## 4 Discussion
For these reasons, in the present work we focused on the analysis of respiratory tracks for building a decision support system which could identify left breast cancer patient benefitting from the DIBH radiotherapy treatment. Although the introduction of AI in radiotherapy is still at an early stage [64; 65], its most promising applications in this context include treatment recommendation, and prioritisation. A machine learning tool was developed which could recommend proton or photon treatment, by estimating which treatment will result in better quality of life for the patient [66; 67; 68]. LSTM looks promising for analysis of signals in radiotherapy. [PERSON] et al. described the prediction of respiratory motion at different time steps of 250 ms, 500 ms, and 750 ms, for precise gated targeting in MR-guided radiotherapy [45]. [PERSON] et al. applied LSTM to prediction of daily quality controls on a linear accelerator for radiotherapy [69]. Training an AI tool requires choice of a proper training dataset, as the quality of an AI tool is affected by the quality of the training dataset, a principle known as \"garbage in, garbage out\". In our work, the strategy of choosing the median value of \(\textit{med}(\Delta\text{D}_{\text{L}})\), results in a perfectly balanced dataset, thus circumventing the difficulties related to dealing with data imbalance. Data imbalance occurs when data distribution is skewed, e.g., when the number of positives is much lower than the number of negatives, a common situation for screening of a rare disease [70]. Models developed from this type of imbalanced dataset would result in poor performance in the minority class, such as poor sensitivity. Because of the well-balanced dataset, in our result we had acceptable values of both sensitivity and specificity. The proposed AI tool based on BLSTM, could support the clinician's decision early in the radiotherapy workflow in selecting patients for DIBH. The clinical implementation of artificial intelligence algorithms is slow because they are considered by physicians as \"black boxes\", meaning that the process that leads to their decisions is difficult to understand [71]. This issue usually makes implementation of AI in the clinical practice more difficult, as clinicians do not trust tools which are opaque in their decisions [23; 72]. One of the strategies for improving interpretability, is to identify features in the data that are used by the AI in reaching its decision. By comparing respiratory tracks of patients classified as negative or positive, we can derive the characteristics used by the neural network to classify the patients. The respiratory signal of a patient considered eligible for DIBH treatment (Figure 2a) is characterised by longer deep inspiration phases, compared with the patient respiratory signal in Figure 1b, which is classified as negative. At present, identifying patients with higher dosimetric benefit to the LAD due to DIBH, requires the acquisition of both FB and DIBH CTs, planning of treatments in FB and DIBH modalities, and evaluation of dose metrics to the LAD. The AI tool developed in the present study allows us to circumvent this cumbersome process, by analysing the respiratory tracks acquired in the same session as the pre-treatment CT acquisition. As such, the tool is useful in automating the choice of treatment approach for left-breast cancer patients. Due to its retrospective nature and the relatively small dataset, the findings in the present study should be confirmed in a larger, independent cohort, prior to being incorporated into clinical practice. Also, it remains to be investigated in a comparison of signal analysis among models; the more recent convolutional or residual LSTM [73], for instance, could improve the performance of the presented model, and this could be the subject of future investigations. A further difficulty is that our work did not consider the patient's ability to tolerate DIBH.