Biomedical Engineering's Internet of Medical Things (IoMT) is helping to
improve the accuracy, dependability, and productivity of electronic equipment
in the healthcare business. Real-time sensory data from patients may be
delivered and subsequently analyzed through rapid development of wearable IoMT
devices, such as neuro-stimulation devices with a range of functions. Data from
the Internet of Things is gathered, analyzed, and stored in a single location.
However, single-point failure, data manipulation, privacy difficulties, and
other challenges might arise as a result of centralization. Due to its
decentralized nature, blockchain (BC) can alleviate these issues. The viability
of establishing a non-invasive remote neurostimulation system employing
IoMT-based transcranial Direct Current Stimulation is investigated in this work
(tDCS). A hardware-based prototype tDCS device has been developed that can be
operated over the internet using an android application. Our suggested
framework addresses the problems of IoMTBC-based systems, meets the criteria of
real-time remote patient monitoring systems, and incorporates literature best
practices in the relevant fields.

本研究基于生物医学工程的医疗互联网事物（IoMT）技术，提出了一种基于区块链的非侵入式远程神经刺激系统，通过可穿戴 IoMT 设备实时收集和分析患者的传感数据，并解决了集中式系统可能遇到的单点故障、数据操纵和隐私等问题。

基于区块链的物联网与远程患者监测的神经刺激装置安全框架

IoMT-Blockchain based Secured Remote Patient Monitoring Framework for  Neuro-Stimulation Device

The science of solving clinical problems by analyzing images generated in
clinical practice is known as medical image analysis. The aim is to extract
information in an effective and efficient manner for improved clinical
diagnosis. The recent advances in the field of biomedical engineering has made
medical image analysis one of the top research and development area. One of the
reason for this advancement is the application of machine learning techniques
for the analysis of medical images. Deep learning is successfully used as a
tool for machine learning, where a neural network is capable of automatically
learning features. This is in contrast to those methods where traditionally
hand crafted features are used. The selection and calculation of these features
is a challenging task. Among deep learning techniques, deep convolutional
networks are actively used for the purpose of medical image analysis. This
include application areas such as segmentation, abnormality detection, disease
classification, computer aided diagnosis and retrieval. In this study, a
comprehensive review of the current state-of-the-art in medical image analysis
using deep convolutional networks is presented. The challenges and potential of
these techniques are also highlighted.

研究医学图像分析的最新技术，特别是使用机器学习中的深度卷积网络实现医学图像分析，深度卷积网络被广泛应用于医学图像分割、异常检测和疾病分类等应用领域。