• Journal of Gastric Cancer
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• v.23 no.3
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• pp.375-387
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• 2023

Stomach cancer has a high annual mortality rate worldwide necessitating early detection and accurate treatment. Even experienced specialists can make erroneous judgments based on several factors. Artificial intelligence (AI) technologies are being developed rapidly to assist in this field. Here, we aimed to determine how AI technology is used in gastric cancer diagnosis and analyze how it helps patients and surgeons. Early detection and correct treatment of early gastric cancer (EGC) can greatly increase survival rates. To determine this, it is important to accurately determine the diagnosis and depth of the lesion and the presence or absence of metastasis to the lymph nodes, and suggest an appropriate treatment method. The deep learning algorithm, which has learned gastric lesion endoscopyimages, morphological characteristics, and patient clinical information, detects gastric lesions with high accuracy, sensitivity, and specificity, and predicts morphological characteristics. Through this, AI assists the judgment of specialists to help select the correct treatment method among endoscopic procedures and radical resections and helps to predict the resection margins of lesions. Additionally, AI technology has increased the diagnostic rate of both relatively inexperienced and skilled endoscopic diagnosticians. However, there were limitations in the data used for learning, such as the amount of quantitatively insufficient data, retrospective study design, single-center design, and cases of non-various lesions. Nevertheless, this assisted endoscopic diagnosis technology that incorporates deep learning technology is sufficiently practical and future-oriented and can play an important role in suggesting accurate treatment plans to surgeons for resection of lesions in the treatment of EGC.

• Journal of Embryo Transfer
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• v.32 no.3
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• pp.153-157
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• 2017

This study was conducted to investigate the optimal artificial insemination (AI) time with diagnostic kit at ovulation time. We already applied the patent about the protein in the cow heat mucose in external reproductive tract. And we would examine the accuracy for detection of cow heat by the kit produced with the protein. Evaluation of optimal heat detection was tried two time at 12 hrs and 24 hrs after the heat. And then, AI service also performed two times with no relation to the results of heat diagnosis by heat detection kit and pregnancy rates were checked with rectal palpation on $60^{th}$ day after AI. Heat diagnostic results by kit in natural heat after 12 hrs in Hanwoo cows were showed 31.3~75.0% on positive in first heat detection and 33.3~100.0% on positve in second heat detection. In the $1^{st}$ positive results were significant different (p<0.05), but $2^{nd}$ positive were not. The results of heat detection showed different result on regional influence and individual cow effects. The pregnancy rates of first trial of heat detection were showed 34.4~78.7% on positive and 21.3~68.8% on negative after the diagnosis by heat detection kit. And the pregnancy rates of next trial of heat detection were showed 33.3~85.7% on positive and 14.3~66.6% on negative after the heat diagnosis. Both positive results of first trial and next trial also were showed significant different (p<0.05), but negative results were not. In positive result, first trial of total pregnancy rates was higher than the next trial of pregnancy, but there showed opposite results on negative results. In conclusion, the optimal heat detection kit is suitable to ordinary Hanwoo cows and it suggested that we have to improve the kit's accuracy by detecting the materials like proteins related optimal AI time.

• Journal of IKEEE
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• v.26 no.1
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• pp.104-110
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• 2022

In this paper, we propose a new dataset for AI diagnosis and BLDC motor diagnosis in UAV. In the diagnosis of BLDC motor, PVA(Park's Vector Approach) is difficult to apply due to many ripples of frequency components. However, since the components of ripples are the third harmonics, we propose a method to utilize PVA as circle fitting by applying Savitzky-Golay filter which is excellent for the third harmonics. On the other hand, PVA, a technique to convert from three-phase to two-phase, is always based on the origin during the transformation process. This study demonstrates that the error of the origin and the measured center can be detected and diagnosed in the application process of Circle fitting, and that it can be used as a new data set of AI technology.

• Journal of Oral Medicine and Pain
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• v.17 no.1
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• pp.89-94
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• 1992

To evaluate Helhimo's Anamnestic Dysfunction Index(AI), Helkimo's Clinical Dysfunction Index(HDI), and Friction's Craniomandibular Index(CMI) in Korean Young population, clinical examinations were performed in 207 dental college students who were healthy and had no evidence of the craniomandibular disorders. The obtained results were as follows; 1. The mean values of the Helkimo's Anamnestic Index(AI), Clinical Dysfunction Index(HDI), Fricton's Palpation Index(PI), Dysfunction Index(DI), and Craniomandibular Index(CMI) were 0.35, 0.71, 0.03, 0.05, and 0.04 in male subjects. 2. The mean values of the Helkimo's Anamnestic Index(AI), Clinical Dysfunction Index(HDI), Fricton's Palpation Index(PT), Dysfunction Index(DI), and Fricton's Craniomandibular Index(CMI) were 0.42, 0.72, 0.02, 0.04, and 0.03 in female subjects. 3. There was no statistically significant difference between male and female subjects in each parameter. 4. The palpation index(PI) observed in Korean Young Population was lower than that in American population.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.3
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• pp.151-158
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• 2024

Although on-device artificial intelligence (AI) has gained attention to diagnosing machine faults in real time, most previous studies did not consider the model retraining and redeployment processes that must be performed in real-world industrial environments. Our study addresses this challenge by proposing an on-device AI-based real-time machine fault diagnosis system that utilizes continual learning. Our proposed system includes a lightweight convolutional neural network (CNN) model, a continual learning algorithm, and a real-time monitoring service. First, we developed a lightweight 1D CNN model to reduce the cost of model deployment and enable real-time inference on the target edge device with limited computing resources. We then compared the performance of five continual learning algorithms with three public bearing fault datasets and selected the most effective algorithm for our system. Finally, we implemented a real-time monitoring service using an open-source data visualization framework. In the performance comparison results between continual learning algorithms, we found that the replay-based algorithms outperformed the regularization-based algorithms, and the experience replay (ER) algorithm had the best diagnostic accuracy. We further tuned the number and length of data samples used for a memory buffer of the ER algorithm to maximize its performance. We confirmed that the performance of the ER algorithm becomes higher when a longer data length is used. Consequently, the proposed system showed an accuracy of 98.7%, while only 16.5% of the previous data was stored in memory buffer. Our lightweight CNN model was also able to diagnose a fault type of one data sample within 3.76 ms on the Raspberry Pi 4B device.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.13-22
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• 2021

The clinical decision support system uses accumulated medical data to apply an AI model learned by machine learning to patient diagnosis and treatment prediction. However, the existing black box-based AI application does not provide a valid reason for the result predicted by the system, so there is a limitation in that it lacks explanation. To compensate for these problems, this paper proposes a system model that applies XAI that can be explained in the development stage of the clinical decision support system. The proposed model can supplement the limitations of the black box by additionally applying a specific XAI technology that can be explained to the existing AI model. To show the application of the proposed model, we present an example of XAI application using LIME and SHAP. Through testing, it is possible to explain how data affects the prediction results of the model from various perspectives. The proposed model has the advantage of increasing the user's trust by presenting a specific reason to the user. In addition, it is expected that the active use of XAI will overcome the limitations of the existing clinical decision support system and enable better diagnosis and decision support.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.30 no.4
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• pp.145-152
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• 2019

Objectives: The detection of autism spectrum disorder (ASD) is based on behavioral observations. To build a more objective datadriven method for screening and diagnosing ASD, many studies have attempted to incorporate artificial intelligence (AI) technologies. Therefore, the purpose of this literature review is to summarize the studies that used AI in the assessment process and examine whether other behavioral data could potentially be used to distinguish ASD characteristics. Methods: Based on our search and exclusion criteria, we reviewed 13 studies. Results: To improve the accuracy of outcomes, AI algorithms have been used to identify items in assessment instruments that are most predictive of ASD. Creating a smaller subset and therefore reducing the lengthy evaluation process, studies have tested the efficiency of identifying individuals with ASD from those without. Other studies have examined the feasibility of using other behavioral observational features as potential supportive data. Conclusion: While previous studies have shown high accuracy, sensitivity, and specificity in classifying ASD and non-ASD individuals, there remain many challenges regarding feasibility in the real-world that need to be resolved before AI methods can be fully integrated into the healthcare system as clinical decision support systems.

• Journal of the Korean Society of Systems Engineering
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• v.17 no.2
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• pp.91-97
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• 2021

User interface for cancer classification system is a software application with clinician's friendly tools and functions to diagnose cancer from pathology images. Pathology evolved from manual diagnosis to computer-aided diagnosis with the help of Artificial Intelligence tools and algorithms. In this paper, we explained each block of the project life cycle for the implementation of automated breast cancer classification software using AI and machine learning algorithms to classify normal and invasive breast histology images. The system was designed to help the pathologists in an automatic and efficient diagnosis of breast cancer. To design the classification model, Hematoxylin and Eosin (H&E) stained breast histology images were obtained from the ICIAR Breast Cancer challenge. These images are stain normalized to minimize the error that can occur during model training due to pathological stains. The normalized dataset was fed into the ResNet-34 for the classification of normal and invasive breast cancer images. ResNet-34 gave 94% accuracy, 93% F Score, 95% of model Recall, and 91% precision.

• Journal of Korean Society for Quality Management
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• v.52 no.3
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• pp.557-574
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• 2024

Purpose: This study investigates whether adoption of AI-based systems and technologies improve operational efficiency in healthcare organizations through a systematic review of the literature and real-world examples. Methods: In this study, we divided the AI application cases into care services and administrative functions, then we explored opportunities and challenges in each area. Results: The analysis results indicate that the care service field primarily uses AI-based systems and technologies for quick disease diagnosis and treatment, surgery and disease prediction, and the provision of personalized healthcare services. In the administrative field, AI-based systems and technologies are used to streamline processes and automate tasks for the following functions: patient monitoring through virtual care support systems; automating patient management systems for appointment times, reservations, changes, and no-shows; facilitating patient-medical staff interaction and feedback through interaction support systems; and managing admission and discharge procedures. Conclusion: The results of this study provide valuable insights and significant implications about the application of AI-based systems or technologies for various innovation opportunities in healthcare organizations. As digital transformation accelerates across all industries, these findings provide valuable information to managers of hospitals that are interested in AI adoption, as well as for policymakers involved in the formulation of medical regulations and laws.

• Radiation Oncology Journal
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• v.17 no.3
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• pp.203-208
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• 1999

Purpose : To evaluate whether spontaneous apoptosis has prognostic value among patients with squamous cell carcinoma of lung. Materials and Methods : Material from 19 patients who received thoracic irradiation between 1990 and 1994 was analyzed. Their stages were II (1), IIIa (8), IIIb (5), and IV (5). Patients were observed from 5 to 67 months (median : 17 months). The spontaneous apoptosis index (AI) and p53 mutation were measured by immunohistochemical stains. Results : AI was found to range from 0 to $1\%$ (median $0.4\%$). Patients with low AI ($AI{\leq}$median) had a much higher distant metastasis rate at diagnosis than patients with high AI. By analysis of prognostic factors for survival, M stage was significant in univariate analysis. AI, chemotherapy, M stage, T stage, and stage were significant in multivariate analysis. The correlation between the AI and p53 mutation was not seen. Conclusion : AI was related with distant metastasis at diagnosis and not with p53 mutation. Also low AI group tended to have shorter survival time than high AI group.