Uncertainty quantification-guided patient-specific quality assurance using Bayesian neural networks based on field...

Objective.Recent advancements in artificial intelligence (AI)-driven prediction models for measurement-based patient-specific quality assurance (PSQA) necessitate uncertainty quantification (UQ) to ensure clinical safety.Approach.An uncertainty-guided framework was proposed for PSQA prediction. A classification model utilizing field complexity features and fluence maps was trained to categorize PSQA outcomes. Monte Carlo approximate Bayesian inference-based UQ method was employed to approximate the distribution of classification results. Pre-defined clinical uncertainty thresholds were defined via Correct-Certain (CC) and Incorrect-Uncertain (IU) curves to trigger manual intervention for high-uncertainty cases. A Multilayer Perceptron then predictedγpassing rates (GPR) by integrating classification model embeddings with uncertainty metrics. Prospective test was conducted on clinical test sets to systematically assess clinical reliability of the proposed framework.Main results.The classification model achieved sensitivities of 83.33% at 3%/3 mm, 93.33% at 3%/2 mm, and 94.74% at 2%/2 mm. The pre-defined clinical uncertainty thresholds were determined from CC and IU curves, ranged from 0.057-0.240 at 3%/3 mm, 0.014-0.207 at 3%/2 mm, and 0.080-0.244 at 2%/2 mm. Only 57.27% of manual intervention was required to ensure clinical safety, achieving 100% clinical sensitivity at 3%/3 mm. Compared to conventional measurement-based PSQA, this reduces the workload by 42.73%. The GPR prediction model yielded mean absolute errors of 1.64%, 1.88%, and 2.57% at 3%/3 mm, 3%/2 mm, and 2%/2 mmγcriteria, respectively, with uncertainty integration leading to a relative MAE reduction of 21.03% for the 'failed' cases. For clinical prospective test, the clinical sensitivity, specificity, and accuracy reached 100% under the minimum pre-defined uncertainty threshold at threeγcriteria after manual intervention.Significance.This framework enhances AI-based PSQA reliability by systematically addressing prediction uncertainty, reducing manual workload, and improving GPR accuracy for 'failed' fields, thereby facilitating safe clinical adoption of automated PSQA systems.