## 4 Discussion
#### Limitations  
We must underline that the model showed suboptimal performance in patients with multiple abnormal PGs in both the retrospective and the prospective data set. This shortcoming should be addressed with better tuning of the Grad-CAM++ algorithm and more data containing multiple findings. The false-positive findings of the model (FPR = 11.2%) were comparable to those of the human reader (10.0%) and were mainly attributed to thyroid nodules. This level of FPR is instead an inherent drawback of MBI scintigraphy, particularly in geographic areas with a high prevalence of thyroid modularity, such as Greece, than a flaw of the model.  
Future studies must involve further external testing using more surgical and histological verification cases. However, to achieve better results in parathyroidectomy cases, the training datasets must be populated with surgically verified samples. The absence of such cases in the training set of the current study is a limitation.  
In addition, the network's robustness to image acquisition device variation is currently questionable. Clinical and demographic information integration may improve precision and offer a more holistic approach. However, the actual influence of clinical and demographic factors is ambiguous. These factors are not expected to improve the localisation precision. However, they can improve the per-patient classification, distinguishing between normal and abnormal incidents.  
Finally, the scarcity of related works available for comparison is an unavoidable limitation of the current study. The limited number of studies focusing on the same research area reduces the opportunity for direct comparison of findings and validation of results. Furthermore, the existing studies often employ different datasets, lacking a global standard dataset, which makes it challenging to establish a fair and consistent benchmark. Additionally, variations in image acquisition techniques and devices utilised in these studies introduce further complexity in drawing accurate comparisons. Consequently, the absence of a standardised dataset and consistent imaging devices hinders the ability to perform a comprehensive and unbiased evaluation across different methodologies. Therefore, caution  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** & **Reference** & **Evaluation Data Size** & **Patient-Level Performance** & **PG-Level Performance** \\ \hline [PERSON] et al. & [22] & 632 & 96.56\% accuracy & - \\ [PERSON] et al. & [22] & 50 (operated) & 92\% accuracy & - \\ [PERSON] et al. & [23] & 632 & 94.8\% accuracy & 76.5\% accuracy \\ [PERSON] et al. & [23] & 472 (operated) & 84.51\% accuracy & 59.43\% accuracy \\ [PERSON] et al. & [31] & 44 & - & 83\% sensitivity \\  & & 94.1\% accuracy in distinguishing & & \\ [PERSON] et al. & [32] & 2010 & between single-gland and & \\  & & & multi-gland adenoma & \\ [PERSON] et al. & [33] & 47 & - & 95.7\% precision, \\  & & & & 90.5\% recall \\ [PERSON] et al. & [34] & 90 & - & 89\% precision \\  & & & & and recall \\ This study & & 648 & 98.61\% accuracy & 88.31\% \\ This study & & 100 (external-operated) & 99\% sensitivity & 87.3\% sensitivity \\ \hline \hline \end{tabular}
\end{table}
Table 12: Comparisons with the recent literature. ACC: accuracy, SEN: sensitivity.  
should be exercised when interpreting the results of this study in the broader context of the field, and further research with a more extensive and standardised dataset is warranted to facilitate a more fair and reliable comparison among different approaches.