Machine learning-enhanced gas sensor technology identifies ovarian and endometrial cancer of all stages through plasma volatile organic compound patterns

The Strategic Innovation Programs Swelife and MedTech4Health, a joint venture of Vinnova, Formas and the Energy Agency (grant No. 2022-03464 and grant No. 2023-03874) and within the Convergence Accelerator Program (Track L - Real-World Chemical Sensing Applications), funded by the Swedish Research Council, Vetenskapsrådet (grant No. 2023-07219), and Sweden's Innovation Agency, Vinnova (grant No. 2023-04186), in collaboration with the US National Science Foundation (NSF). The computations, data handling, and machine learning model training and testing were conducted in the MATLAB environment and enabled by resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS), partially funded by the Swedish Research Council through grant agreement no. 2022-06725. This work received funding from the European Union's Horizon Europe Research and Innovation Programme under Grant Agreement No 101214318 (DISARM). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the Health and Digital Executive Agency (HaDEA). Neither the European Union nor HaDEA can be held responsible for them.