Highly reliable personalized noninvasive hemoglobin estimation by using Vision Transformers and dual fine-tuning

Artificial intelligence is revolutionizing health care, particularly in precision medicine and noninvasive diagnostics. Anemia, which is a widespread condition that affects billions of people worldwide, compromises oxygen transport due to low hemoglobin levels, which leads to severe complications if left undetected. Early and frequent monitoring is essential, yet traditional blood tests can be invasive, costly, and impractical for continuous assessment. This study presents the first patient-specific system for noninvasive hemoglobin estimation from palpebral conjunctiva images. Unlike previous approaches, our model integrates the vision transformer (ViT) architecture with dual fine-tuning, which enables personalized adaptation to each patient's unique physiological characteristics. The dataset consists of conjunctival images captured over multiple days from the same patients, which allows for an individualized calibration process that enhances predictive accuracy. Our model achieved an R2 of 0.94, an accuracy of 98%, and a mean absolute error (MAE) of 0.25 g/dL, thus demonstrating a performance comparable to that of laboratory tests. Additionally, the model's 100% sensitivity ensured that all anemic cases were detected, thereby minimizing the risk of false-negatives. By providing highly precise, rapid, and accessible anemia screening, this approach has the potential to redefine long-term hematological monitoring, thereby reducing reliance on frequent blood tests and improving clinical decision-making in resource-limited settings.