AUTHOR=Navarro-Cabrera Jorge Raul , Valles-Coral Miguel Angel , Farro-Roque María Elena , Reátegui-Lozano Nelly , Arévalo-Fasanando Lolita TITLE=Machine vision model using nail images for non-invasive detection of iron deficiency anemia in university students JOURNAL=Frontiers in Big Data VOLUME=Volume 8 - 2025 YEAR=2025 URL=https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2025.1557600 DOI=10.3389/fdata.2025.1557600 ISSN=2624-909X ABSTRACT=IntroductionIron deficiency anemia (IDA) is a global health issue that significantly affects quality of life. Non-invasive methods, such as image analysis using artificial vision, offer accessible alternatives for diagnosis. This study proposes a DenseNet169-based model to detect anemia from nail images and compares its performance with that of the Rad-67 hemoglobin meter.MethodsA cross-sectional study was conducted with 909 nail images collected from university students aged 18–25 years at the Universidad Nacional de San Martín, Peru. Samsung Galaxy A73 5G was used to capture images under controlled conditions, and clinical data were complemented with hemoglobin readings from the Rad-67 device. The images were pre-processed using segmentation and data augmentation techniques to standardize the dataset. Three models (DenseNet169, InceptionV3, and Xception) were trained and evaluated using metrics, such as accuracy, recall, and AUC.ResultsDenseNet169169 demonstrated the best performance, achieving an accuracy of 0.6983, recall of 0.6477, F1-Score of 0.6525, and AUC of 0.7409. Despite the presence of false-negatives, the results showed a positive correlation with Rad-67 readings.ConclusionThe DenseNet169-based model proved to be a promising tool for non-invasive detection of iron deficiency anemia, with potential for application in clinical and educational settings. Future improvements in preprocessing and dataset diversification could enhance performance and applicability.