• 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 Veterinary Clinics
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• v.34 no.2
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• pp.82-86
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• 2017

We determined the risk factors for late embryonic mortality in dairy cows. We diagnosed pregnancy at 31 days and then confirmed the diagnosis at 45 days after artificial insemination (AI) via ultrasonography. The presence of an embryo with a heartbeat was the criterion for a positive pregnancy diagnosis. A diagnosis of late embryonic mortality was made when there was no positive sign of pregnancy in cows previously diagnosed as pregnant. The overall incidence of late embryonic mortality among 3,695 pregnancies was 6.9%. Logistic regression analysis revealed that herd size, AI month, synchronization protocol, and postpartum disease were important risk factors for late embryonic mortality. Herd size > 100 (odds ratio [OR]: 0.66, p < 0.05) and 50-100 lactating cows (OR: 0.63, p < 0.01) had lower risks of late embryonic mortality than herd size < 50 lactating cows. Cows inseminated during May-July had a higher risk (OR: 1.49, p < 0.05) of late embryonic mortality than cows inseminated during February-April. Cows inseminated after estrus following $PGF_{2{\alpha}}$ treatment also had a higher risk (OR: 1.77, p < 0.001) of late embryonic mortality than cows inseminated following natural estrus. Lastly, cows with postpartum disease tended to have a higher risk (OR: 1.26, p < 0.1) of late embryonic mortality than cows without postpartum disease. In conclusion, late embryonic mortality associated with the herd size, AI month, synchronization protocol, and postpartum disease in dairy cows.

• Journal of the Korean Society of Radiology
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• v.17 no.6
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• pp.873-879
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• 2023

This study explored the use of artificial intelligence(AI) to detect foreign bodies in chest X-ray images. Medical imaging, especially chest X-rays, plays a crucial role in diagnosing diseases such as pneumonia and lung cancer. With the increase in imaging tests, AI has become an important tool for efficient and fast diagnosis. However, images can contain foreign objects, including everyday jewelry like buttons and bra wires, which can interfere with accurate readings. In this study, we developed an AI algorithm that accurately identifies these foreign objects and processed the National Institutes of Health chest X-ray dataset based on the YOLOv8 model. The results showed high detection performance with accuracy, precision, recall, and F1-score all close to 0.91. Despite the excellent performance of AI, the study solved the problem that foreign objects in the image can distort the reading results, emphasizing the innovative role of AI in radiology and its reliability based on accuracy, which is essential for clinical implementation.

• Journal of Biomedical Engineering Research
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• v.15 no.2
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• pp.189-194
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• 1994

Effects of ${AI_2O_3}/{P_2O_5}$ ratio on the crystallization of a series of glasses with the nominal composition of 41.4wt % $SiO_2$, 35.0wt % CaO, 20.6wt % (${P_2O_5}$+${AI_2O_3}$) and 3.0wt% MgO were investigated with DTA, XRD and SEM. The major crystalline phases are apatite and anorthite. The glass transition temperature ($T_g$) and the softening point ($T_s$) are shifted to the upper temperature by increasing $AI_2O_3$ content. The temperature of apatite crystallization ($T_{p1}$) is increased by $AI_2O_3$ content, but the tempera¬ture of anorthite crystallization ($T_{p2}$) is not affected significantly. With increased of $AI_2O_3$, the apatite crystallization is decreased, but anorthite crystallization is increased.

• The Korean Journal of Nuclear Medicine
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• v.32 no.5
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• pp.397-402
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• 1998

Purpose: The purpose of this study was to evaluate the phenomenon of diaschisis in the cerebellum and cerebral cortex in patients with pure basal ganglia hemorrhage using cerebral blood flow SPECT. Materials and Methods: Twelve patients with pure basal ganglia hemorrhage were studied with Tc-99m ECD brain SPECT. Asymmetric index (AI) was calculated in the cerebellum and cerebral cortical regions as |$C_R-C_L$/$(C_R-C_L){\times}200$, where $C_R$and $C_L$ are the mean reconstructed counts for the right and left ROIs, respectively. Hypoperfusion was considered to be present when AI was greater than mean +2 SD of 20 control subjects. Results: Mean AI of the cerebellum and cerebral cortical regions in patients with pure basal ganglia hemorrhage was significantly higher than normal controls (p<0.05): Cerebellum ($18.68{\pm}8.94$ vs $4.35{\pm}0.94$, $mean{\pm}SD$), thalamus ($31.91{\pm}10.61$ vs $2.57{\pm}1.45$), basal ganglia ($35.94{\pm}16.15$ vs $4.34{\pm}2.08$), parietal ($18.94{\pm}10.69$ vs $3.24{\pm}0.87$), frontal ($13.60{\pm}10.5$ vs $4.02{\pm}2.04$) and temporal cortex ($15.92{\pm}11.95$ vs $5.13{\pm}1.69$). Ten of the 12 patients had significant hypoperfusion in the contralateral cerebellum. Hypoperfusion was also shown in the ipsilateral thalamus (n=12), ipsilateral parietal (n=12), frontal (n=6) and temporal cortex (n=10). Conclusion: Crossed cerebellar diaschisis (CCD) and cortical diaschisis may frequently occur in patients with pure basal ganglia hemorrhage, suggesting that CCD can develop without the interruption of corticopontocerebellar pathway.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.11
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• pp.305-310
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• 2021

Artificial intelligence technology in the medical field initially focused on analysis and algorithm development, but it is gradually changing to web application development for service as a product. This paper describes a Urinary Stone segmentation model in abdominal CT images and an artificial intelligence web application based on it. To implement this, a model was developed using U-Net, a fully-convolutional network-based model of the end-to-end method proposed for the purpose of image segmentation in the medical imaging field. And for web service development, it was developed based on AWS cloud using a Python-based micro web framework called Flask. Finally, the result predicted by the urolithiasis segmentation model by model serving is shown as the result of performing the AI web application service. We expect that our proposed AI web application service will be utilized for screening test.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.292-300
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• 2023

This paper proposes a patent discovery method and strategy for Generative AI-related patents by utilizing qualitative evaluation indicators established based on the core components of the technology. Currently, the evaluation of patent quality relies on quantitative indicators, but existing quantitative indicators cannot represent the characteristics of Generative AI technology, making it difficult to accurately evaluate. Therefore, there is a need for additional qualitative indicators that consider technical characteristics based on patent claims, which can reveal the actual strength of the patent. In this paper, we propose a new evaluation index considering the technical characteristics of Generative AI. Core patents were selected using the proposed evaluation index, and the appropriateness of the proposed index was verified through the existing quantitative evaluation method for the selected core patents.

• Korean Medical Education Review
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• v.2 no.2
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• pp.73-82
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• 2001

• 대한치과보철학회:학술대회논문집
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• 1999.04a
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• pp.62-62
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• 1999

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.10
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• pp.239-246
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• 2020

In the medical field where artificial intelligence technology is introduced, research related to clinical decision support system(CDSS) in relation to diagnosis and prediction is actively being conducted. In particular, medical imaging-based disease diagnosis area applied AI technologies at various products. However, medical imaging data consists of inconsistent data, and it is a reality that it takes considerable time to prepare and use it for research. This paper describes a one-stop AI learning platform for converting to medical image standard R_CDM(Radiology Common Data Model) and supporting AI algorithm development research based on the dataset. To this, the focus is on linking with the existing CDM(common data model) and model the system, including the schema of the medical imaging standard model and report information for multi-center research based on DICOM(Digital Imaging and Communications in Medicine) tag information. And also, we show the execution results based on generated datasets through the AI learning platform. As a proposed platform, it is expected to be used for various image-based artificial intelligence researches.