Atopic dermatitis (AD) is a chronic inflammatory disease most commonly seen in children and adults. The condition seriously affects patients’ quality of life. Its prevalence rate worldwide is 7-30% in children and 1-10% in adults. Evidently, reliable and accurate evaluation methods are necessary for early diagnosis and individual treatment of AD.
The use of machine learning (ML) in medical fields has increased substantially in recent years. ML helps detect and classify diseases, improve predictions, and personalize treatment. ML is mostly used in dermatology to identify skin lesions or histopathologic images, such as those for vitiligo and psoriasis, but very few models at the molecular level have been reported for AD—until recently, when Songjiang Wu, a PhD candidate in dermatology from Third Xiangya Hospital at the Central South University in China and his co-authors established several relatively stable and reliable diagnosis and efficacy evaluation prediction models for AD based on ML algorithm.
The researchers developed six prediction models for AD, using publicly available RNA transcriptome data from AD lesions and non-lesions with three different ML algorithms; lasso, linear regression (LR) and random forest (RF). The models displayed excellent performance in distinguishing between AD lesions and non-lesions (AUC >0.8).
Those samples which received biological therapy showed a positive correlation of the model score with SCORAD (“SCORing Atopic Dermatiti”s) and a negative correlation with treatment duration, indicating an improving trend.
"These results indicate the potential of the models to evaluate treatment efficacy, especially for biological agents and small molecule drugs; however, due to the small sample size and lack of sample quality, the correlation coefficients between the two models and SCORAD were not high enough,” explained Wu.
The team published their findings in the KeAi journal Fundamental Research .
According to the corresponding authors Qinghai Zeng and Jing Chen, ML-based models showed favorable prediction performance in AD diagnosis and treatment efficacy, suggesting new options for early diagnosis and intervention.
Going forward, the team, will collect patient samples for verification and evaluation of the stability of the models.
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Contact the corresponding author: Qinghai Zeng, zengqinghai@csu.edu.cn
The publisher KeAi was established by Elsevier and China Science Publishing & Media Ltd to unfold quality research globally. In 2013, our focus shifted to open access publishing. We now proudly publish more than 100 world-class, open access, English language journals, spanning all scientific disciplines. Many of these are titles we publish in partnership with prestigious societies and academic institutions, such as the National Natural Science Foundation of China (NSFC).
Fundamental Research
Computational simulation/modeling
Human tissue samples
Machine learning-based prediction models for atopic dermatitis diagnosis and evaluation
The authors declare that they have no conflicts of interest in this work