• Korean Journal of Radiology
• /
• v.25 no.1
• /
• pp.86-102
• /
• 2024

Early diagnosis, accurate assessment, and localization of peritoneal metastasis (PM) are essential for the selection of appropriate treatments and surgical guidance. However, available imaging modalities (computed tomography [CT], conventional magnetic resonance imaging [MRI], and 18fluorodeoxyglucose positron emission tomography [PET]/CT) have limitations. The advent of new imaging techniques and novel molecular imaging agents have revealed molecular processes in the tumor microenvironment as an application for the early diagnosis and assessment of PM as well as real-time guided surgical resection, which has changed clinical management. In contrast to clinical imaging, which is purely qualitative and subjective for interpreting macroscopic structures, radiomics and artificial intelligence (AI) capitalize on high-dimensional numerical data from images that may reflect tumor pathophysiology. A predictive model can be used to predict the occurrence, recurrence, and prognosis of PM, thereby avoiding unnecessary exploratory surgeries. This review summarizes the role and status of different imaging techniques, especially new imaging strategies such as spectral photon-counting CT, fibroblast activation protein inhibitor (FAPI) PET/CT, near-infrared fluorescence imaging, and PET/MRI, for early diagnosis, assessment of surgical indications, and recurrence monitoring in patients with PM. The clinical applications, limitations, and solutions for fluorescence imaging, radiomics, and AI are also discussed.

• Journal of Veterinary Clinics
• /
• v.34 no.2
• /
• pp.82-86
• /
• 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 Electrical Engineering and Technology
• /
• v.9 no.6
• /
• pp.2058-2064
• /
• 2014

In transformer fault diagnosis, dissolved gas analysis (DGA) is been widely employed for a long period and numerous methods have been innovated to interpret its results. Still in some cases it fails to identify the corresponding faults. Due to the limitation of training data and non-linearity, the estimation of key-gas ratio in the transformer oil becomes more complicated. This paper presents Intuitionistic Fuzzy expert System (IFS) to diagnose several faults in a transformer. This revised approach is well suitable to diagnosis the transformer faults and the corresponding action to be taken. The proposed method is applied to an independent data of different power transformers and various case studies of historic trends of transformer units. It has been proved to be a very advantageous tool for transformer diagnosis and upkeep planning. This method has been successfully used to identify the type of fault developing within a transformer even if there is conflict in the results of AI technique applied to DGA data.

• Annual Conference of KIPS
• /
• 2024.10a
• /
• pp.686-687
• /
• 2024

This study aims to predict MMSE scores in Alzheimer's disease (AD) patients using a CNN-LSTM model that processes MRI images and metadata. The OASIS-2 dataset was used, with MRI slices (central, ±10mm, and ±15mm) and metadata. Two datasets were created: one with central and ±10mm slices (10mm dataset), and another with central, ±10mm, and ±15mm slices (combined dataset). The CNN-LSTM model extracted features using VGG16 and combined them with metadata to predict MMSE scores. The 10mm model outperformed the combined model, achieving an MSE of 0.527 and MAE of 0.509. This study highlights the potential of predicting MMSE scores using MRI and metadata for early diagnosis of AD.

• Journal of the Microelectronics and Packaging Society
• /
• v.30 no.1
• /
• pp.30-41
• /
• 2023

With the aggressive down-scaling of advanced integrated circuits (ICs), electronic packages have become the bottleneck of both reliability and performance of whole electronic systems. In order to resolve the reliability issues, Institute of Electrical and Electronics Engineers (IEEE) laid down a roadmap on fault detection and diagnosis (FDD), thrusting the digital twin: a combination of reliability physics and artificial intelligence (AI). In this paper, we especially review research works regarding the signal-based FDD approaches on the electronic packages. We also discuss the research trend of FDD utilizing AI techniques.

• Journal of the Korean Society of Radiology
• /
• v.83 no.3
• /
• pp.473-485
• /
• 2022

The incidence of neurodegenerative diseases in the older population has increased in recent years. A considerable number of studies have been performed to characterize these diseases. Imaging analysis is an important biomarker for the diagnosis of neurodegenerative disease. Objective and reliable assessment and precise detection are important for the early diagnosis of neurodegenerative diseases. Artificial intelligence (AI) using brain MRI applied to the study of neurodegenerative diseases could promote early diagnosis and optimal decisions for treatment plans. MRI-based AI software have been developed and studied worldwide. Representatively, there are MRI-based volumetry and segmentation software. In this review, we present the development process of brain volumetry analysis software in neurodegenerative diseases, currently used and developed AI software for neurodegenerative disease in the Republic of Korea, probable uses of AI in the future, and AI software limitations.

• Journal of the Korean Society of Radiology
• /
• v.85 no.4
• /
• pp.769-779
• /
• 2024

Purpose To determine the pros and cons of an artificial intelligence (AI) model developed to diagnose acute rib fractures in chest CT images of patients with chest trauma. Materials and Methods A total of 1209 chest CT images (acute rib fracture [n = 1159], normal [n = 50]) were selected among patients with chest trauma. Among 1159 acute rib fracture CT images, 9 were randomly selected for AI model training. 150 acute rib fracture CT images and 50 normal ones were tested, and the remaining 1000 acute rib fracture CT images was internally verified. We investigated the diagnostic accuracy and errors of AI model for the presence and location of acute rib fractures. Results Sensitivity, specificity, positive and negative predictive values, and accuracy for diagnosing acute rib fractures in chest CT images were 93.3%, 94%, 97.9%, 82.5%, and 95.6% respectively. However, the accuracy of the location of acute rib fractures was low at 76% (760/1000). The cause of error in the diagnosis of acute rib fracture seemed to be a result of considering the scapula or clavicle that were in the same position (66%) or some ribs that were not recognized (34%). Conclusion The AI model for diagnosing acute rib fractures showed high accuracy in detecting the presence of acute rib fractures, but diagnosis of the exact location of rib fractures was limited.

• Journal of the Korean Society of Radiology
• /
• v.82 no.1
• /
• pp.12-28
• /
• 2021

Mammography is the primary imaging modality for breast cancer detection; however, a high level of expertise is needed for its interpretation. To overcome this difficulty, artificial intelligence (AI) algorithms for breast cancer detection have recently been investigated. In this review, we describe the characteristics of AI algorithms compared to conventional computer-aided diagnosis software and share our thoughts on the best methods to develop and validate the algorithms. Additionally, several AI algorithms have introduced for triaging screening mammograms, breast density assessment, and prediction of breast cancer risk have been introduced. Finally, we emphasize the need for interest and guidance from radiologists regarding AI research in mammography, considering the possibility that AI will be introduced shortly into clinical practice.

• Korean Journal of Ecology and Environment
• /
• v.54 no.3
• /
• pp.190-198
• /
• 2021

Human Aichivirus (Aichivirus A; AiV-A) is a positive-sense single-strand RNA non-enveloped virus that has been detected worldwide in various water environments including sewage, river, surface, and ground over the past decade. To develop a method with excellent sensitivity and specificity for AiV-A diagnosis from water environments such as groundwater, a combination capable of reverse transcription (RT)-nested polymerase chain reaction (PCR) was developed based on existing reported and newly designed primers. A selective method was applied to evaluate domestic drinking groundwater samples. Thus, a procedure was devised to select and subsequently identify RT-nested PCR primer sets that can successfully detect and identify AiV-A from groundwater samples. The findings will contribute to developing a better monitoring system to detect AiV-A contamination in water environments such as groundwater.

• Journal of Digestive Cancer Research
• /
• v.11 no.3
• /
• pp.130-140
• /
• 2023

Gastric cancer (GC) is one of the most common malignant tumors worldwide, with a 5-year survival rate of < 40%. The diagnosis and treatment decisions of GC rely on human experts' judgments on medical images; therefore, the accuracy can be hindered by image condition, objective criterion, limited experience, and interobserver discrepancy. In recent years, several applications of artificial intelligence (AI) have emerged in the GC field based on improvement of computational power and deep learning algorithms. AI can support various clinical practices in endoscopic examination, pathologic confirmation, radiologic staging, and prognosis prediction. This review has systematically summarized the current status of AI applications after a comprehensive literature search. Although the current approaches are challenged by data scarcity and poor interpretability, future directions of this field are likely to overcome the risk and enhance their accuracy and applicability in clinical practice.