California, US. 

Detection of cancer from exhaled breath

Researchers at the Fraunhofer Project Hub for Microelectronic and Optical Systems for Biomedicine (MEOS) in central Germany are now developing solutions designed to enable the analysis of breath gas to detect cancer. 

A lot of diseases cause a change in the composition of the volatile organic trace gases in exhaled air that can be used as biomarkers. A special ion-mobility spectrometer (IMS) is being developed to identify these patterns. Given that each person exhales around 200 volatile organic compounds (VOCs), this is by no means an easy job. The focus of this research is the detection of cancer – particularly lung cancer.

The research team at Fraunhofer MEOS hopes this new technology will be able to detect a broad range of biomarkers, including indicators for neurodegenerative diseases such as Alzheimer’s. They also want to use it to distinguish between Covid-19 and other respiratory infections.