AI-enhanced electronic nose shows promise for early ovarian cancer detection

Researchers have used artificial intelligence to greatly improve the performance of an electronic nose sensor system that detects volatile organic compounds (VOCs) in breath and bodily samples, a development that may enable earlier, non-invasive ovarian cancer detection.
AI cancer screening, ovarian cancer, electronic nose, volatile organic compounds, early detection, machine learning models, non-invasive diagnostics

Scientists are combining AI with advanced sensor technology, commonly known as an electronic nose, to detect subtle patterns in volatile organic compounds (VOCs) associated with ovarian cancer.

The AI component improves the system’s ability to differentiate disease-specific chemical fingerprints from benign or background VOC profiles, increasing sensitivity and specificity compared with earlier sensor-only approaches.

Ovarian cancer is notoriously difficult to diagnose in early stages due to vague symptoms and a lack of reliable screening tools. The AI-boosted electronic nose aims to fill this gap by analysing breath, urine, or blood headspace samples in a non-invasive manner, with the potential to be deployed in clinical or even point-of-care settings.

Early experimental results suggest that regressing VOC patterns using machine learning models can distinguish ovarian cancer cases with greater accuracy than traditional methods alone. However, larger clinical validation studies are still underway.

Researchers emphasise that this technology is intended as a screening and triage tool to flag individuals for more definitive diagnostics, not as a standalone diagnostic test at present.

If successfully scaled and validated, AI-enhanced VOC detection could lead to earlier interventions and improved survival outcomes for patients with ovarian cancer.

Would you like to learn more about AI, tech and digital diplomacy? If so, ask our Diplo chatbot!