Automated Scan Region Classification and Patient-specific Dose Modeling for Enhanced Dose Management in Computed...

Effective dose management in computed tomography is impeded by 2 key operational challenges: error-prone manual protocol mapping and the high volume of nonactionable alerts from fixed diagnostic reference levels (DRLs). This "alert fatigue" creates a risk of overlooking clinically significant dose deviations. This study aimed to develop and evaluate a novel artificial intelligence (AI)-assisted framework to automate scan classification and provide a patient-specific context for dose assessment.

This retrospective study analyzed 2955 CT irradiation events. A processing pipeline was developed that first performs automated body segmentation using a deep learning model. A random forest classifier was then trained on the resulting organ volumes to identify 15 distinct scan regions. For 4 common examination types, linear regression models were established to predict the CT dose index (CTDI vol ) based on the patient's mean cross-sectional water-equivalent area. Cases were identified as statistical outliers if the absolute standardized residual was >2. The number of these outliers was compared with the number of conventional DRL exceedances.

The automated scan region classifier achieved high accuracy, with a macro-averaged F1 score of 93.8% on the hold-out test set. The regression models demonstrated a clear linear correlation between patient anatomy and CTDI vol (r = 0.56 to 0.79). Crucially, the patient-specific models identified substantially fewer cases for review (60 statistical outliers) compared with the standard DRL-based method (170 exceedances). Manual analysis confirmed that all flagged cases were clinically justified.

Our findings validate that an AI-assisted, patient-centered framework is a highly effective strategy for dose management. By shifting the paradigm from rigid, population-based thresholds to a dynamic, patient-specific assessment, our approach provides a more effective method for identifying potential dose deviations while substantially reducing the burden of nonactionable alerts. This work charts a course towards a new standard of radiation dose monitoring, advancing the field in the direction of a more efficient and reliable form of personalized dose monitoring.