Atopic dermatitis (AD) is the most common chronic inflammatory disease in children and adults. This condition severely impacts the patient’s quality of life. Worldwide prevalence ranges from 7% to 30% in children and 1% to 10% in adults. Clearly, early diagnosis and individualized treatment of AD require reliable and accurate assessment methods.
The use of machine learning (ML) in the medical field has grown significantly in recent years. ML can help detect and classify diseases, improve predictions, and personalize treatments. Although ML is mainly used in dermatology to identify skin lesions and histopathological images such as vitiligo and psoriasis, few models have been reported at the molecular level for Alzheimer’s disease. Dermatology candidates from Third Xiangya Hospital, Central South University, China, and their co-authors have established several relatively stable and reliable diagnostic and efficacy assessment predictive models for AD based on ML algorithms.
Researchers used publicly available RNA transcriptome data from AD lesional and nonlesional lesions with three different ML algorithms to develop six predictive models of AD. Lasso, Linear Regression (LR), Random Forest (RF). The model showed excellent performance in discriminating between AD lesions and non-lesions (AUC > 0.8).
Samples receiving biologic therapy showed a positive correlation with model score with SCORAD (SCORing Atopic Dermatitis) and a negative correlation with duration of treatment, showing a trend of improvement.
“These results demonstrate the potential of the model, especially for assessing the therapeutic efficacy of biologics and small molecule drugs. However, due to the small sample size and lack of sample quality, The correlation coefficient between the two models and SCORAD was not high enough,” explained Wu.
The team published the findings in a magazine basic research.
According to corresponding authors Qinghai Zeng and Jing Chen, ML-based models show good predictive performance in the diagnosis and treatment effects of Alzheimer’s disease, suggesting new options for early diagnosis and intervention.
Going forward, the team plans to collect patient samples for validation and evaluation of model stability.
For more information:
Songjiang Wu et al., A machine learning-based predictive model for the diagnosis and assessment of atopic dermatitis, basic research (2023). DOI: 10.1016/j.fmre.2023.02.021
Provided by: KeAi Communications Inc.
