Embedded AI system for interactive vision screen based on human action recognition.

In recent years, vision screening has emerged globally for employment (on a yearly basis) within primary and high schools since myopia heavily affects school-aged children. However, this is a laborious and time-consuming task. This article proposes an intelligent system for "self-service" vision screening. Individuals can accomplish this task independently-without any assistance by technical staff. The technical solution involved within this platform is human action recognition realized by pose estimation (real-time human joint localization in images, including detection, association, and tracking). The developed system is based on a compact and embedded artificial intelligence platform, aided by a red-green-blue-D sensor for ranging and pose extraction. A set of intuitive upper-limb actions was designed for unambiguous recognition and interaction. The deployment of this intelligent system brings great convenience for large-scale and rapid vision screening. Implementation details were extensively described, and the experimental results demonstrated efficiency for the proposed technique.

Segmentation of human aorta using 3D nnU-net-oriented deep learning.

Computed tomography angiography (CTA) has become the main imaging technique for cardiovascular diseases. Before performing the transcatheter aortic valve intervention operation, segmenting images of the aortic sinus and nearby cardiovascular tissue from enhanced images of the human heart is essential for auxiliary diagnosis and guiding doctors to make treatment plans. This paper proposes a nnU-Net (no-new-Net) framework based on deep learning (DL) methods to segment the aorta and the heart tissue near the aortic valve in cardiac CTA images, and verifies its accuracy and effectiveness. A total of 130 sets of cardiac CTA image data (88 training sets, 22 validation sets, and 20 test sets) of different subjects have been used for the study. The advantage of the nnU-Net model is that it can automatically perform preprocessing and data augmentation according to the input image data, can dynamically adjust the network structure and parameter configuration, and has a high model generalization ability. Experimental results show that the DL method based on nnU-Net can accurately and effectively complete the segmentation task of cardiac aorta and cardiac tissue near the root on the cardiac CTA dataset, and achieves an average Dice similarity coefficient of 0.9698 ± 0.0081. The actual inference segmentation effect basically meets the preoperative needs of the clinic. Using the DL method based on the nnU-Net model solves the problems of low accuracy in threshold segmentation, bad segmentation of organs with fuzzy edges, and poor adaptability to different patients' cardiac CTA images. nnU-Net will become an excellent DL technology in cardiac CTA image segmentation tasks.