Joint replacement is a frequently performed surgical intervention to address end-stage joint ailments. However, successful surgery relies on early diagnosis and appropriate treatment of joint diseases. With the advancement of machine learning technology and its integration with the medical field, the use of machine learning technology in joint replacement has made substantial advancements. This article will explore the implementation and current standing of machine learning in joint replacement from four aspects: preoperative auxiliary diagnosis of arthritis, preoperative auxiliary decision-making, postoperative complication diagnosis and postoperative prediction.
Sleeping is a vital biological state which help maintaining the homeostasis of organisms of all biological lives. A full sleep can be divided into different repeating stages, Rapid Eye Movement sleep (REM) stage, Non-Rapid Eye Movement Sleep (NREM) stage one to four. An effective sleep staging system can help patients improving their sleep quality. In the past, patients are required to wear Polysomnogram (PSG) for the whole night to collect signals like Electroencephalogram (EEG), Electrooculogram (EOG), Electromyogram (EMG) for diagnosis. And the traditional sleep staging system use one or more signals above to predict a sleep stage. In this paper, we introduce a new sleep staging algorithm based on machine learning. Our model has two main inputs: patients’ respiratory signal and their physical data, like age, gender, and weight. The strategy is to use two CNNs to extract features from raw respiratory signal in time domain and frequency domain, several Word2vec layers are built to extract features from patients’ meta data and a transformer encoder to collect all the features. Using the MIT-BIH Polysomnographic database, our model achieves a result of 81.96% accuracy. This shows that it is completely feasible to classify patients’ sleep stage with their respiratory signal and meta information.
The rapid development of Machine Learning (ML), Machine Vision and imaging technology has greatly promoted medical imaging and Intelligent Medical Engineering. Radiomics combines medical imaging with Big Data, Machine Learning and other technologies to realize the diagnosis and treatment of Corona Virus Disease 2019 (COVID-19) by obtaining and analyzing lung image characteristics. This paper systematically reviews the realization process of radiomics in COVID-19, the latest research on radiomics in COVID-19's diagnosis, classification and prognosis, as well as the problems and challenges faced in this research field. By and large, radiomics provides great potential and application value in the diagnosis, classification and prognosis of COVID-19. It makes up for the deficiency of doctor's diagnosis and Reverse Transcription-Polymerase Chain Reaction (RT-PCR) test, and provides an effective and feasible method for improving the diagnosis and treatment level of COVID-19 with low cost, high efficiency and accuracy.
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