• Investigative Magnetic Resonance Imaging
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• v.25 no.3
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• pp.164-171
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• 2021

Purpose: Mild cognitive impairment (MCI) is a prodromal stage of Alzheimer's disease (AD). Brain atrophy in this disease spectrum begins in the medial temporal lobe structure, which can be recognized by magnetic resonance imaging. To overcome the unsatisfactory inter-observer reliability of visual evaluation, quantitative brain volumetry has been developed and widely investigated for the diagnosis of MCI and AD. The aim of this study was to assess the prediction accuracy of quantitative brain volumetry using a fully automated segmentation software package, NeuroQuant^®, for the diagnosis of MCI. Materials and Methods: A total of 418 subjects from the Korean Brain Aging Study for Early Diagnosis and Prediction of Alzheimer's Disease cohort were included in our study. Each participant was allocated to either a cognitively normal old group (n = 285) or an MCI group (n = 133). Brain volumetric data were obtained from T1-weighted images using the NeuroQuant software package. Logistic regression and receiver operating characteristic (ROC) curve analyses were performed to investigate relevant brain regions and their prediction accuracies. Results: Multivariate logistic regression analysis revealed that normative percentiles of the hippocampus (P < 0.001), amygdala (P = 0.003), frontal lobe (P = 0.049), medial parietal lobe (P = 0.023), and third ventricle (P = 0.012) were independent predictive factors for MCI. In ROC analysis, normative percentiles of the hippocampus and amygdala showed fair accuracies in the diagnosis of MCI (area under the curve: 0.739 and 0.727, respectively). Conclusion: Normative percentiles of the hippocampus and amygdala provided by the fully automated segmentation software could be used for screening MCI with a reasonable post-processing time. This information might help us interpret structural MRI in patients with cognitive impairment.

• Journal of Biomedical Engineering Research
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• v.42 no.4
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• pp.150-158
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• 2021

Intracranial hemorrhage (ICH) refers to acute bleeding inside the intracranial vault. Not only does this devastating disease record a very high mortality rate, but it can also cause serious chronic impairment of sensory, motor, and cognitive functions. Therefore, a prompt and professional diagnosis of the disease is highly critical. Noninvasive brain imaging data are essential for clinicians to efficiently diagnose the locus of brain lesion, volume of bleeding, and subsequent cortical damage, and to take clinical interventions. In particular, computed tomography (CT) images are used most often for the diagnosis of ICH. In order to diagnose ICH through CT images, not only medical specialists with a sufficient number of diagnosis experiences are required, but even when this condition is met, there are many cases where bleeding cannot be successfully detected due to factors such as low signal ratio and artifacts of the image itself. In addition, discrepancies between interpretations or even misinterpretations might exist causing critical clinical consequences. To resolve these clinical problems, we developed a diagnostic model predicting intracranial bleeding and its subtypes (intraparenchymal, intraventricular, subarachnoid, subdural, and epidural) by applying deep learning algorithms to CT images. We also constructed a visualization tool highlighting important regions in a CT image for predicting ICH. Specifically, 1) 27,758 CT brain images from RSNA were pre-processed to minimize the computational load. 2) Three different CNN-based models (ResNet, EfficientNet-B2, and EfficientNet-B7) were trained based on a training image data set. 3) Diagnosis performance of each of the three models was evaluated based on an independent test image data set: As a result of the model comparison, EfficientNet-B7's performance (classification accuracy = 91%) was a way greater than the other models. 4) Finally, based on the result of EfficientNet-B7, we visualized the lesions of internal bleeding using the Grad-CAM. Our research suggests that artificial intelligence-based diagnostic systems can help diagnose and treat brain diseases resolving various problems in clinical situations.

• Journal of Biomedical Engineering Research
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• v.33 no.1
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• pp.15-24
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• 2012

Classification of human thought is an emerging research field that may allow us to understand human brain functions and further develop advanced brain-computer interface (BCI) systems. In the present study, we introduce a new approach to classify various mental states from noninvasive electrophysiological recordings of human brain activity. We utilized the full spatial and spectral information contained in the electroencephalography (EEG) signals recorded while a subject is performing a specific mental task. For this, the EEG data were converted into a 2D spatiospectral pattern map, of which each element was filled with 1, 0, and -1 reflecting the degrees of event-related synchronization (ERS) and event-related desynchronization (ERD). We evaluated the similarity between a current (input) 2D pattern map and the template pattern maps (database), by taking the inner-product of pattern matrices. Then, the current 2D pattern map was assigned to a class that demonstrated the highest similarity value. For the verification of our approach, eight participants took part in the present study; their EEG data were recorded while they performed four different cognitive imagery tasks. Consistent ERS/ERD patterns were observed more frequently between trials in the same class than those in different classes, indicating that these spatiospectral pattern maps could be used to classify different mental states. The classification accuracy was evaluated for each participant from both the proposed approach and a conventional mental state classification method based on the inter-hemispheric spectral power asymmetry, using the leave-one-out cross-validation (LOOCV). An average accuracy of 68.13% (${\pm}9.64%$) was attained for the proposed method; whereas an average accuracy of 57% (${\pm}5.68%$) was attained for the conventional method (significance was assessed by the one-tail paired $t$-test, $p$ < 0.01), showing that the proposed simple classification approach might be one of the promising methods in discriminating various mental states.

• Science of Emotion and Sensibility
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• v.16 no.3
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• pp.409-416
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• 2013

Attention is one of important cognitive functions in human affecting on the selectional concentration of relevant events and ignorance of irrelevant events. The discrimination of attentional and inattentional status is the first step to manage human's attentional capability using computer assisted device. In this paper, we newly combine the non-linear recurrence pattern analysis and spectrum analysis to effectively extract features(total number of 13) from the electroencephalographic signal used in the input to classifiers. The performance of diverse types of attention-inattention classifiers, including supporting vector machine, back-propagation algorithm, linear discrimination, gradient decent, and logistic regression classifiers were evaluated. Among them, the support vector machine classifier shows the best performance with the classification accuracy of 81 %. The use of spectral band feature set alone(accuracy of 76 %) shows better performance than that of non-linear recurrence pattern feature set alone(accuracy of 67 %). The support vector machine classifier with hybrid combination of non-linear and spectral analysis can be used in later designing attention-related devices.

• 한국경영정보학회:학술대회논문집
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• 2007.06a
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• pp.421-426
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• 2007

This study considered the most crucial elements in websites as web usability and web accessibility. The web usability signified the empirical satisfaction of ordinary users on websites and how much they used the relevant websites conveniently and accurately, while the web accessibility signified the technical support that could have an easy access to web contents and the principles of cognitive aspects, by considering the users such as those who have been alienated from information. Even if the people were given lots of beneficial information, the websites would be useless if their usability and accessibility were not considered. It would be necessary to reduce the information gap by letting any users (the physically challenged, the elderly, etc.) give an access to every information without any professional abilities and to provide the efficient websites corresponding to the embodiment goal of e-government through rapidity and accuracy of contents and the feedback for systematic structure and people's demand.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2003.11a
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• pp.81-83
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• 2003

The present study attempted to observe what changes the supply of highly concentrated(30％) oxygen cause to people's ability and cerebrum lateralization of verbal cognition, compared to air of normal oxygen concentration(21％). The experiment consisted of two runs, one for verbal cognition test with normal air(21％ of oxygen) and for verbal cognition test with more oxygen in the air(30％ of oxygen). Functional brain images were taken form 3T MRI using the single-shot EPI method. There were more activations observed at the occipital, parietal, temporal, and frontal lobes, but there were no changes in cerebrum lateralization with 30％ oxygen administration. The result of task performance showed the accuracy increased at 30％'s concentration of oxygen rather than 21％'s. It is concluded that the positive effect on the verbal cognitive performance level by the highly concentrated oxygen administration was due to changeless increase of left and right cerebrum activation.

• Nuclear Engineering and Technology
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• v.45 no.2
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• pp.141-150
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• 2013

There is mounting evidence that complex computer system displays in control rooms contribute to cognitive complexity and, thus, to the probability of human error. Research shows that reaction time increases and response accuracy decreases as the number of elements in the display screen increase. However, in terms of supporting the control room operator, approaches focusing on addressing display complexity solely in terms of information density and its location and patterning, will fall short of delivering a properly designed interface. This paper argues that information complexity and semantic complexity are mandatory components when considering display complexity and that the addition of these concepts assists in understanding and resolving differences between designers and the preferences and performance of operators. This paper concludes that a number of simplified methods, when combined, can be used to estimate the impact that a particular display may have on the operator's ability to perform a function accurately and effectively. We present a mixed qualitative and quantitative approach and a method for complexity estimation.

• Journal of Oral Medicine and Pain
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• v.47 no.2
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• pp.107-108
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• 2022

Artificial intelligence (AI) refers to the use of machines to mimic intelligent human behavior. It involves interactions with humans in clinical settings, and augmented intelligence is considered as a cognitive extension of AI. The importance of AI in healthcare and medicine has been emphasized in recent studies. Machine learning models, such as genetic algorithms, artificial neural networks (ANNs), and fuzzy logic, can learn and examine data to execute various functions. Among them, ANN is the most popular model for diagnosis based on image data. AI is rapidly becoming an adjunct to healthcare professionals and is expected to be human-independent in the near future. The introduction of AI to the diagnosis and treatment of oral diseases worldwide remains in the preliminary stage. AI-based or assisted diagnosis and decision-making will increase the accuracy of the diagnosis and render treatment more precise and personalized. Therefore, dental professionals must actively initiate and lead the development of AI, even if they are unfamiliar with it.

• Journal of the Korea Society of Computer and Information
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• v.19 no.9
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• pp.45-53
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• 2014

This paper suggests methods to chronicle and track the history of dialogues exchanged among characters to prevent logical errors of a story. As for stories that are long with many characters, especially in full-length novels and co-written stories, cognitive burden is imposed on a writer. If the writer has confused understanding of a character, then a logical error would enter the story. This would compromise completeness and integrity of writing. Against the backdrop, this paper shows how dialogues among characters are chronicled and tracked by using the aforementioned tracking methods through design of a writer support system that relieves a writer's cognitive burden while supporting the writing and through an analysis of existing novels. In addition, we showed the accuracy results of average 68.5% through the performance evaluation of the query used in the dialogue history tracking.

• Journal of Physiology & Pathology in Korean Medicine
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• v.27 no.5
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• pp.553-562
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• 2013

This study was performed to develop cold-heat and deficiency-excess pattern identification for dementia, as well as for standard Korean medicine diagnosis and treatment. Five experts comprised of 4 neuropsychiatrists of Korean medicine and 1 statistician to develop cold-heat and deficiency-excess pattern identification for dementia. We searched studies about pattern identification and selected 507 articles using Oasis search terms provided by the KIOM. As a result, 10 pattern identification research study were recruited. Moreover, we analyzed neuropsychological assessments for dementia that evaluate Behavioral and Psychological Symptoms of Dementia (BPSD) and cognitive function using experts conferences and we selected neuropsychological instruments using pattern identification. Six cold patterns, six heat patterns, ten deficiency patterns, and four excess patterns were identified according to the cold-heat and deficiency-excess pattern identification of dementia. We selected the Caregiver-Administered Neuropsychiatric Inventory and the Korean Mini-Mental State Examination as neuropsychological assessments of dementia, which examine behavioral symptoms and cognitive function, suspectively. We formed positive and negative correlation between Korean medicine pattern identification and neuropsychological assessments for dementia. We developed and suggested a forecast module of pattern identification for dementia. But, it is necessary to perform additional clinical trials to verify its validity and accuracy.