Comparison of manual and machine learning image processing approaches to determine fungiform papillae on the tongue

Automated identification and segmentation of urine spots based on deep-learning

Micturition serves an essential physiological function that allows the body to eliminate metabolic wastes and maintain water-electrolyte balance. The urine spot assay (VSA), as a simple and economical assay, has been widely used in the study of micturition behavior in rodents. However, the traditional VSA method relies on manual judgment, introduces subjective errors, faces difficulty in obtaining appearance time of each urine spot, and struggles with quantitative analysis of overlapping spots. To address these challenges, we developed a deep learning-based approach for the automatic identification and segmentation of urine spots. Our system employs a target detection network to efficiently detect each urine spot and utilizes an instance segmentation network to achieve precise segmentation of overlapping urine spots. Compared with the traditional VSA method, our system achieves automated detection of urine spot area of micturition in rodents, greatly reducing subjective errors. It accurately determines the urination time of each spot and effectively quantifies the overlapping spots. This study enables high-throughput and precise urine spot detection, providing important technical support for the analysis of urination behavior and the study of the neural mechanism underlying urination.

Machine learning and topological data analysis identify unique features of human papillae in 3D scans

The tongue surface houses a range of papillae that are integral to the mechanics and chemistry of taste and textural sensation. Although gustatory function of papillae is well investigated, the uniqueness of papillae within and across individuals remains elusive. Here, we present the first machine learning framework on 3D microscopic scans of human papillae ([Formula: see text]), uncovering the uniqueness of geometric and topological features of papillae. The finer differences in shapes of papillae are investigated computationally based on a number of features derived from discrete differential geometry and computational topology. Interpretable machine learning techniques show that persistent homology features of the papillae shape are the most effective in predicting the biological variables. Models trained on these features with small volumes of data samples predict the type of papillae with an accuracy of 85%. The papillae type classification models can map the spatial arrangement of filiform and fungiform papillae on a surface. Remarkably, the papillae are found to be distinctive across individuals and an individual can be identified with an accuracy of 48% among the 15 participants from a single papillae. Collectively, this is the first evidence demonstrating that tongue papillae can serve as a unique identifier, and inspires a new research direction for food preferences and oral diagnostics.

Alteration of taste perception, food neophobia and oral microbiota composition in children with food allergy

Currently, the mechanisms underlying sensory perception and sensory performance in children with food allergies are far from being understood. As well, only recently, single research afforded the oral host-commensal milieu, addressing oral microbial communities in children with peanut allergies. To bridge the current gaps in knowledge both in the sensory and microbial fields, a psychophysiological case-control study was performed in allergic children (n = 29) and a healthy sex-age-matched control group (n = 30). Taste perception, food neophobia, and liking were compared in allergic and non-allergic children. The same subjects were characterized for their oral microbiota composition by addressing saliva to assess whether specific profiles were associated with the loss of oral tolerance in children with food allergies. Our study evidenced an impaired ability to correctly identify taste qualities in the allergic group compared to controls. These results were also consistent with anatomical data related to the fungiform papillae on the tongue, which are lower in number in the allergic group. Furthermore, distinct oral microbial profiles were associated with allergic disease, with significant down-representations of the phylum Firmicutes and of the genera Veillonella spp., Streptococcus spp., Prevotella spp., and Neisseria spp. For the first time, this study emphasizes the link between sensory perception and food allergy, which is a novel and whole-organism view of this pathology. Our data indicated that an impaired taste perception, as regards both functionality and physiologically, was associated with food allergy, which marginally influences the food neophobia attitude. It is also accompanied by compositional shifts in oral microbiota, which is, in turn, another actor of this complex interplay and is deeply interconnected with mucosal immunity. This multidisciplinary research will likely open exciting new approaches to therapeutic interventions.

A survey of artificial intelligence in tongue image for disease diagnosis and syndrome differentiation

The rapid development of artificial intelligence technology has gradually extended from the general field to all walks of life, and intelligent tongue diagnosis is the product of a miraculous connection between this new discipline and traditional disciplines. We reviewed the deep learning methods and machine learning applied in tongue image analysis that have been studied in the last 5 years, focusing on tongue image calibration, detection, segmentation, and classification of diseases, syndromes, and symptoms/signs. Introducing technical evolutions or emerging technologies were applied in tongue image analysis; as we have noticed, attention mechanism, multiscale features, and prior knowledge were successfully applied in it, and we emphasized the value of combining deep learning with traditional methods. We also pointed out two major problems concerned with data set construction and the low reliability of performance evaluation that exist in this field based on the basic essence of tongue diagnosis in traditional Chinese medicine. Finally, a perspective on the future of intelligent tongue diagnosis was presented; we believe that the self-supervised method, multimodal information fusion, and the study of tongue pathology will have great research significance.