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Oral health phenotype of postmenopausal women using AI

  • Menopause is the permanent cessation of menstruation occurring naturally in women's aging. The most frequent symptoms associated with menopausal phases are mucosal dryness, increased weight and body fat, and changes in sleep patterns. Oral symptoms in menopause derived from saliva flow reduction can lead to dry mouth, ulcers, and alterations of taste and swallowing patterns. However, the oral health phenotype of postmenopausal women has not been characterized. The aim of the study was to determine postmenopausal women's oral phenotype, including medical history, lifestyle, and oral assessment through artificial intelligence algorithms. We enrolled 100 postmenopausal women attending the Dental School of the University of Seville were included in the study. We collected an extensive questionnaire, including lifestyle, medication, and medical history. We used an unsupervised k-means algorithm to cluster the data following standard features for data analysis. Our results showed the main oral symptoms in our postmenopausal cohort were reduced salivary flow and periodontal disease. Relying on the classical assessment of the collected data, we might have a biased evaluation of postmenopausal women. Then, we used artificial intelligence analysis to evaluate our data obtaining the main features and providing a reduced feature defining the oral health phenotype. We found 6 clusters with similar features, including medication affecting salivation or smoking as essential features to obtain different phenotypes. Thus, we could obtain main features considering differential oral health phenotypes of postmenopausal women with an integrative approach providing new tools to assess the women in the dental clinic.

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Author of HS ReutlingenKraft, Rodion; Martínez Madrid, Natividad
Erschienen in:Applications in electronics pervading industry, environment and society : APPLEPIES 2023. - (Lecture Notes in Electrical Engineering ; 1110)
Place of publication:Cham
Editor:Francesco Bellotti, Miltos Grammatikakis, Ali Mansour, Massimo Roch, Ralf Seepold, Agusti Solanas, Riccardo Berta
Document Type:Conference proceeding
Publication year:2024
Tag:artificial intelligence; menopause; phenotype
Page Number:7
First Page:222
Last Page:228
PPN:Im Katalog der Hochschule Reutlingen ansehen
DDC classes:610 Medizin, Gesundheit
Open access?:Nein
Licence (German):License Logo  In Copyright - Urheberrechtlich geschützt