Coronary artery disease (CAD) is a type of heart disease, where echocardiography can be used in the diagnosis. Due to the time-consuming and non-reproducibility of manual assessment, automatic evaluation methods are increasingly required where the regional wall segmentation is a crucial step. Currently, most studies prioritize designing sophisticated networks, yet overlooking the fact that the poor segmentation performance comes from the inherent fuzziness and low contrast of echocardiography. In this paper, a framework named MGS-EP is proposed. Inspired by clinical annotation practices where experts infer missing wall structures using anatomical knowledge, this expert intuition is formalized as the Expert Prior (EP). The proposed MGS-EP integrates EP constraints to compensate for image degradation, which consists of a pre-decoupler and a mask-guided segmentation (MGS) network. Where the pre-decoupler first models the EP, generating pseudo masks that encapsulate both complete topology and approximate spatial localization of the regional walls. These pseudo masks are subsequently concatenated with raw echocardiography to form a composite input for the MGS network, thereby enabling regional wall segmentation with topological integrity. The concatenation of pseudo masks with original echocardiography as input to the MGS network serves two purposes: mitigating regional walls' contour degradation due to fuzziness while embedding EP topological constraints. Experimental results demonstrate that, compared to the baseline nnU-Net, the MGS-EP enables topologically continuous segmentation, and can achieve an average improvement of 7.76% in Dice and a reduction of$\mathbf{1 1. 6 1}$pixels in Hausdorff Distance.

The first ACM/IEEE TinyML Design Contest (TDC) held at the 41st International Conference on Computer-Aided Design (ICCAD) in 2022 is a challenging, multimonth, research and development competition. TDC’22 focuses on real-world medical problems that require the innovation and implementation of artificial intelligence/machine learning (AI/ML) algorithms on implantable devices. The challenge problem of TDC’22 is to develop a novel AI/ML-based real-time detection algorithm for life-threatening ventricular arrhythmia (VA) over low-power microcontrollers utilized in implantable cardioverter-defibrillators (ICDs). The dataset contains more than 38000 5-s intracardiac electrograms (IEGMs) segments over eight different types of rhythm from 90 subjects. The dedicated hardware platform is NUCLEO-L432KC manufactured by STMicroelectronics. TDC’22, which is open to multiperson teams world-wide, attracted more than 150 teams from over 50 organizations. This article first presents the medical problem, dataset, and evaluation procedure in detail. It further demonstrates and discusses the designs developed by the leading teams as well as representative results. This article concludes with the direction of improvement for the future TinyML design for health monitoring applications.

For the biomedical transceiver, the data transmission is often asymmetric. At the downlink, the transceiver only needs to receive a simple command to control the operation of the external device, and the receiving data rate is low, about hundreds of Kb/s. However, data collected by external devices such as temperature sensors, pressure sensors, or cameras are often very large, which results in a transmitting data rate of several Mb/s. Therefore, a high energy-efficient modulator is needed. Compared with conventional digital modulator, analog modulator circuits have demonstrated superior energy efficiency at high data rates. This article presents a quasi-digital quadrature phase-shift keying (QPSK) modulator design realized by pure analog circuits which follows a logic design flow. The simulation results show that the system can generate a stable carrier of 64 MHz that meets intra-body communications (IBCs) requirements with a data transmission rate of 10 Mb/s. When the signal-to-noise ratios (SNRs) of the Gaussian channel is 14 dB, it can still maintain a bit error rate (BER) below 10 4 .