Researchers from Chalmers University of Technology in Sweden have developed a new computer model using artificial intelligence (AI), which successfully identifies signs of lymphatic cancer.

The model was developed in collaboration with researchers from Memorial Sloan Kettering Cancer Center, Chalmers University of Technology, Medical University in Vienna, Icahn School of Medicine at Mount Sinai and NYU Langone Health, with results published in The Lancet Digital Health.

Lymphoma is a cancer of the lymphatic system, including the lymph nodes, spleen, thymus gland and bone marrow, and can affect other organs throughout the body.

The two main subtypes of lymphoma are Hodgkin’s lymphoma and non-Hodgkin’s lymphoma, which is the sixth most common cancer in the UK, responsible for around 14,200 cases every year, according to Cancer Research UK.

Using AI-assisted image analysis of lymphoma, researchers developed a deep learning system to train computers based on over 17,000 images from more than 5,000 lymphoma patients to spot visual signs of cancer in the lymphatic system.

The Lymphoma Artificial Reader System (LARS) works by inputting an image from positron emission tomography and analysing the image using the model to identify patterns and features in the image to confirm whether it contains lymphoma or not.

Researchers from the University of Glasgow’s James Watt School of Engineering are aiming to ensure that artificial intelligence (AI)-based healthcare monitoring systems in the future are free of gender bias, to improve care for both men and women.

For 18 months, the project will examine the potential for gender bias in healthcare AI and discover ways to ensure that AI-supported treatment remains equitable.

The use of cutting-edge sensors is currently being investigated to track the rhythms of patients’ hearts and lungs without requiring them to wear monitoring devices or be recorded on video cameras.

The team aims to address and ensure that its AI component is properly trained and capable of making the correct judgements without bias towards one gender of patients.

Supported by £8,200 in funding from the Université Paris Dauphine-PSL’s Women and Science Chair, with further support from the L’Oréal Foundation, Generali France, La Poste, Amundi and the Talan Group, researchers will work to develop a new framework to balance gender-related behaviours in an AI monitoring system.

Researchers will collect healthcare data from 30 male and 30 female study volunteers using radar sensors to be used to train a newly developed AI architecture and analyse the results of the radar monitoring.