## 4 Discussion  
Nowadays, although ATTRwt is increasingly diagnosed [20], the gold standard for the diagnosis of amyloid cardiomyopathy type still requires an EMB, which remains an invasive and potentially complicated procedure. Nevertheless, [PERSON] et al. [11] have reported in a large multicentric cohort of patients the potential of bone scintigraphy for the diagnosis of cardiac ATTR, showing >99% sensitivity and 86% specificity considering a visual score \(\geq\)2 according to [PERSON] et al. [13], with specificity reaching 100% when excluding the AL form [11]. In addition, as shown by [PERSON] et al. [21], the use of NM imaging semiquantitative indexes can enable a better differentiation between cardiac amyloidosis subtypes. They reported a significantly higher semiquantitative cardiac uptake in ATTR patients than in the AL cohort using \({}^{99m}\)Tc-PYP as a bone-seeking radiotracer. Lately, [PERSON] et al. [22] used H/WBr and other metrics in \({}^{99m}\)Tc-HMDP bone scintigraphy to discriminate ATTR and AL patients in a heterogeneous cohort of 76 patients. Nevertheless, the use of semiquantitative NM indexes might require a time-consuming procedure, depending on the available referring workstation tools, as the drawing of ROIs is needed on both anterior and posterior views to compute the GM [9]. The GM method could ease the calculation of semiquantitative NM indexes as it requires fewer operations by the NM physician, while its correlation with previously published data that adopted the CM is still strong [23]. In this work, the GM was validated in a cohort of ATTR patients and implemented in a novel assisted workflow developed in MIM that further speeds up and facilitates the process and makes it more reproducible among different users. The validation of the GM with respect to the CM was performed in terms of Bland-Altman analysis, which basically reports the systematic over- or underestimation (in terms of positive or negative bias) and the possible deviation (in terms of 95%CI) when the same quantity is estimated with two alternative methods. Although GM and CM were in good agreement when Hr, WBr, and H/WBr were evaluated in the referring workstation, both the bias and the 95%CI were further reduced with the assisted workflow. This is mainly because each ROI was automatically mirrored and positioned from the anterior image to the posterior and geometric mean images, thus reducing the uncertainty due to ROI positioning or recontouring. The residual bias showed that GM systematically overestimated Hr and H/WBr and, conversely, underestimated WBr, although this was negligible in terms of clinical content. This can be ascribed to the properties of the geometric mean operation, which assigns zero value to a pixel if its value is null in either the anterior or posterior image, which is more frequent in the low-uptake areas (e.g., soft tissues) than in the heart. The same characteristic is likely to explain why the Bland-Altman Plot shows that  
Figure 8: Confusion matrixes of the (**a**) LASSO model, (**b**) Random Forest model, and (**c**) Neural Network model for the validation set. For the LASSO and Neural Network models, only the best cut-off is reported. the absolute difference between the two methods increases as the average value increases. Although the reported difference was deemed not clinically relevant (the highest reported difference was 0.2% over a mean of 9.5%, that means an H/WBr of 9.6% with the GM versus 9.4% with the CM), the fact that the discrepancy between the GM and the CM was more relevant when the radiopharmaceutical accumulation in the heart was higher should be considered. The Hr, WBr, and H/WBr data obtained on a large cohort of 154 patients were used to compare ATTRwt and ATTRm populations. ATTRwt showed statistically significantly higher Hr and H/WBr, while a trend of lower WBr in ATTRwt patients was observed. This data is in disagreement with [PERSON] et al.