Developing a Diagnostic Decision Support System for Benign Paroxysmal Positional Vertigo Using a Deep-Learning Model

Lim, Eun-Cheon; Park, Jeong Hye; Jeon, Han Jae; Kim, Hyung-Jong; Lee, Hyo‐Jeong; Song, Chang-Geun; Hong, Sung Kwang

doi:10.3390/jcm8050633

Cited by 40 publications

(31 citation statements)

References 29 publications

(33 reference statements)

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“…Despite having numerous imperfections, big data, as well as artificial intelligence have been applied in the field of medication from numerous parts [115,116]. There are numerous possible guidelines of using big data and artificial intelligence in nephrology that requires greater attention, as well as further consideration [74,78,[117][118][119][120][121][122][123][124][125].…”

Section: Potential Directions and Future Scopementioning

confidence: 99%

Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation

Thongprayoon

Kaewput

Kovvuru

et al. 2020

JCM

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Kidney diseases form part of the major health burdens experienced all over the world. Kidney diseases are linked to high economic burden, deaths, and morbidity rates. The great importance of collecting a large quantity of health-related data among human cohorts, what scholars refer to as “big data”, has increasingly been identified, with the establishment of a large group of cohorts and the usage of electronic health records (EHRs) in nephrology and transplantation. These data are valuable, and can potentially be utilized by researchers to advance knowledge in the field. Furthermore, progress in big data is stimulating the flourishing of artificial intelligence (AI), which is an excellent tool for handling, and subsequently processing, a great amount of data and may be applied to highlight more information on the effectiveness of medicine in kidney-related complications for the purpose of more precise phenotype and outcome prediction. In this article, we discuss the advances and challenges in big data, the use of EHRs and AI, with great emphasis on the usage of nephrology and transplantation.

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Section: Potential Directions and Future Scopementioning

confidence: 99%

Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation

Thongprayoon

Kaewput

Kovvuru

et al. 2020

JCM

View full text Add to dashboard Cite

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“…By virtue of both recent developments in information (IT) and biology (BT) technology through programs available on mobile devices [46] and using artificial intelligence and a deep-learning model, interest has turned to whether this approach can be used to determine the underlying disorder(s) causing dizziness and vertigo, and with this the subtype of BPPV [47]. Furthermore, recording of nystagmus during the attacks of vertigo may also become feasible in near future using various portable devices [48].…”

Section: Diagnosismentioning

confidence: 99%

Update on benign paroxysmal positional vertigo

2020

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Benign paroxysmal positional vertigo (BPPV) is the most common cause of vertigo worldwide. This review considers recent advances in the diagnosis and management of BPPV including the use of web-based technology and artificial intelligence as well as the evidence supporting the use of vitamin D supplements for patients with BPPV and subnormal serum vitamin D. Keywords Dizziness • Vertigo • Benign paroxysmal positional vertigo Hyo-Jung Kim and JaeHan Park equally contributed to this study.

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“…Disease biomarkers can be identified by visualizing these levels of concern for different physiological parameters. The principle is shown in Equation 3 and Equation 4.…”

Section: Attention Mechanismmentioning

confidence: 99%

“…Despite it has an excellent performance in the field of automatic diagnosis using medical images, the interpretability and text-based medical data analyzability of AI still faces great challenges. 4,5,45 In order to solve the problems above, on the global scale, researchers have gradually integrated deep learning technology with a medical diagnosis. Edward Choi and his colleagues used a recurrent neural network to process electronic health records (EHR) for diagnosing heart failure onset.…”

Section: Introductionmentioning

confidence: 99%

<p>Deep Learning-Based Method of Diagnosing Hyperlipidemia and Providing Diagnostic Markers Automatically</p>

Liu¹,

Zhang²,

Zhao³

et al. 2020

DMSO

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Introduction: The research of auxiliary diagnosis has always been one of the hotspots in the world. The implementation of auxiliary diagnosis support algorithm for medical text data faces challenges with interpretability and creditability. The improvement of clinical diagnostic techniques means not only the improvement of diagnostic accuracy but also the further study of diagnostic basis. Traditional research methods for diagnostic markers often require a large amount of time and economic costs. Research objects are often dozens of samples, and it is, therefore, difficult to synthesize large amounts of data. Therefore, the comprehensiveness and reliability of traditional methods have yet to be improved. Therefore, the establishment of a model that can automatically diagnose diseases and automatically provide a diagnostic basis at the same time has a positive effect on the improvement of medical diagnostic techniques. Methods: Here, we established an auxiliary diagnostic tool based on attention deep learning algorithm to diagnostic hyperlipemia and automatically predict the corresponding diagnostic markers using hematological parameters. In this paper, we not only demonstrated the ability of the proposed model to automatically diagnose diseases using text-based medical data, such as physiological parameters, but also demonstrated its ability to forecast disease diagnostic markers. Human physiological parameters are used as input to the model, and the doctor's diagnosis results as an output. Through the attention layer, the degree of attention of the model to different physiological parameters can be obtained, that is, the model provides a diagnostic basis. Results: It achieved 94% ACC, 97.48% AUC, 96% sensitivity and 92% specificity with the test dataset. All the above samples are drawn from clinical practice. Moreover, the model predicted the diagnostic markers of hyperlipidemia by the attention mechanism, and the results were fully agreeable to the golden criteria. Discussion: The auxiliary diagnosis system proposed in this paper not only achieves the accurate and robust performance, and can be used for the preliminary diagnosis of patients, but also showing its great potential to discover new diagnostic markers. Therefore, it not only can improve the efficiency of clinical diagnosis but also shorten the research period of researching a diagnosis basis to an extent. It has a positive significance to the development of the medical diagnosis level.

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Developing a Diagnostic Decision Support System for Benign Paroxysmal Positional Vertigo Using a Deep-Learning Model

Cited by 40 publications

References 29 publications

Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation

Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation

Update on benign paroxysmal positional vertigo

<p>Deep Learning-Based Method of Diagnosing Hyperlipidemia and Providing Diagnostic Markers Automatically</p>

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