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Background The overall prognosis of oral cancer remains poor because over half of patients are diagnosed at advanced-stages. Previously reported screening and earlier detection methods for oral cancer still largely rely on health workers’ clinical experience and as yet there is no established method. We aimed to develop a rapid, non-invasive, cost-effective, and easy-to-use deep learning approach for identifying oral cavity squamous cell carcinoma (OCSCC) patients using photographic images. Methods We developed an automated deep learning algorithm using cascaded convolutional neural networks to detect OCSCC from photographic images. We included all biopsy-proven OCSCC photographs and normal controls of 44,409 clinical images collected from 11 hospitals around China between April 12, 2006, and Nov 25, 2019. We trained the algorithm on a randomly selected part of this dataset (development dataset) and used the rest for testing (internal validation dataset). Additionally, we curated an external validation dataset comprising clinical photographs from six representative journals in the field of dentistry and oral surgery. We also compared the performance of the algorithm with that of seven oral cancer specialists on a clinical validation dataset. We used the pathological reports as gold standard for OCSCC identification. We evaluated the algorithm performance on the internal, external, and clinical validation datasets by calculating the area under the receiver operating characteristic curves (AUCs), accuracy, sensitivity, and specificity with two-sided 95% CIs. Findings 1469 intraoral photographic images were used to validate our approach. The deep learning algorithm achieved an AUC of 0·983 (95% CI 0·973–0·991), sensitivity of 94·9% (0·915–0·978), and specificity of 88·7% (0·845–0·926) on the internal validation dataset ( n = 401), and an AUC of 0·935 (0·910–0·957), sensitivity of 89·6% (0·847–0·942) and specificity of 80·6% (0·757–0·853) on the external validation dataset ( n = 402). For a secondary analysis on the internal validation dataset, the algorithm presented an AUC of 0·995 (0·988–0·999), sensitivity of 97·4% (0·932–1·000) and specificity of 93·5% (0·882–0·979) in detecting early-stage OCSCC. On the clinical validation dataset ( n = 666), our algorithm achieved comparable performance to that of the average oral cancer expert in terms of accuracy (92·3% [0·902–0·943] vs 92.4% [0·912–0·936]), sensitivity (91·0% [0·879–0·941] vs 91·7% [0·898–0·934]), and specificity (93·5% [0·909–0·960] vs 93·1% [0·914–0·948]). The algorithm also achieved significantly better performance than that of the average medical student (accuracy of 87·0% [0·855–0·885], sensitivity of 83·1% [0·807–0·854], and specificity of 90·7% [0·889–0·924]) and the average non-medical student (accuracy of 77·2% [0...

Shared decision making between children and parents is required in orthodontics. This study compared agreement among mothers, fathers, and children regarding the oral health-related quality of life (OHRQoL) of children. A sample of 71 child patients (41 girls and 30 boys) aged 12.6 years with an orthodontic treatment need, together with both their parents completed components of the child OHRQoL measure. Agreement among children, mothers, and fathers was derived from the 31 analogous questions and assessed using comparison and correlation analyses. Comparison analyses identified significant differences between mother's and children's reports and between father's and children's reports. The magnitude of the difference between mother's and children's reports, and between father's and children's reports could best be described as moderate (standard difference >0.2). In addition, absolute differences in scores constituted between 12 and 18 per cent of domain and overall scores for both mother's and children's, and father's and children's reports. Correlation analysis, at the individual family unit level, showed that agreement between mothers and children, and between fathers and children was fair [intraclass correlation coefficient (ICC) < 0.04]. Neither mothers nor fathers know their child's oral health status very well, as there was significant disagreement between mothers', fathers', and children's perceptions. The disagreement between mothers and children, and fathers and children was similar. While at the group level, mothers and fathers tended to agree on perception of their children's oral health status, at an individual family unit level they did not.

An EGCG3″Me supplemented diet produces promising effects on gut microecology by enhancing beneficial microbial populations and by affecting metabolic pathways including amino acids biosynthesis, the two-component system, and ABC transporters, contributing to the improvement of host health.

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