**Authors:** Muxiu Yang, Fengzhou Li, Wenfeng Qiu **Published:** _IEEE Access_, 2025 **Link:** [IEEE Xplore](https://doi.org/10.1109/ACCESS.2024.3523519) --- ### Summary This study presents a real-time monitoring system for **dental equipment** using [[AutoGluon]], an automated machine learning (AutoML) framework. The system classifies equipment performance as **Optimal, Warning, or Failure** based on real-time sensor data like **temperature, vibration, and pressure**. It aims to prevent unexpected equipment failure and optimize maintenance schedules through predictive analytics. --- ### Key Points - **[[AutoGluon]] for Real-Time Monitoring:** - Automatically selects and trains [[ML]] models for performance classification. - Models are fine-tuned with **hyperparameter optimization** and **feature selection**. - Achieved **100% accuracy** in classifying equipment states using the **Weighted Ensemble model**. - **Sensor Data:** - Uses temperature, vibration, and pressure sensors to detect anomalies. - **SHAP Analysis** showed **temperature** and **vibration** as the most influential features. - **Model Performance:** - Weighted Ensemble achieved **100% accuracy**, **balanced accuracy**, and Matthews Correlation Coefficient #MCC of **1.0**. - Other models like [[LightGBM]], [[RandomForest]], and [[CatBoost]] also performed exceptionally well. - **Scalability and Deployment:** - Designed to be scalable and deployable in **live clinical environments**. - Future improvements include **edge computing** for faster real-time analysis and expanding the system to more types of dental equipment. --- ### How It Works 1. **Data Collection:** - Real-time sensor readings (temperature, vibration, pressure) are streamed from dental equipment. 2. **Preprocessing and Training:** - Data is preprocessed with **label encoding** and **normalization**. - [[AutoGluon]] handles **model selection**, **training**, and **hyperparameter tuning**. 3. **Model Evaluation:** - Models are tested for #Accuracy, #Precision, #Recall, and #F1-score - The best-performing models are ensembled for optimal predictions. 4. **Deployment:** - The system is deployed for **real-time monitoring** and **predictive maintenance** in dental clinics. --- ### Strengths - **High Accuracy:** Achieves 100% classification accuracy in real-time. - **Low Maintenance:** [[AutoGluon]] handles model selection and optimization automatically. - **Scalable:** Can be expanded to include more sensor types and equipment. --- ### Limitations - **Data Dependency:** Requires high-quality sensor data for optimal performance. - **Limited Equipment Types:** Currently focused on X-rays, dental chairs, and sterilizers. - **Sensor Integration:** Needs seamless integration with proprietary dental equipment.