# New Approaches to AI Methods for Screening Cardiomegaly on Chest Radiographs
## 1 Introduction  
Chest radiographs (CXRs) are widely used in everyday clinical practice for various purposes, including diagnostics, screenings, and monitoring of diseases [1; 2; 3]. They are performed for a broad range of indications, including but not limited to respiratory disease (e.g., hemoptysis, investigation of tuberculosis, and pneumonia) and cardiac disease (heart failure and heart enlargement) [1; 2; 3; 4]. The cardiothoracic ratio (CTR) from CXRs is a commonly used parameter for evaluating cardiomegaly, which is diagnosed if CTR > 50% [5; 6]. Transverse cardiac diameter (TCD) is less popular in clinical practice, but is still an important indicator of cardiomegaly [5]. Transverse cardiac diameters of 155 mm for males and 145 mm for females are considered as the normal upper limits [7]. However, the manualassessment of CTR and TCD is related to observer variation and is time-consuming. Therefore, the automatic calculation of CTR and TCD according to traditional methods could be a useful tool in daily clinical practice to improve accuracy and reduce workload [8; 9].  
The use of artificial intelligence (AI) in various aspects of X-ray image analysis is becoming more and more popular, particularly in areas such as segmentation, the automated detection of abnormalities, disease classification, and risk stratification [10; 11; 12; 13; 14]. Artificial intelligence algorithms have demonstrated high accuracy in identifying conditions such as pneumonia, lung nodules, fractures, and tuberculosis [11; 12]. Moreover, AI-powered tools are increasingly being used to prioritize critical cases in busy clinical workflows, generate automated reports, and assist in triaging [13; 14]. Additionally, AI has emerged as a promising tool for assessing cardiomegaly on CXRs, offering the potential to improve diagnostic efficiency and ensure greater consistency in interpretation. The meta-analysis conducted by [PERSON] et al. included 14 studies comprising a total of 70 472 CXR images, evaluating various AI models for cardiomegaly detection [15]. The pooled area under the curve (AUC) for cardiomegaly detection was 0.959 (95% confidence interval [CI]: 0.944-0.975), demonstrating the high diagnostic performance of AI models across diverse study settings. These advances underline the transformative potential of AI in improving diagnostic accuracy, efficiency, and accessibility in medical imaging.  
Using AI, there are two main approaches in the detection of heart enlargement on CXRs [16]. The first is a classification-based approach, which does not give data about the region and stage of the cardiomegaly. In the second approach, the heart and lung fields can be accurately segmented, and then CTR and TCD may be automatically calculated using a simple mathematical algorithm [17]. Segmentation-based methods are mainly based on the U-net model, the most successful convolutional network for biomedical image segmentation [8; 9]. So far, CTR and TCD calculations made from automatic segmentation have mostly been technically validated; however, there is still a need to clinically validate AI algorithms before they can be implemented in routine clinical settings [8; 9; 18].  
Therefore, we aimed to investigate the performance and efficiency of AI in screening for cardiomegaly using the nnUNet framework as a universal medical segmentation environment. Importantly, we compared the results of CTR and TCD measurements to the diagnosis of cardiomegaly established using the gold standard modality--cardiac magnetic resonance (CMR).