# Deep Learning for Point-of-Care Ultrasound Image Quality Enhancement: A Review
## 4 Discussion  
Point-of-care ultrasound (POCUS) is recognized for its affordability and convenience, but it suffers from a lower image quality compared to conventional high-end, cart-based ultrasound systems. Recent advances in deep learning have achieved state-of-the-art performance in various problems of image processing, including the enhancement of image quality. This systematic review provides a overview of research focused on ultrasound image enhancement using deep learning methods, which is suitable for real-time POCUS applications. A comprehensive description of the methods used, as well as a further analysis of the performance of the proposed algorithms, is given. It was observed that the majority of studies utilized GANs incorporating CNNs in both the generator and discriminator networks. The emergence of GANs in the medical  
Figure 4: Forest plots of (**a**) the mean PSNR difference (95% CI) and (**b**) the mean SSIM difference (95% CI). imaging field, as described by [PERSON] et al. [23], is noteworthy, as these models are capable of generating highly realistic medical images, effectively bridging the gap between supervised learning and image generation. Despite this trend, there was considerable variation in the GAN and CNN architectures, loss functions, and evaluation methods across studies. Some studies compared their proposed network with existing networks to benchmark quality enhancement, while others compared the generated images to the original low-quality input images and/or paired high-quality reference images. For the methods that quantitatively assessed the networks' performance, a variety of image quality metrics was reported. In addition to metric-based assessments, some studies incorporated visual assessments or tested the effect of quality enhancement on downstream tasks such as segmentation or diagnosis. Often, a combination of these evaluation methods was utilized to provide a more comprehensive overview of the proposed algorithm's performance. Variability in evaluation methods and performance metrics poses a challenge for direct comparisons among all articles. However, those reporting the most commonly reported performance metrics (PSNR and SSIM) for the low-quality input images or generated images allowed for some comparisons. Figure 3a,b reveal substantial disparities in low-quality input image performance, indicating varying baseline qualities across studies. These differences may be explained by the heterogeneity in ultrasound devices and dataset types. Nevertheless, consistent improvements in image quality were observed when comparing enhanced images to the original inputs, as shown in Figure 3a,b. The meta-analysis further supports these findings, showing a statistically significant increase in the PSNR and SSIM values. This indicates the potential of the proposed deep learning algorithms for enhancing the quality of ultrasound images. However, variability in the extent of performance gain across datasets and articles is notable. This is further supported by the I\({}^{2}\) score of both meta-analyses (I\({}^{2}\) = 100%), indicating high heterogeneity. This variability complicates the determination of achievable quality gain. Notably, simulated datasets generally exhibited higher performance gains compared to in vivo and phantom datasets, suggesting that simulation results may not fully represent clinical scenarios. This review focused on ultrasound enhancement for POCUS applications but included studies for ultrasounds in general, as well to ensure the comprehensive coverage of relevant algorithms. A key selection criterion was the simultaneous addressing of multiple distortion types, which led to the inclusion of 15 articles. Interestingly, despite the importance of computation time for real-time applications, most articles did not report this aspect. Furthermore, the lack of source code availability hinders the reproducibility of the conducted research.