Prediction of 3-month treatment outcome of IgG4-DS based on BP artificial neural network
Yanxiong Shao1,2,3, Zhijun Wang1,2,3, Ningning Cao1,2,3
1Department of Oral Surgery, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Insights
This study developed an artificial neural network model to predict treatment outcomes for IgG4-related disease (IgG4-DS). The model accurately forecasts the reduction of serum IgG4 levels after three months, aiding clinical decisions.
Area of Science:
- Immunology
- Medical Informatics
- Artificial Intelligence
Background:
- Immunoglobulin G4-related disease (IgG4-DS) is a complex condition requiring effective treatment monitoring.
- Predicting treatment response is crucial for managing IgG4-DS and improving patient outcomes.
Purpose of the Study:
- To develop and validate a back-Propagation artificial neural network (BP-ANN) model for predicting the 3-month treatment outcome in IgG4-DS patients.
- To identify key clinical variables associated with treatment response in IgG4-DS.
Main Methods:
- A cohort of 26 IgG4-DS patients was analyzed retrospectively.
- Spearman's rank correlation test identified associations between risk factors and serum IgG4 reduction.
- A BP-ANN model was constructed using MATLAB R2019b, incorporating significant variables.
Main Results:
- Erythrocyte Sedimentation Rate (ESR), serum IgG4 (sIgG4), and serum IgG (sIgG) were significantly associated with sIgG4 reduction (p < .05).
- The developed BP-ANN model achieved a high coefficient of determination (R² = 0.95512).
- The model demonstrated strong predictive capability for 3-month sIgG4 reduction.
Conclusions:
- The BP-ANN model, utilizing ESR, sIgG4, and sIgG, effectively predicts 3-month sIgG4 reduction in IgG4-DS patients.
- This predictive model holds significant potential for clinical application in managing IgG4-DS.
Objective:
The study aimed to establish an effective back-Propagation artificial neural network (BP-ANN) model for automatic prediction of 3-month treatment outcome of IgG4-DS.
Methods:
A total of 26 IgG4-DS patients at Shanghai Ninth People's Hospital from January 2018 to December 2019 were involved in the study. They were all followed for >3 months. The primary outcome was reduction of serum IgG4 (sIgG4) after 3-month treatment. The association between risk factors and reduction of sIgG4 was analyzed by Spearman's rank correlation test. According to the R values, we built a BP-ANN model by MATLAB R2019b.
Results:
The average reduction of sIgG4 was 5.55 ± 5.03. After Spearman's rank correlation test, ESR, sIgG4, and sIgG were independently associated with reduction of sIgG4 (p < .05) and were selected as input variables. Take into account these parameters, BP-ANN model was developed and the coefficient of determination (R2 ) model was 0.95512.
Conclusion:
The BP-ANN model based on ESR, sIgG4, and sIgG could predict the 3-month reduction of sIgG4 for IgG4-DS patients. It showed potential clinical application value.
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