• International journal of advanced smart convergence
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• 제7권4호
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• pp.169-173
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• 2018

Iris diagnosis based on the color and texture information is one of a novel approach which can represent the current state of a certain organ inside body or the health condition of a person. In analysis of the iris images, there are critical image artifacts which can prevent of use interpretation of the iris textures on images. Here, we developed the iris diagnosis system based on a hand-held typed imaging probe which consists of a single camera sensor module with 8M pixels, two pairs of 400~700 nm LED, and a guide beam. Two original images with different light noise pattern were successively acquired in turns, and the light noise-free image was finally reconstructed and demonstrated by the proposed artifact removal approach.

• BMB Reports
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• 제51권11호
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• pp.563-571
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• 2018

Colorectal cancer (CRC), the third most common cancer in the world, has no specific biomarkers that facilitate its diagnosis and subsequent treatment. The miRNAs, small single-stranded RNAs that repress the mRNA translation and trigger the mRNA degradation, show aberrant levels in the CRC, by which these molecules have been related with the initiation, progression, and drug-resistance of this cancer type. Numerous studies show the microRNAs influence the cellular mechanisms related to the cell cycle, differentiation, apoptosis, and migration of the cancer cells through the post-transcriptionally regulated gene expression. Specific patterns of the upregulated and down-regulated miRNA have been associated with the CRC diagnosis, prognosis, and therapeutic response. Concretely, the downregulated miRNAs represent attractive candidates, not only for the CRC diagnosis, but for the targeted therapies via the tumor-suppressing microRNA replacement. This review shows a general overview of the potential uses of the miRNAs in the CRC diagnosis, prognosis, and treatment with a special focus on the downregulated ones.

• Clinical and Experimental Emergency Medicine
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• 제5권4호
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• pp.264-271
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• 2018

Objective For patients with acute myocardial infarction (AMI), symptoms assessed by emergency medical services (EMS) providers have a critical role in prehospital treatment decisions. The purpose of this study was to evaluate the diagnostic accuracy of EMS provider-assessed cardiac symptoms of AMI. Methods Patients transported by EMS to 4 study hospitals from 2008 to 2012 were included. Using EMS and administrative emergency department databases, patients were stratified according to the presence of EMS-assessed cardiac symptoms and emergency department diagnosis of AMI. Cardiac symptoms were defined as chest pain, dyspnea, palpitations, and syncope. Disproportionate stratified sampling was used, and medical records of sampled patients were reviewed to identify an actual diagnosis of AMI. Using inverse probability weighting, verification bias-corrected diagnostic performance was estimated. Results Overall, 92,353 patients were enrolled in the study. Of these, 13,971 (15.1%) complained of cardiac symptoms to EMS providers. A total of 775 patients were sampled for hospital record review. The sensitivity, specificity, positive predictive value, and negative predictive value of EMS provider-assessed cardiac symptoms for the final diagnosis of AMI was 73.3% (95% confidence interval [CI], 70.8 to 75.7), 85.3% (95% CI, 85.3 to 85.4), 3.9% (95% CI, 3.6 to 4.2), and 99.7% (95% CI, 99.7 to 99.8), respectively. Conclusion We found that EMS provider-assessed cardiac symptoms had moderate sensitivity and high specificity for diagnosis of AMI. EMS policymakers can use these data to evaluate the pertinence of specific prehospital treatment of AMI.

• 대한의생명과학회지
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• 제29권4호
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• pp.344-354
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• 2023

This study emphasizes the importance of early diagnosis and response to COVID-19, leading to the development of a rapid diagnostic kit using quantum dots. The research focuses on finely tuning bioconjugation with quantum dots to enhance the accuracy and sensitivity of COVID-19 diagnosis. We have developed a COVID-19 rapid diagnostic kit that exhibits a sensitivity more than 50 times higher than existing COVID-19 diagnostic kits. Quantum dots enable the accurate detection of COVID-19 viral antigens even at low concentrations, providing a rapid response in the early stages of infection. The COVID-19 quantum dot diagnostic kit offers quick analysis time, utilizing the quantum properties of particles to swiftly measure COVID-19 infection for immediate response and isolation measures. Additionally, this diagnostic kit allows for multiple analyses with ease, as multiple quantum dots can detect various antigens and antibodies simultaneously in a single experiment. This efficiency enhances testing, reduces sample requirements, and lowers experimental costs. The application of this diagnostic technology is anticipated in the future for early diagnosis and monitoring of other infectious diseases.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1995년도 Proceedings of the Korea Automation Control Conference, 10th (KACC); Seoul, Korea; 23-25 Oct. 1995
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• pp.500-504
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• 1995

A neural network based upon the back propagation algorithm was designed and applied to acoustic power spectra of electrohydraulic total artificial hearts in order to diagnose mechanical failure of devices. The trained network distinguished spectra of the mechanically damaged device from those of the undamaged device with overall success rate of 63%. Moreover, the network correctly classified more than 70% of spectra in the frequency bands of 0-100 Hz and 700-950 Hz. Consequently, the neural network analysis was useful for the diagnosis of mechanical failure of a total artificial heart.

• 대한의용생체공학회:의공학회지
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• 제30권1호
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• pp.41-48
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• 2009

In this paper, we present the diagnosis system for swallowing disorder. There are some types of diagnosis device for swallowing disorder, for example, the video fluoroscopy, the nuclear medicine inspection, the endoscopy, EMG and motion analysis. But these systems need heavy devices or have dangerous nuclear exposure, so are uncomfortable for handicapped person. Our system has advantages of simplicity, accuracy and quantitative analysis. In addition to the diagnosis aspect, this system can be used to biofeedback treatment.

• 대한의용생체공학회:의공학회지
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• 제3권2호
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• pp.83-94
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• 1982

This paper presents a practical approach to develop computer algorithm for automatic diagnosis electrocardiogram(ECG) based on a decision table techniques.

• 대한의생명과학회지
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• 제28권4호
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• pp.223-228
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• 2022

It has well reported that tumor markers have many advantages like minimally invasive, convenient use, low cost but also has many limitations like low sensitivity and specificity, relevance of prognosis, low organ specificity. Although no tumor markers are ideal, many tumor markers are used for cancer diagnosis, treatment monitoring, and surveillance monitoring after treatment. We review the classification and characteristics of tumor markers according cancer types and clinical roles in current times.

• 한국멀티미디어학회논문지
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• 제19권8호
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• pp.1538-1543
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• 2016

Strabismus is a non-aligned state;the visual axis of each eye is not directed toward the same direction at the same time. Clinically, the degrees of strabismus are measured by prism cover test, corneal reflex test (Hirschberg test), prism reflex test (Krimsky prism test), But corneal reflex test and prism reflex test is a possibility that errors occur. we suggest a computer-aided diagnosis for strabismus. We made a mobile application to measure angles of strabismus. For 34 patients, we tested our application. The result of comparing between two methods, It showed a difference 7 Prism Diopter(PD). Our application gives strabismus angles just using a camera and a smart device. Therefore, it can reduce the cost and make the diagnosis of strabismus accurate.

• Smart Structures and Systems
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• 제5권2호
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• pp.153-171
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• 2009

In this paper, the analytical fault detection and diagnosis (FDD) is presented using model-based and signal-based methodology with wavelet analysis on signals obtained from sensors and sensor networks. In the model-based FDD, we present the modeling of contact interface found in soft materials, including the biomedical contacts. Fingerprint analysis and signal-based FDD are also presented with an experimental framework consisting of a mechanical pneumatic system typically found in manufacturing automation. This diagnosis system focuses on the signal-based approach which employs multi-resolution wavelet decomposition of various sensor signals such as pressure, flow rate, etc., to determine leak configuration. Pattern recognition technique and analytical vectorized maps are developed to diagnose an unknown leakage based on the established FDD information using the affine mapping. Experimental studies and analysis are presented to illustrate the FDD methodology. Both model-based and wavelet-based FDD applied in contact interface and manufacturing automation have implication towards better quality of life by applying theory and practice to understand how effective diagnosis can be made using intelligent FDD. As an illustration, a model-based contact surface technology an benefit the diabetes with the detection of abnormal contact patterns that may result in ulceration if not detected and treated in time, thus, improving the quality of life of the patients. Ultimately, effective diagnosis using FDD with wavelet analysis, whether it is employed in biomedical applications or manufacturing automation, can have impacts on improving our quality of life.