Upload an ATR-FTIR infrared spectrum file from a saliva sample and receive an instant machine-learning-based screening result for Type 2 diabetes risk.
Accepted formats: CSV (wavenumber, absorbance columns) or JSON array of absorbance values from 399–4000 cm⁻¹.
From saliva sample to screening result in four steps.
A small saliva sample is collected after fasting. No needles, no blood.
The sample is scanned with infrared spectroscopy to capture its molecular fingerprint.
Export the spectrum as a CSV or JSON file and upload it here.
Our neural network analyzes the spectral pattern and outputs a risk confidence score.
Type 2 diabetes is a chronic condition where the body doesn't use insulin effectively, causing blood glucose levels to rise. It accounts for over 90% of all diabetes cases worldwide and affects more than 500 million people.
Key risk factors include being overweight, physical inactivity, family history of diabetes, age over 45, and a history of gestational diabetes. Lifestyle changes can significantly reduce risk.
Saliva contains biochemical information about metabolic changes in the body. ATR-FTIR spectroscopy can detect molecular-level differences between diabetic and non-diabetic saliva — offering a non-invasive alternative to blood tests.
Regular physical activity (150 min/week), a balanced diet low in processed sugars, maintaining a healthy weight, and avoiding smoking can significantly lower your risk of developing Type 2 diabetes.
This prototype was built as part of a TU/e Multi-Disciplinary CBL project. The neural network was trained on 1,040 ATR-FTIR saliva spectra from the SEDENA dataset (Mexico, 2019–2020). It is not a certified medical device.
Official diabetes screening uses fasting blood glucose (≥126 mg/dL) or HbA1c (≥6.5%). If you're concerned, contact your GP. Early detection leads to much better outcomes.