## 4 Discussion  
Automation in radiotherapy treatment planning has been steadily increasing in recent years. Automatic optimization processes have the potential of improving the efficiency of the radiotherapy workflow by standardizing planning procedures and enhancing the overall quality of generated plans [21; 22; 23]. In particular, artificial intelligence represents a promising tool for personalized tailored treatments for a wide range of diseases [24; 25; 26]. This study presented a proof of concept for introducing an automatable replanning strategy using an ML-based VQA. In fact, despite VQA being able to streamline measurement-based PSQA [4; 5; 8; 27], the replans and associated workload due to a failed PSQA can result in delayed clinical treatments. Log file analysis could provide a time-efficient alternative method for detecting both small systematic and random errors in MLC positioning during delivery. However, this approach is regarded as lacking independence from the delivery system and there are still concerns about whether it could offer the same confidence as conventional PSQA methods [28; 29]. We selected ten patients found at risk of PSQA failure by our ML model and investigated three replanning procedures consisting of limiting the delivered MUs (MUlimit) or simplifying the MLC aperture shape during optimization (ASC_L and ASC_H). To assess the optimizations outcomes, we considered the plan dosimetric quality, expressed in terms of DVCs, and plan deliverability, quantified by plan complexity with the MCS and GPR. This ensured a comprehensive evaluation of plan quality [30]. Overall, the replanning methods produced an increase in DVCs for OARs and BODY, with a reduction for CTV/PTV. In particular, the largest significant differences were found with the MUlimit and ASC_H for, respectively, CTV/PTV and OARs. Nonetheless, we highlight that the clinical constraints for the re-optimized plans were still respected in all  
Figure 5: Box plots showing the distribution of the MCS, pGPR, and mGPR for the arcs of the clinical plans and re-optimized plans with the selected replanning strategies. Abbreviations: ASC = aperture shape controller, DVC = dose volume constraint, MCS = modulation complexity score, mGPR = measured gamma passing rate, MRO = manual re-optimization, MU = monitor unit, pGPR = predicted gamma passing rate. cases. In particular, the target coverage remained within acceptable limits, and doses to OARs were optimized following the ALARA principle (As Low As Reasonably Achievable). Regarding the deliverability, the three strategies significantly reduced the plan complexity, with MUlimit producing the highest MCS. This trend was confirmed by the mGPR results, which on average increased in all cases, though the changes were not significant for the MUlimit strategy. We hypothesize this was due to external factors, such as EPID degradation and setup, which could have influenced the PSQA measurements and produced low mGPRs (see Figure 5). As the EPID is a perpendicular composite method, the integrated image may mask some dose delivery errors, such as those in the scattered regions and those due to non-uniform dose rate. Moreover, EPIDs are not ideal absolute dosimeters and require careful calibration due to their smaller thickness compared with the X-ray build-up. Utilizing other devices for PSQA with characteristics other than EPID (e.g., ArcCHECK, Delta4 Panthom+, or MatrixXX) could affect the gamma analyses and should be investigated. Our results are aligned with other studies, which demonstrated that these tools reduce the plan complexity but tend to increase the dose to normal tissue and reduce the dose to the target compared with the reference clinical plan. Specifically, [PERSON] et al. investigated the use of MUlimit and ASC_VH in ten prostate and ten orophnx plans [18]. They reported the ASC was effective for reducing the unnecessary complexity of a plan, with an increased plan deliverability and no loss of plan quality. In another work, [PERSON] et al.