The models displayed excellent performance in distinguishing between AD lesions and non-lesions.

Researchers have developed six relatively stable and reliable diagnosis and efficacy evaluation prediction models for atopic dermatitis (AD) based on a machine learning algorithm.

Songjiang Wu, a PhD candidate in dermatology from Third Xiangya Hospital at the Central South University in China , and colleagues developed the prediction models using publicly available RNA transcriptome data from AD lesions and non-lesions with three different machine learning 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 Dermatitis) 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,” explains Wu in a news release.

The team published their findings in 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.

PHOTO CAPTION: a). The models can accurately identify AD lesions in training data; b). LASSO (REC and AAG) and LR (REC and AAG) models showed good classification performed in the testing datasets; c). all genes displayed a significant time-dependent downregulation in the LASSO and LR models with Dupilumab treatment; d). LASSO(REC) and LR(AAG) model scores were positively correlated with SCORD; e). Correlation between LASSO (REC) and LR (AAG) model scores and immune cell infiltration.

PHOTO CREDIT: The authors