AI-boosted electronic nose can detect ovarian cancer at an early stage

Donatella Puglisi, associate professor at Linköping University. Credit: Olov Planthaber

Using machine learning, an electronic nose can “smell” early signs of ovarian cancer in the blood. The method is precise and, according to the LiU researchers behind the study, it could eventually be used to find many different cancers. The study is published in Advanced Intelligent Systems.

“We’re trying to mimic the mammalian sense of smell artificially. We’ve now developed an algorithm that can distinguish ovarian cancer from endometrial cancer and healthy control groups, using data from an electronic nose,” says Donatella Puglisi, associate professor at Linköping University, Sweden.

In ovarian cancer, symptoms are often vague and similar to those of other more common diseases. This type of cancer is therefore detected at a late stage of development, when survival outcomes are poor. Earlier discovery would increase chances of timely medical care. In 2022, some 325,000 new cases of ovarian cancer and more than 200,000 deaths were reported globally. Moreover, the World Cancer Research Fund estimates that these figures will have increased drastically by 2050.

“More and more people are being diagnosed with cancer, especially young adults, and this is alarming. If screening were more accessible, both in terms of cost and location, it would be possible to improve early diagnosis. Our approach could facilitate the adoption of new screening protocols and the development of new diagnostic methods, improving survival rates, quality of life, and overall clinical outcomes,” says Puglisi.

Detailed spatiotemporal response of sensor #10 in the e-nose to VOCs emitted from blood plasma samples, measured across two batches (108 healthy individuals and 88 OC patients). Credit: Advanced Intelligent Systems (2026). DOI: 10.1002/aisy.202500838

Electronic nose technology has been around for about 60 years. The prototype used by the researchers has 32 sensors that react to various volatile substances emitted from the sample being examined. Each form of cancer emits different volatile substances, thus different cancers “smell” differently.

The sensors are of a relatively simple model and are available on the market. But with the dramatic development of machine learning and AI in recent years, established technology can be used in new ways.

Current health care cancer screening by blood test involves searching for a number of biomarkers that are unique to the form of cancer suspected. However, test analysis is slow and often not very accurate.

“Unlike in breast cancer, there is currently no reliable ovarian cancer screening method. These tests are often based on a single biomarker and lack the precision required to detect the disease at an early stage. Our method is therefore far ahead not only in terms of accuracy but also in the ability to identify early disease,” says Jens Eriksson, associate professor at LiU and CTO at VOC Diagnostics AB, the company developing the electronic nose.

Jens Eriksson, associate professor at Linköping University. Credit: Olov Planthaber

The method developed by the researchers does not need the identification of a specific biomarker. Instead, the electronic nose picks up a large variety of volatile substances emitted from blood plasma samples. The data are then analyzed using advanced machine-learning models to identify patterns specific to, in this case, ovarian cancer. The models are trained on known samples from a biobank. The tool has 97% accuracy.

“It’s a simple test that takes 10 minutes and gives a clear result. Our method can test many people at a low cost and is much more accurate than what’s on the market today. This study is a pilot, but we hope it will be used as part of cancer screening within three years. Right now, we’ve focused on detecting cancer, but the applications are endless,” says Eriksson.

Blood test detects ovarian cancer with high accuracy, study finds