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An annotated dataset of tongue images supporting geriatric disease diagnosis.


ABSTRACT: Hospitalized geriatric patients are a highly heterogeneous group often with variable diseases and conditions. Physicians, and geriatricians especially, are devoted to seeking non-invasive testing tools to support a timely, accurate diagnosis. Chinese tongue diagnosis, mainly based on the color and texture of the tongue, offers a unique solution. To develop a non-invasive assessment tool using machine learning in supporting a timely, accurate diagnosis in the elderly, we created an annotated dataset of 15% of 688 (=100) tongue images collected from hospitalized geriatric patients in a tertiary hospital in Shanghai, China. Images were captured via a light-field camera using CIELAB color space (to simulate human visual perception) and then were manually labeled by a panel of subject matter experts after chart reviewing patients' clinical information documented in the hospital's information system. We expect that the dataset can assist in implementing a systematic means of conducting Chinese tongue diagnosis, predicting geriatric syndromes using tongue appearance, and even developing an mHealth application to provide individualized health suggestions for the elderly.

SUBMITTER: Shi D 

PROVIDER: S-EPMC7452583 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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An annotated dataset of tongue images supporting geriatric disease diagnosis.

Shi Dan D   Tang Chunlei C   Blackley Suzanne V SV   Wang Liqin L   Yang Jiahong J   He Yanming Y   Bennett Samuel I SI   Xiong Yun Y   Shi Xiao X   Zhou Li L   Bates David W DW  

Data in brief 20200808


Hospitalized geriatric patients are a highly heterogeneous group often with variable diseases and conditions. Physicians, and geriatricians especially, are devoted to seeking non-invasive testing tools to support a timely, accurate diagnosis. Chinese tongue diagnosis, mainly based on the color and texture of the tongue, offers a unique solution. To develop a non-invasive assessment tool using machine learning in supporting a timely, accurate diagnosis in the elderly, we created an annotated data  ...[more]

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