# Deep Learning for Point-of-Care Ultrasound Image Quality Enhancement: A Review  
[PERSON]  
1 Image-Guided Surgery, Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands; [EMAIL_ADDRESS] (H.G.A.v.d.P.); [EMAIL_ADDRESS] (L.M.v.K.); [EMAIL_ADDRESS] (M.W.)  
[PERSON]  
1 Image-Guided Surgery, Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands; [EMAIL_ADDRESS] (H.G.A.v.d.P.); [EMAIL_ADDRESS] (L.M.v.K.); [EMAIL_ADDRESS] (M.W.)  
[PERSON]  
1 Image-Guided Surgery, Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands; [EMAIL_ADDRESS] (H.G.A.v.d.P.); [EMAIL_ADDRESS] (L.M.v.K.); [EMAIL_ADDRESS] (M.W.)  
[PERSON]  
1 Image-Guided Surgery, Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands; [EMAIL_ADDRESS] (H.G.A.v.d.P.); [EMAIL_ADDRESS] (L.M.v.K.); [EMAIL_ADDRESS] (M.W.)  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
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or edges of structures, and increased noise levels [6; 13; 14; 15]. Despite the advancements in POCUS technology in recent years, a tradeoff remains between imaging quality and the benefits of cost and portability [16; 14; 17].  
Efforts to enhance the quality of POCUS can be categorized into three main approaches. The first approach involves advancements in hardware. However, this approach is constrained by rising costs or compromised portability. Another option for quality improvement involves refinements in the ultrasound beamforming algorithm [18; 19]. Nevertheless, the accessibility of raw radio frequency (RF) signals required for these improvements is limited in most commercial ultrasound systems. Therefore, this systematic review opts to center its focus on a third alternative: modifications to the image post-processing methods, eliminating the need for hardware remodeling or operations on the raw RF signal.  
Traditional post-processing techniques, such as filtering and deconvolution, have been employed for ultrasound image enhancement for some time, as described in the review by [PERSON] et al. [20]. Over the last few years, deep learning has emerged as a powerful tool, achieving state-of-the-art performance in various image processing tasks, including image quality enhancement [21; 22; 23]. [PERSON] et al. recently conducted a systematic survey on existing state-of-the-art image enhancement techniques, including deep learning [24]. However, to the best of the authors' knowledge, there has been no recent literature review on the current status of deep learning-based image enhancement specifically focusing on ultrasound. This gap in the literature presents a compelling area for investigation, particularly given the affordability and flexibility of POCUS, alongside its inherent challenges related to image quality.  
The aim of this systematic review is, therefore, to explore the current state-of-the-art progress in ultrasound image enhancement using deep learning for point-of-care ultrasound applications. In this review, we will categorize the quality enhancement methods used in the selected articles, provide an overview of the improvements in performance achieved by these methods, and assess the practical benefits of these deep learning algorithms in enhancing ultrasound image quality for clinical practice.