ArticleComputational and structural biotechnology journal2025
Automatic detection of fungiform papillae on the human tongue via Convolutional Neural Networks and identification of the best performing model.
Article in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Determination of Fungiform Papilla Number Using Deep Learning Methods.Journal of imaging informatics in medicine · 2026Article
- Optical Coherence Tomography Assessment of Tongue Papillary Atrophy and Patient-Reported Impact on Taste Following Head and Neck Cancer Therapy.Journal of clinical medicine · 2026Article
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Authors and funding
6 authors.
Funding
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Abstract
Fungiform papillae (FPs) are fundamental for taste perception, as they contain the taste sensory cells responsible for detecting taste stimuli. Variations in the number and functionality of FPs among individuals lead to differences in taste perception, impacting the ability to identify nutrient-rich foods, health, and the joy of consuming tasty foods. Detecting FPs is a complex and time-consuming task, and there is no consensus on manual and automated methods for their identification and analysis.
objectivesThis work aimed to provide an efficient, reliable, and automatic method for FP identification on the tongue, considering the physiological variations in morphology and distribution among subjects.
methodsWe used three different Convolutional Neural Networks as a regression task on 175 images of the tongue, the Classic U-Net, the MultiResUNet, and the Optimized U-Net, designed to enhance the performance also when it must identify FPs in challenging input images.
resultsThe Optimized U-Net showed the best performance by achieving the lowest errors and the highest similarity between Ground Truths and prediction values, and the more balanced detection of True Positives, Untrue Negatives, and Untrue Positives.
conclusionsOur results show that the Optimized U-Net achieved the highest stability, accuracy, and robustness in learning and prediction of FPs with challenging morphologies. The ability to automatically detect FPs has important implications for understanding individual differences in taste perception, which could eventually help in diagnosing taste disorders or guiding personalized nutrition plans.
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