Can Explainable Artificial Intelligence Optimize the Data Quality of Machine Learning Model? Taking Meibomian Gland Dysfunction Detections as a Case Study

Abstract

Abstract Data quality plays a crucial role in computer-aided diagnosis (CAD) for ophthalmic disease detection. Various methodologies for data enhancement and preprocessing exist, with varying effectiveness and impact on model performance. However, the process of identifying the most effective approach usually involves time-consuming and resource-intensive experiments to determine optimal parameters. To address this issue, this study introduces a novel guidance framework that utilizes Explainable Artificial Intelligence (XAI) to enhance data quality. This method provides evidence of the significant contribution of XAI in classifying meibomian gland dysfunction (MGD) by aiding in feature selection, improving model transparency, mitigating data biases, providing interpretability, enabling error analysis, and establishing trust in machine learning (ML) models using multi-source meibomian datasets. The experimental results demonstrate substantial performance improvements in ML models when utilizing enhanced datasets compared to original images, as indicated by increased accuracy (0.67 vs. 0.86), recall (0.46 vs. 0.89), F1 score (0.48 vs. 0.84), XAI indicator (0.51 vs. 0.81), and IOU score (0.44 vs. 0.79). These findings highlight the significant potential of XAI in ML model MGD classification, particularly in advancing interpretability, standardization, fairness, domain integration, and clinical adoption. Consequently, the proposed framework not only saves valuable resources but also provides interpretable evidence for decision-making in data enhancement strategies. This study contributes to the understanding of XAI’s role in ML model MGD classification and its potential for driving advancements in key areas such as interpretability, standardization, fairness, domain integration, and clinical adoption.