# New Approaches to AI Methods for Screening Cardiomegaly on Chest Radiographs
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Comparison**} & **Dice Lung** & **Dice Heart** & **Hausdorff Lung** & **Hausdorff Heart** \\  & **(Mean \(\pm\) sd, Min–Max)** & **(Mean \(\pm\) sd, Min–Max)** & **(Mean \(\pm\) sd, Min–Max)** & **(Mean \(\pm\) sd, Min–Max)** \\ \hline Reader 1 vs. Reader 2 & 0.97 \(\pm\) 0.01 (0.95–0.98) & 0.96 \(\pm\) 0.02 (0.90–0.98) & 0.05 \(\pm\) 0.24 (0.00–1.41) & 2.30 \(\pm\) 4.40 (0.00–16.28) \\ \hline Reader 1 vs. Reader 3 & 0.97 \(\pm\) 0.01 (0.93–0.98) & 0.95 \(\pm\) 0.03 (0.79–0.98) & 0.20 \(\pm\) 0.80 (0.00–5.00) & 3.78 \(\pm\) 8.68 (0.00–43.42) \\ \hline Reader 2 vs. Reader 3 & 0.97 \(\pm\) 0.01 (0.94–0.98) & 0.95 \(\pm\) 0.03 (0.82–0.98) & 0.12 \(\pm\) 0.46 (0.00–2.83) & 3.99 \(\pm\) 8.50 (0.00–43.74) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Inter-variability segmentation metrics made by the two radiologists. CardioXNet, which is based on the DenseNet neural network and was trained on 103 CXRs and expanded to 2630 images through data augmentation [32]. This model achieved an accuracy of 93%, a F1 score of 94%, and a precision of 100%. In another study, the Inception V3 model was trained on 1026 CXRs from Kyungpook National University Hospital and achieved 96% accuracy [33]. [PERSON] et al. demonstrated that the DenseNet121 algorithm achieved good results when used in the development of an AI model for cardiomegaly detection, with an accuracy of 95%, a recall of 91%, and an F1 score of 94% [34]. Our study revealed that the sensitivity of the CTR and TCD measurements derived from both the automatic and manual segmentations were greater than those derived from direct manual measurements while maintaining satisfactory specificity. This suggests that the AI method may be used as a suitable tool in screening for cardiomegaly on CXRs. In our study, according to a different definition of cardiomegaly based on CMR imaging, accuracy for the CTR measurements ranged from 62.0 to 74.0% for automatic segmentation (for TCD it ranged from 64.0 to 72.0%). The differences regarding the accuracy across studies may result from the fact that the performance of the AI algorithms used for segmentation depends on their ability to correctly evaluate heart and lung boundaries. The segmentation of the lungs and heart may be challenging because of the low-quality images and low pixel variation, and some algorithms may give more precise segmentations than others. Additionally, differences in the studied samples may be significant. In our study, we assessed patients with various cardiovascular diseases, including patients with cardiovascular implanted electronic devices, which may make proper segmentation difficult and may lower the performance of CTR and TCD measurements. Moreover, unlike other studies, we compared the diagnosis of cardiomegaly established with CTR and TCD to the diagnosis established using the gold standard method--CMR. Taken together, this might explain the lower values of accuracy in cardiomegaly detection using the radiographic parameters achieved in our population when compared to other studies. Furthermore, our study demonstrated that the mean CTR and TCD measurements were slightly greater in the AI model (both using the manual and automatic segmentation) than in the direct manual reading. We also observed that with the direct manual measurements, many more false negative results were found, while with the AI model, more false positive results were noted. This further supports the fact that the AI method may be better as a screening tool, since more cases of cardiomegaly may be found using AI algorithms. Some differences in the CTR and TCD values calculated from the manual reading and automatic segmentation may result from several circumstances that could obscure the borders of the heart and lungs on CXRs. Sometimes, the heart border may be partially hidden behind the mediastum. On the other hand, pulmonary disease, including atelectasis, consolidations, and parenchymal scarring-fibrosis or vascular congestion may obscure the heart border [35].