## 3 Results  
The cohort comprised 153 patients, including 98 and 55 individuals in the internal and external datasets, respectively. Overall, 82.4% (126/153) were male, and 17.6% (27/153) were female, with a median age of 77 years (range: 58-105). For the internal dataset, median follow-up was 3.1 years (range: 0.48-14.8), with a median of 7 ultrasound scans per patient (range: 2-36). Among these patients, 3.3% (5/153) underwent open AAA repair, 0.7% (1/153) had EVAR, and 96.1% (147/153) had no intervention; no AAA ruptures occurred. Of the external dataset, only data relating to open AAA repair was available, reported in 21% (12/55) of cases. Additional details, including demographics and scanner usage, are listed in Table 3a,b. The training dataset included 69 patients from the internal dataset, while the testing datasets comprised 29 internal and 55 external patients. Gender distributions were consistent across groups, showing no significant differences. However, there was a difference in age between the external testing dataset and the training dataset. No significant differences were found between the training and internal testing datasets for follow-up characteristics. Table 4 outlines the demographic differences between training and testing datasets. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Total** & **Internal** & **External** \\ \hline No. of Patients & 153 & 98 & 55 \\ Male/female & 126/27 & 77/21 & 49/6 \\ Median Age, years & 77 (range: 58–105) & 76 (range: 61–92) & 80 (range: 58–105) \\ Median follow-up time, years & N/A 1 & 3 years (range: 0–15) & N/A 1 \\ Median number of follow-up & N/A 1 & 7 (range: 2–36) & N/A 1 \\ ultrasound scans & & & \\ Progression to open repair & 17 & 5 & 12 \\ Progression to EVAR & N/A 1 & 1 & N/A 1 \\ Stable—no intervention & N/A 1 & 147 & N/A 1 \\ \hline \multicolumn{4}{c}{**(b)**} \\ \hline
**Scanner Model** & & **Count** \\ \hline GE Healthcare Discovery 710 & & 54 \\ GE Healthcare Discovery 690 & & 13 \\ GE Healthcare Discovery STE & & 9 \\ Philips Gemini TF64 & & 22 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: (**a**): Patient Demographics; (**b**) Scanner Breakdown Table (Internal Data Only). A total of 93 radiomic features were extracted per patient using PyRadiomics, forming the dataset used for model training and validation. The regression-MLP models trained across ten-fold cross-validation experiments were evaluated on unseen internal test samples. The statistical analysis, which assessed prediction accuracy, included calculations of the mean absolute error (MAE) and its standard error of the mean (SEM), with the model achieving an MAE of 1.35 \(\pm\) 3.2e\(-\)4 mm/year. The average training and validation curves for loss and accuracy, with prediction error standard deviations (computed across all experiment folds) are shown in Figure 4a. To refine the models, features with least significance were eliminated using RF-based RFE which yielded 29 features. As before, regression-MLP models were trained across  
* \({}^{1}\) N/A: not available; \({}^{2}\) significant difference as indicated by \(p\) \(>\) 0.05  
\end{table}
Table 4: Training and Testing Demographics: Z-Scores and _p_-Values. Figure 4: **(a)** Average training and validation curves for loss and accuracy for the reduced feature set, (**b**) prediction error standard deviations for the reduced feature set, (**c**) average training and validation curves for loss and accuracy for the full feature set, and (**d**) prediction error standard deviations for the full feature set. ten-fold cross-validation experiments using these reduced sets of features. The models were evaluated on the same set of unseen internal test samples as previously, resulting in a near identical average accuracy of 1.35 \(\pm\) 2.5e\(-\)4 mm/year. The average training and validation curves for loss and accuracy, with prediction error standard deviations (computed across all experiment folds) are illustrated in Figure 4c.