# A Development and Validation of an AI Model for Cardiomegaly Detection in Chest X-rays
## 2 Related Research
### Application to Cardiomegaly  
The standard diagnostic indicator of cardiomegaly on chest radiographs is the cardiothoracic ratio (CTR), which is the ratio of the maximum transverse diameter of the heart to the maximum width of the chest measured at the rib angle. A CTR of 0.5 or greater is considered indicative of cardiomegaly.  
In 2018, Que Q. published \"CardioXNet: Automated Detection for Cardiomegaly Based on Deep Learning\", which detailed the development of CardioXNet using the DenseNet neural network to determine the presence of cardiomegaly in chest X-ray images [10]. The study utilized 103 standard PA radiographs. Due to the limited number of images, data augmentation techniques were utilized to increase the total number of images to 2630. The trained model achieved an accuracy of 0.93, an F1 score of 0.94, and a precision of 1.0 [10]. This case illustrates the use of data augmentation techniques when data are insufficient. In another instance, the Inception V3 model was utilized to integrate other deep learning algorithms into the medical field. Utilizing 1026 chest X-rays from Kyungpook National University Hospital for training, the model achieved an accuracy of 0.96 and a loss of 0.22. Future research is expected to achieve excellent results by utilizing more diverse medical imaging data [11]. There is no clear standard for the amount of data required to achieve high performance when training deep learning algorithms. However, previous studies have indicated a trend towards the use of datasets comprising at least 1000 images. In 2021, [PERSON] used a total of 1026 chest X-ray images (526 normal and 500 with cardiomegaly) [12]. The accuracy rates for the training and testing phases were 0.92 and 0.92, respectively. Furthermore, to evaluate the model's performance with varying data quantities, data augmentation was conducted 30-fold and 60-fold. The application of data augmentation, conducted 60-fold, resulted in a training and testing accuracy of 0.99 and 0.95, respectively. This indicates that data availability is positively correlated with training accuracy [12]. In 2023, [PERSON] employed a chest X-ray dataset retrospectively obtained from two major hospitals in Vietnam. A total of 15,000 images were used for training, while the remaining 3000 constituted the test set. Of these, 230 images were identified as cardiomegaly exhibiting and 1003 images were classified as normal. The algorithm used was ResNet50-v2, a model frequently utilized for cardiomegaly classification. It commenced with ImageNet weights and underwent fine-tuning on the individual dataset. The results demonstrated accuracy of 0.91, a precision of 0.74, and an F1 score of 0.79 for the model [13]. In 2020, [PERSON] et al. published \"Cardiomegaly Detection on Chest Radiographs: Segmentation Versus Classification\", highlighting the significant research focused on predicting specific abnormalities in chest X-rays [14]. However, the study points out that image-level labels, which refer to the entire image, do not provide detailed information about the shape or location of these abnormalities. This method, often described as a \"black-box\" system, is challenging to interpret and may not be suitable for clinical settings. Therefore, it emphasizes the need for more intuitive and interpretable models that provide insights into the reasoning behind inference [14].