Treatment and referral experiences of patients with type A aortic dissection and their families: a qualitative study.

OBJECTIVES: This study aimed to analyse the experiences and feelings of patients with type A aortic dissection (TAAD) and their families during the medical treatment and referral process, investigate the entire process's needs and problems and provide evidence for improving the aortic dissection treatment system. DESIGN: A qualitative descriptive design using a phenomenological study. Face-to-face semistructured interviews were conducted. Thematic analysis was used to analyse the interview data, which was transcribed verbatim. SETTING: Department of Cardiovascular Surgery of Shanxi Bethune Hospital in China. PARTICIPANTS: Fifteen family groups, consisting of patients with TAAD who underwent surgical treatment and their families, were selected. RESULTS: Three primary themes were discussed and developed. Theme 1: the experiences of medical treatment and referral (confusion at the onset; complex inner feelings and emotional expressions of the medical treatment and referral; preoperative inner conflict); theme 2: problems with the TAAD medical treatment system (the quality of diagnosis and medical treatment needs to be improved; deficiency of medical system policies and procedures); and theme 3: real demands (demands for TAAD-related knowledge and access to the disease; economic-related demands). CONCLUSION: Patients with TAAD and their families encounter complex inner experiences, multiple requirements and numerous challenges during the medical treatment and referral process. It is advised that the treatment and referral system of TAAD in China needs to be improved. Future research and clinical practice should standardise diagnosis and treatment training, establish a fast channel for TAAD to prioritise treatment, popularise aortic dissection-related knowledge and improve the funding system.

ConvWin-UNet: UNet-like hierarchical vision Transformer combined with convolution for medical image segmentation.

Convolutional Neural Network (CNN) plays a vital role in the development of computer vision applications. The depth neural network composed of U-shaped structures and jump connections is widely used in various medical image tasks. Recently, based on the self-attention mechanism, the Transformer structure has made great progress and tends to replace CNN, and it has great advantages in understanding global information. In this paper, the ConvWin Transformer structure is proposed, which refers to the W-MSA structure in Swin and combines with the convolution. It can not only accelerate the convergence speed, but also enrich the information exchange between patches and improve the understanding of local information. Then, it is integrated with UNet, a U-shaped architecture commonly used in medical image segmentation, to form a structure called ConvWin-UNet. Meanwhile, this paper improves the patch expanding layer to perform the upsampling operation. The experimental results on the Hubmap datasets and synapse multi-organ segmentation dataset indicate that the proposed ConvWin-UNet structure achieves excellent results. Partial code and models of this work are available at https://github.com/xmFeng-hdu/ConvWin-UNet.