# New Approaches to AI Methods for Screening Cardiomegaly on Chest Radiographs
## 4 Discussion  
The lung and heart segmentation method used in our study had good Dice coefficients in both the training and testing datasets[PERSON] Using U-net for the training set, the Dice coefficient for the lung was 0.984 \(\pm\) 0.003, while for the heart this value was 0.983 \(\pm\) 0.004. For the testing set, the Dice coefficient for the lung was 0.970 \(\pm\) 0.012, while for the heart this value was 0.950 \(\pm\) 0.021. These findings [PERSON] with our previous observations, confirming that the implementation of U-net within the nnUNet framework delivers superior segmentation accuracy and reliability [27][PERSON] Similarly to our results, [PERSON] et al. developed and evaluated two AI segmentation models using the U-Net architecture, with performance assessed through Dice coefficients compared to manual segmentations conducted by a radiologist [28]. The initial model was trained on 247 open-source images with segmentation labels for the right lung, left lung, and heart. To enhance accuracy, the advanced model utilized additional training data from newly screened and labeled segmentation images. The initial model achieved Dice coefficients of 0.96 \(\pm\) 0.02 for the heart and 0.98 \(\pm\) 0.01 for the lung, while the advanced model showed improved performance, achieving Dice coefficients of 0.97 \(\pm\) 0.02 for the heart and 0.99 \(\pm\) 0.01 for the lung. Additionally, it has been demonstrated that the AI method can improve an individual radiologist's cardiomegaly F1 score (a machine learning metric used to assess the predictive skill of a model) when compared to direct manual measurements (0.845 to 0.851) [29]. Previous studies have found that CTR calculations using the AI method for lungs and heart segmentations are related to lower inter-observer bias and variation than direct manual measurements (CV and bias: 1.72% vs. 2.13% and \(-\)0.61 vs. \(-\)1.62) [8]. In our study, the ICC values for the CTR and TCD measurements calculated from the automatic segmentation and averaged manual measurements were consistently high, exceeding 0.95 in most comparisons. This supports the assertion that AI-based segmentation methods provide reliable and reproducible results, comparable to the aggregated expertise of radiologists. The direct manual measurements by individual radiologists showed noticeable differences, as reflected in lower ICC values and significant _p_-values in some comparisons. This highlights the inherent subjectivity and variability of manual interpretations, underscoring the advantage of AI in ensuring consistent measurements. The inter-variability segmentation metrics, made by the two radiologists, show minimal differences in the Dice coefficients and Hausdorff distances across readers for the lung segmentation. For the heart segmentation, although the Hausdorff distances were slightly higher due to its complex boundaries, the overall agreement remained strong. These results validate the use of manual segmentation as a dependable benchmark for assessing AI performance. So far, studies have found that CTRs determined with AI can identify cardiomegaly with varying accuracy. [PERSON] et al. found that the classification performance test of the AI-only method was not superior to direct manual measurements [8]. On the other hand, [PERSON] et al. and [PERSON] et al. demonstrated that the sensitivity of the AI method was significantly better than the manual method [30; 31]. [PERSON] et al. developed