A Deep Belief Network and Dempster-Shafer-Based Multiclassifier for the Pathology Stage of Prostate Cancer

Kim, Jae Kwon; Choi, Minseok; Lee, Jong Sik; Hong, Jun Hyuk; Kim, Choung Soo; Seo, Seong Il; Jeong, Chang Wook; Byun, Seok Soo; Koo, Kyo Chul; Chung, Byung Ha; Park, Yong Hyun; Lee, Ji Youl; Choi, In Young

doi:10.1155/2018/4651582

Cited by 11 publications

(4 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the past few years, some predictive tools were developed to help identify the PCa risk before biopsy, such as probability table, artificial neural network, and nomogram. [12][13][14][15][16][17][18] Comparing with other tools, the nomogram could integrate different risk factors and provide an individualized estimation of PCa probability. Besides, the nomogram can be displayed graphically and easily applied in clinical practices.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a nomogram for predicting prostate cancer in patients with PSA ≤ 20 ng/mL at initial biopsy

Yin

et al. 2021

Medicine

View full text Add to dashboard Cite

The aim of this study was to construct a nomogram for predicting prostate cancer (PCa) in patients with PSA ≤ 20 ng/mL at initial biopsy. The patients with PSA ≤ 20 ng/mL who underwent prostate biopsy were retrospectively included in this study. The nomogram was developed based on predictors for PCa, which were assessed by multivariable logistic regression analysis. The receiver operating characteristic curve, calibration plots and decision curve analysis (DCA) were used to evaluate the performance of the nomogram. This retrospective study included 691 patients, who were divided into training set (505 patients) and validation set (186 patients). The nomogram was developed based on the multivariable logistic regression model, including age, total PSA, free PSA, and prostate volume. It had a high area under the curve of 0.857, and was well verified in validation set. Calibration plots and DCA further validated its discrimination and potential clinical benefits. Applying the cut-off value of 15%, our nomogram would avoid 42.5% of unnecessary biopsies while miss only 4.4% of PCa patients. The nomogram provided high predictive accuracy for PCa in patients with PSA ≤ 20 ng/mL at initial biopsy, which could be used to avoid the unnecessary biopsies in clinical practice.

show abstract

Section: Introductionmentioning

confidence: 99%

Development and validation of a nomogram for predicting prostate cancer in patients with PSA ≤ 20 ng/mL at initial biopsy

Yin

et al. 2021

Medicine

View full text Add to dashboard Cite

show abstract

“…Some of these studies include analyzing medical images with AI technology to discriminate images and use them for treatments; predicting the course of a disease through various medical and health care data; developing medical devices that can support decision-making during treatments or for diagnosis; and encrypting medical data. 5 6 7 8 9 10 11 …”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Health Care: Current Applications and Issues

et al. 2020

View full text Add to dashboard Cite

In recent years, artificial intelligence (AI) technologies have greatly advanced and become a reality in many areas of our daily lives. In the health care field, numerous efforts are being made to implement the AI technology for practical medical treatments. With the rapid developments in machine learning algorithms and improvements in hardware performances, the AI technology is expected to play an important role in effectively analyzing and utilizing extensive amounts of health and medical data. However, the AI technology has various unique characteristics that are different from the existing health care technologies. Subsequently, there are a number of areas that need to be supplemented within the current health care system for the AI to be utilized more effectively and frequently in health care. In addition, the number of medical practitioners and public that accept AI in the health care is still low; moreover, there are various concerns regarding the safety and reliability of AI technology implementations. Therefore, this paper aims to introduce the current research and application status of AI technology in health care and discuss the issues that need to be resolved.

show abstract

“…The findings showed that CNN-based models mostly focused on analyzing medical image data, with RNN architectures for sequential data analysis and AEs for unsupervised dimensionality reduction yet to be actively explored. Other deep learning methods, such as deep belief networks [ 137 , 138 ], deep Q network [ 139 ], and dictionary learning [ 140 ], have also been applied to biomedical research but were excluded from the content analysis because of low citation count. As deep learning is a rapidly evolving field, future biomedical researchers should pay attention to the emerging trends and keep aware of state-of-the-art models for enhanced performance, such as transformer-based models, including bidirectional encoder representations from transformers for NLP [ 141 ]; wav2vec for speech recognition [ 142 ]; and the Swin transformer for computer vision tasks of image classification, segmentation, and object detection [ 143 ].…”

Section: Discussionmentioning

confidence: 99%

Understanding the Research Landscape of Deep Learning in Biomedical Science: Scientometric Analysis

Nam¹,

Kim²,

Jung³

et al. 2022

J Med Internet Res

View full text Add to dashboard Cite

Background Advances in biomedical research using deep learning techniques have generated a large volume of related literature. However, there is a lack of scientometric studies that provide a bird’s-eye view of them. This absence has led to a partial and fragmented understanding of the field and its progress. Objective This study aimed to gain a quantitative and qualitative understanding of the scientific domain by analyzing diverse bibliographic entities that represent the research landscape from multiple perspectives and levels of granularity. Methods We searched and retrieved 978 deep learning studies in biomedicine from the PubMed database. A scientometric analysis was performed by analyzing the metadata, content of influential works, and cited references. Results In the process, we identified the current leading fields, major research topics and techniques, knowledge diffusion, and research collaboration. There was a predominant focus on applying deep learning, especially convolutional neural networks, to radiology and medical imaging, whereas a few studies focused on protein or genome analysis. Radiology and medical imaging also appeared to be the most significant knowledge sources and an important field in knowledge diffusion, followed by computer science and electrical engineering. A coauthorship analysis revealed various collaborations among engineering-oriented and biomedicine-oriented clusters of disciplines. Conclusions This study investigated the landscape of deep learning research in biomedicine and confirmed its interdisciplinary nature. Although it has been successful, we believe that there is a need for diverse applications in certain areas to further boost the contributions of deep learning in addressing biomedical research problems. We expect the results of this study to help researchers and communities better align their present and future work.

show abstract

A Deep Belief Network and Dempster-Shafer-Based Multiclassifier for the Pathology Stage of Prostate Cancer

Cited by 11 publications

References 31 publications

Development and validation of a nomogram for predicting prostate cancer in patients with PSA ≤ 20 ng/mL at initial biopsy

Development and validation of a nomogram for predicting prostate cancer in patients with PSA ≤ 20 ng/mL at initial biopsy

Artificial Intelligence in Health Care: Current Applications and Issues

Understanding the Research Landscape of Deep Learning in Biomedical Science: Scientometric Analysis

Contact Info

Product

Resources

About