• Tuberculosis and Respiratory Diseases
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• 제86권4호
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• pp.251-263
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• 2023

The stethoscope has long been used for the examination of patients, but the importance of auscultation has declined due to its several limitations and the development of other diagnostic tools. However, auscultation is still recognized as a primary diagnostic device because it is non-invasive and provides valuable information in real-time. To supplement the limitations of existing stethoscopes, digital stethoscopes with machine learning (ML) algorithms have been developed. Thus, now we can record and share respiratory sounds and artificial intelligence (AI)-assisted auscultation using ML algorithms distinguishes the type of sounds. Recently, the demands for remote care and non-face-to-face treatment diseases requiring isolation such as coronavirus disease 2019 (COVID-19) infection increased. To address these problems, wireless and wearable stethoscopes are being developed with the advances in battery technology and integrated sensors. This review provides the history of the stethoscope and classification of respiratory sounds, describes ML algorithms, and introduces new auscultation methods based on AI-assisted analysis and wireless or wearable stethoscopes.

• 센서학회지
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• 제32권6호
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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.

• Journal of Medicine and Life Science
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• 제20권4호
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• pp.141-157
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• 2023

Stroke is a leading cause of disability and death. The condition requires prompt diagnosis and treatment. The quality of care provided to patients with stroke can vary depending on the availability of medical resources, which in turn, can affect prognosis. Recently, there has been growing interest in using machine learning (ML) to support stroke diagnosis and treatment decisions based on large medical data sets. Current ML applications in stroke care can be divided into two categories: analysis of neuroimaging data and clinical information-based predictive models. Using ML to analyze neuroimaging data can increase the efficiency and accuracy of diagnoses. Commercial software that uses ML algorithms is already being used in the medical field. Additionally, the accuracy of predictive ML models is improving with the integration of radiomics and clinical data. is expected to be important for improving the quality of care for patients with stroke.

• International Journal of Computer Science & Network Security
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• 제22권4호
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• pp.203-208
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• 2022

The occurrence of Type 2 Diabetes Mellitus (T2DM) is hoarding globally. All kinds of Diabetes Mellitus is controlled to disrupt over 415 million grownups worldwide. It was the seventh prime cause of demise widespread with a measured 1.6 million deaths right prompted by diabetes during 2016. Over 90% of diabetes cases are T2DM, with the utmost persons having at smallest one other chronic condition in UK. In valuation of contemporary applications of Big Data (BD) to Diabetes Medicare by sighted its upcoming abilities, it is compulsory to transmit out a bottomless revision over foremost theoretical literatures. The long-term growth in medicine and, in explicit, in the field of "Diabetology", is powerfully encroached to a sequence of differences and inventions. The medical and healthcare data from varied bases like analysis and treatment tactics which assistances healthcare workers to guess the actual perceptions about the development of Diabetes Medicare measures accessible by them. Apache Spark extracts "Resilient Distributed Dataset (RDD)", a vital data structure distributed finished a cluster on machines. Machine Learning (ML) deals a note-worthy method for building elegant and automatic algorithms. ML library involving of communal ML algorithms like Support Vector Classification and Random Forest are investigated in this projected work by using Jupiter Notebook - Python code, where significant quantity of result (Accuracy) is carried out by the models.

• Korean Journal of Radiology
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• 제22권3호
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• pp.334-343
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• 2021

Objective: We aimed to develop a prediction model for diagnosing severe aortic stenosis (AS) using computed tomography (CT) radiomics features of aortic valve calcium (AVC) and machine learning (ML) algorithms. Materials and Methods: We retrospectively enrolled 408 patients who underwent cardiac CT between March 2010 and August 2017 and had echocardiographic examinations (240 patients with severe AS on echocardiography [the severe AS group] and 168 patients without severe AS [the non-severe AS group]). Data were divided into a training set (312 patients) and a validation set (96 patients). Using non-contrast-enhanced cardiac CT scans, AVC was segmented, and 128 radiomics features for AVC were extracted. After feature selection was performed with three ML algorithms (least absolute shrinkage and selection operator [LASSO], random forests [RFs], and eXtreme Gradient Boosting [XGBoost]), model classifiers for diagnosing severe AS on echocardiography were developed in combination with three different model classifier methods (logistic regression, RF, and XGBoost). The performance (c-index) of each radiomics prediction model was compared with predictions based on AVC volume and score. Results: The radiomics scores derived from LASSO were significantly different between the severe AS and non-severe AS groups in the validation set (median, 1.563 vs. 0.197, respectively, p < 0.001). A radiomics prediction model based on feature selection by LASSO + model classifier by XGBoost showed the highest c-index of 0.921 (95% confidence interval [CI], 0.869-0.973) in the validation set. Compared to prediction models based on AVC volume and score (c-indexes of 0.894 [95% CI, 0.815-0.948] and 0.899 [95% CI, 0.820-0.951], respectively), eight and three of the nine radiomics prediction models showed higher discrimination abilities for severe AS. However, the differences were not statistically significant (p > 0.05 for all). Conclusion: Models based on the radiomics features of AVC and ML algorithms may perform well for diagnosing severe AS, but the added value compared to AVC volume and score should be investigated further.

• 한국방사선학회논문지
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• 제14권1호
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• pp.23-29
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• 2020

표준섭취계수(SUV) PET과 영상 재구성 방법에 따라 크게 달라진다. 삼성 서울병원 핵의학과에 설치된 GE사의 Discovery MIDR와 Discovery Ste의 영상 재구성 알고리즘을 이용하여 의인화몸통팬텀 내의 심장, 간과 배경영역에서 표준섭취계수를 측정하여 실제 SUV와 비교하였다. 영상 재구성 알고리즘은 MIDR에서는 VPFX-S (TOF+PSF), QCFX-S-350 (Q.Clear+TOF+PSF), QCFX-S-50와 VPHD-S (OSEM+PSF), Ste에서는 VUE Point (OSEM)와 FORE-FBP를 사용하였다. 방사선사의 방사선 피폭을 감소시키기 위하여 소량의 ¹⁸F-FDG 선원을 물과 혼합하였다: 52.5 ml 심장에는 2.28 MBq, 1,290 ml 간에는 20.3 MBq와 9,590 ml 배경영역에는 45.7 MBq을 주입하였다. 심장에서의 표준섭취계수는 MIDR의 VPFX-S, QCFX-S-350, QCFX-S-50, VPHD-S와 Ste의 VUE Point (OSEM)와 FOR-FBP 알고리즘에서 각각 27.1, 28.0, 27.1, 26.5, 8.0와 7.4 이었으며, 기대치는 5.9이었다. 배경역역에서는 4.2, 4.1, 4.2, 4.1, 1.1와 1.2 이었으며, 기대치는 0.8이었다. 각 영역에서 표준섭취계수는 PET과 알고리즘에 따라 크게 차이가 있었지만, 심장과 배경영역의 SUV 비율은 비교적 일정하여 6개 영상 재구성 알고리즘에 대하여 6.5, 6.8, 6.5, 6.5, 7.3와 6.2 이었으며 기대치는 7.8이었다. 심장에서의 평균 신호 대 잡음비(SNR)는 각각 6개 알고리즘에 대하여 8.3, 12.8, 8.3, 8.4, 17.2와 16.6이었다. 결론적으로 PET 성능 평가는 각 영역에서의 절댓값 보다는 두 영역 사이의 SUV 비율이 적절하며, 비교적 소량의 방사능으로도 확인할 수 있는 가능성이 있다.

• 한국임상수의학회지
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• 제40권4호
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• pp.243-259
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• 2023

Machine learning and deep learning (ML/DL) algorithms have been successfully applied in medical practice. However, their application in veterinary medicine is relatively limited, possibly due to a lack in the quantity and quality of relevant research. Because the potential demands for ML/DL applications in veterinary clinics are significant, it is important to note the current gaps in the literature and explore the possible directions for advancement in this field. Thus, a scoping review was conducted as a situation analysis. We developed a search strategy following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed and Embase databases were used in the initial search. The identified items were screened based on predefined inclusion and exclusion criteria. Information regarding model development, quality of validation, and model performance was extracted from the included studies. The current review found 55 studies that passed the criteria. In terms of target animals, the number of studies on industrial animals was similar to that on companion animals. Quantitative scarcity of prediction studies (n = 11, including duplications) was revealed in both industrial and non-industrial animal studies compared to diagnostic studies (n = 45, including duplications). Qualitative limitations were also identified, especially regarding validation methodologies. Considering these gaps in the literature, future studies examining the prediction and validation processes, which employ a prospective and multi-center approach, are highly recommended. Veterinary practitioners should acknowledge the current limitations in this field and adopt a receptive and critical attitude towards these new technologies to avoid their abuse.

• 대한핵의학회지
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• 제38권6호
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• pp.486-491
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• 2004

목적: 요소분석법. 독립성분분석법 등이 PET을 이용하여 심근혈류를 비침습적으로 측정하기 위하여 사용되어 왔다. 이론적으로 뛰어나고 새로운 방법인 앙상블 독려성분분석법을 이용하여 $H_2^{15}O$ 동적 심근 PET데이터의 정량분석방법을 개발하였다. 이 연구에서 사용한 앙상블 독려성분분석법을 이용하여 환자의 혈류를 정량화 하였다. 대상 및 방법: 관동맥질환이 의심되어 관류 SPECT를 시행한 환자 20명을 대상으로 $H_2^{15}O$ 동적 심근 PET을 시행한 후 앙상블 독립성분분석법을 이용하여 심근 독립성분영상을 추출하였으며, 좌심실영역과 심근영역에 대한 영상대조도를 조사하였다. 앙상블 학습은 독립성분과 가중치 행렬에 대한 확률분포를 가정하고 베이지안 이론에 의해서 혼합자료에 대한 확률분포를 추정한다. 이렇게 추정한 혼합자료의 확률분포와 실제 분포간의 차이인 Kullback-Leibler 발산치가 최소가 되도록 독립성분과 가중치 행렬을 순차적으로 변화시켜가며 최종 해를 찾는 방식이다. 이 연구에서 사후확률분포는 동적 핵의학 영상에 적합한 비음성제약조건과 함께 수정된 가우시안 분포를 이용하여 최적화 하였다. 혈류량은 심첨부, 중벽 네 부분, 하벽 네 부분의 9개 영역으로 나누어 측정하였으며, 측정결과에 대해 관류 SPECT 소견과 관동맥조영술의 소견과 비교하였다. 결과: 전체 20명의 휴식기 및 부하기 영상에서 5명을 제외한 15명의 데이터에 대해 심근혈류를 측정할 수 있었다. $H_2^{15}O$ 동적 심근 PET에서 앙상블 독립성분분석법을 이용하여 정량화한 휴식기 혈류량은 $1.2{\pm}0.40$ ml/min/g, 부하기 혈류량은 $1.85{\pm}1.12$ml/min/g이었다. 같은 영역에 대해 두 번 측정했을 때 측정된 심근혈류값의 상관계수는 0.99로 재현성이 높았다. 분리된 독립성분영상에서 영상대조도는 좌심실에 대한 심근영역의 비는 평균 1:2.7이었다. 관동맥 조영술을 시행한 9명에서 협착이 없는 분절과 협착이 있는 분절의 혈류예비능에 유의한 차이가 있었다(P<0.01). 또한, 관동맥조영술에서 협착이 확인된 66분절의 심근관류 SPECT 소견에서 가역적 혈류감소를 보인 분절의 혈류예비능이 더 많이 감소되는 경향을 보였으나 통계적 유의성을 보이지는 않았다. 결론: 앙상블 학습을 이용한 독립성분분석방법을 이용하여 심근혈류가 측정이 되었다. 앙상블 독립성분분석법을 이용한 $H_2^{15}O$ 동적 심근 PET 분석방법이 관상동맥 질환의 분석 및 동적 핵의학 영상 데이터의 연구에 도움이 될 것으로 기대된다.

• International Journal of Computer Science & Network Security
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• 제22권4호
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• pp.420-426
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• 2022

Breast cancer is among the cancers that may be healed as the disease diagnosed at early times before it is distributed through all the areas of the body. The Automatic Analysis of Diagnostic Tests (AAT) is an automated assistance for physicians that can deliver reliable findings to analyze the critically endangered diseases. Deep learning, a family of machine learning methods, has grown at an astonishing pace in recent years. It is used to search and render diagnoses in fields from banking to medicine to machine learning. We attempt to create a deep learning algorithm that can reliably diagnose the breast cancer in the mammogram. We want the algorithm to identify it as cancer, or this image is not cancer, allowing use of a full testing dataset of either strong clinical annotations in training data or the cancer status only, in which a few images of either cancers or noncancer were annotated. Even with this technique, the photographs would be annotated with the condition; an optional portion of the annotated image will then act as the mark. The final stage of the suggested system doesn't need any based labels to be accessible during model training. Furthermore, the results of the review process suggest that deep learning approaches have surpassed the extent of the level of state-of-of-the-the-the-art in tumor identification, feature extraction, and classification. in these three ways, the paper explains why learning algorithms were applied: train the network from scratch, transplanting certain deep learning concepts and constraints into a network, and (another way) reducing the amount of parameters in the trained nets, are two functions that help expand the scope of the networks. Researchers in economically developing countries have applied deep learning imaging devices to cancer detection; on the other hand, cancer chances have gone through the roof in Africa. Convolutional Neural Network (CNN) is a sort of deep learning that can aid you with a variety of other activities, such as speech recognition, image recognition, and classification. To accomplish this goal in this article, we will use CNN to categorize and identify breast cancer photographs from the available databases from the US Centers for Disease Control and Prevention.