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Gandha Satria Adi;Inkyu Park
- Journal of Sensor Science and Technology
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- v.32 no.6
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- pp.378-385
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- 2023
Human biosignals provide essential information for diagnosing diseases such as dementia and Parkinson's disease. Owing to the shortcomings of current clinical assessments, noninvasive solutions are required. Machine learning (ML) on wearable sensor data is a promising method for the real-time monitoring and early detection of abnormalities. ML facilitates disease identification, severity measurement, and remote rehabilitation by providing continuous feedback. In the context of wearable sensor technology, ML involves training on observed data for tasks such as classification and regression with applications in clinical metrics. Although supervised ML presents challenges in clinical settings, unsupervised learning, which focuses on tasks such as cluster identification and anomaly detection, has emerged as a useful alternative. This review examines and discusses a variety of ML algorithms such as Support Vector Machines (SVM), Random Forests (RF), Decision Trees (DT), Neural Networks (NN), and Deep Learning for the analysis of complex clinical data.
https://doi.org/10.46670/JSST.2023.32.6.378 인용 PDF

Hyun Woo Goo
- Korean Journal of Radiology
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- v.20 no.1
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- pp.50-57
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- 2019
In pediatric thoracic CT, respiratory motion is generally treated as a motion artifact degrading the image quality. Conversely, respiratory motion in the thorax can be used to answer important clinical questions, that cannot be assessed adequately via conventional static thoracic CT, by utilizing four-dimensional (4D) CT. However, clinical experiences of 4D thoracic CT are quite limited. In order to use 4D thoracic CT properly, imagers should understand imaging techniques, radiation dose optimization methods, and normal as well as typical abnormal imaging appearances. In this article, the imaging techniques of pediatric thoracic 4D CT are reviewed with an emphasis on radiation dose. In addition, several clinical applications of pediatric 4D thoracic CT are addressed in various thoracic functional abnormalities, including upper airway obstruction, tracheobronchomalacia, pulmonary air trapping, abnormal diaphragmatic motion, and tumor invasion. One may further explore the clinical usefulness of 4D thoracic CT in free-breathing children, which can enrich one's clinical practice.
https://doi.org/10.3348/kjr.2018.0325 인용 PDF