• Korean Journal of Radiology
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• v.25 no.4
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• pp.343-350
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• 2024

Objective: Artificial intelligence-based computer-aided diagnosis (AI-CAD) is increasingly used in mammography. While the continuous scores of AI-CAD have been related to malignancy risk, the understanding of how to interpret and apply these scores remains limited. We investigated the positive predictive values (PPVs) of the abnormality scores generated by a deep learning-based commercial AI-CAD system and analyzed them in relation to clinical and radiological findings. Materials and Methods: From March 2020 to May 2022, 656 breasts from 599 women (mean age 52.6 ± 11.5 years, including 0.6% [4/599] high-risk women) who underwent mammography and received positive AI-CAD results (Lunit Insight MMG, abnormality score ≥ 10) were retrospectively included in this study. Univariable and multivariable analyses were performed to evaluate the associations between the AI-CAD abnormality scores and clinical and radiological factors. The breasts were subdivided according to the abnormality scores into groups 1 (10-49), 2 (50-69), 3 (70-89), and 4 (90-100) using the optimal binning method. The PPVs were calculated for all breasts and subgroups. Results: Diagnostic indications and positive imaging findings by radiologists were associated with higher abnormality scores in the multivariable regression analysis. The overall PPV of AI-CAD was 32.5% (213/656) for all breasts, including 213 breast cancers, 129 breasts with benign biopsy results, and 314 breasts with benign outcomes in the follow-up or diagnostic studies. In the screening mammography subgroup, the PPVs were 18.6% (58/312) overall and 5.1% (12/235), 29.0% (9/31), 57.9% (11/19), and 96.3% (26/27) for score groups 1, 2, 3, and 4, respectively. The PPVs were significantly higher in women with diagnostic indications (45.1% [155/344]), palpability (51.9% [149/287]), fatty breasts (61.2% [60/98]), and certain imaging findings (masses with or without calcifications and distortion). Conclusion: PPV increased with increasing AI-CAD abnormality scores. The PPVs of AI-CAD satisfied the acceptable PPV range according to Breast Imaging-Reporting and Data System for screening mammography and were higher for diagnostic mammography.

• Korean Circulation Journal
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• v.53 no.11
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• pp.758-771
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• 2023

Background and Objectives: Paroxysmal atrial fibrillation (AF) is a major potential cause of embolic stroke of undetermined source (ESUS). However, identifying AF remains challenging because it occurs sporadically. Deep learning could be used to identify hidden AF based on the sinus rhythm (SR) electrocardiogram (ECG). We combined known AF risk factors and developed a deep learning algorithm (DLA) for predicting AF to optimize diagnostic performance in ESUS patients. Methods: A DLA was developed to identify AF using SR 12-lead ECG with the database consisting of AF patients and non-AF patients. The accuracy of the DLA was validated in 221 ESUS patients who underwent insertable cardiac monitor (ICM) insertion to identify AF. Results: A total of 44,085 ECGs from 12,666 patient were used for developing the DLA. The internal validation of the DLA revealed 0.862 (95% confidence interval, 0.850-0.873) area under the curve (AUC) in the receiver operating curve analysis. In external validation data from 221 ESUS patients, the diagnostic accuracy of DLA and AUC were 0.811 and 0.827, respectively, and DLA outperformed conventional predictive models, including CHARGE-AF, C2HEST, and HATCH. The combined model, comprising atrial ectopic burden, left atrial diameter and the DLA, showed excellent performance in AF prediction with AUC of 0.906. Conclusions: The DLA accurately identified paroxysmal AF using 12-lead SR ECG in patients with ESUS and outperformed the conventional models. The DLA model along with the traditional AF risk factors could be a useful tool to identify paroxysmal AF in ESUS patients.

• 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 Information Technology Applications and Management
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• v.29 no.5
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• pp.13-25
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• 2022

The purpose of this study is to explore the factors that influence the purchase of AI speakers in China. We integrate the Unified Theory of Acceptance and Use of Technology (UTAUT) and Task-technology fit (TTF) model into one model and put forward assumptions. According to the characteristics of AI speakers, we selected 6 independent variables, such as Performance Expectation, Effort Expectation, Social Influence, Facilitating Conditions, Task and Technology-characteristics. The final impact on purchase behavior is evaluated through Task-technology fit and purchase intention. After counting 478 samples, through SPSS22.0 and AMOS analysis, hypotheses have been proved by strong experimental data, except facilitating conditions. These results also imply that improving the technical level of AI speakers and enhancing consumers' purchasing intention are the central line of marketing. Based on this, we put forward several suggestions to marketers, including strengthening the research and development of AI speaker technology, and building a circle of friends of AI speakers.

• International journal of advanced smart convergence
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• v.13 no.1
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• pp.48-56
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• 2024

The application of artificial intelligence (AI) is becoming increasingly important to coping with air pollution. AI is effective in coping with it in various ways including air pollution forecasting, monitoring, and control, which is attracting a lot of attention. This attention has created high need for analyzing studies on AI and air pollution. To contribute for satisfying it, this study performed bibliometric analyses on the studies on AI and air pollution from 2012 to 2022 using the Web of Science database. This study analyzed them in various aspects such as the trend in the number of articles, the trend in the number of citations, the top 10 countries of origin, the top 10 research organizations, the top 10 research funding agencies, the top 10 journals, the top 10 articles in terms of total citations, and the distribution by languages. This study not only reports the bibliometric analysis results but also reveals the eight distinct features in the research steam in studies on AI and air pollution, identified from the bibliometric analysis results. They are expected to make a useful contribution for understanding the research stream in AI and air pollution.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1240-1245
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• 1990

Intelligent Control is an extended paradigm that subsumes both control and AI paradigms, each of which is limited by its own abstractions. Autonomy, as a design goal, offers an arena where both control and AI paradigms must be applied -and a challenge to the viability of both as independent entities. We discuss hierarchical event-based control architectures in which AI and Control paradigms can be integrated within a model-based approach. In a niodel-based system, knowledge is encapsulated in the form of models at the various layers to support the predefined system objectives. Concepts are illustrated with a robotmanaged space-borne chemical laboratory.

• Investigative Magnetic Resonance Imaging
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• v.26 no.1
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• pp.1-9
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• 2022

Artificial intelligence (AI) powered by deep learning (DL) has shown remarkable progress in image recognition tasks. Over the past decade, AI has proven its feasibility for applications in medical imaging. Various aspects of clinical practice in neuroimaging can be improved with the help of AI. For example, AI can aid in detecting brain metastases, predicting treatment response of brain tumors, generating a parametric map of dynamic contrast-enhanced MRI, and enhancing radiomics research by extracting salient features from input images. In addition, image quality can be improved via AI-based image reconstruction or motion artifact reduction. In this review, we summarize recent clinical applications of DL in various aspects of neuroimaging.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.619-620
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• 2023

본 논문에서는 AI 모델이 노출될 수 있는 적대적 공격을 연구한 논문이다. AI 쳇봇이 적대적 공격에 노출됨에 따라 최근 보안 침해 사례가 다수 발생하고 있다. 이에 대해 본 논문에서는 적대적 공격이 무엇인지 조사하고 적대적 공격에 대응하거나 사전에 방어하는 방안을 연구하고자 한다. 적대적 공격의 종류 4가지와 대응 방안을 조사하고, AI 모델의 보안 중요성을 강조하고 있다. 또한, 이런 적대적 공격을 방어할 수 있도록 대응 방안을 추가로 조사해야 한다고 결론을 내리고 있다.

• Journal of Korean Neurosurgical Society
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• v.58 no.6
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• pp.578-581
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• 2015

A rigid spino-pelvic fixation to anchor long constructs is crucial to maintain the stability of long fusion in spinal deformity surgery. Besides obtaining immediate stability and proper biomechanical strength of constructs, the S2 alar-iliac (S2AI) screws have some more advantages. Four Korean fresh-frozen human cadavers were procured. Free hand S2AI screw placement is performed using anatomic landmarks. The starting point of the S2AI screw is located at the midpoint between the S1 and S2 foramen and 2 mm medial to the lateral sacral crest. Gearshift was advanced from the desired starting point toward the sacro-iliac joint directing approximately $20^{\circ}$ angulation caudally in sagittal plane and $30^{\circ}$ angulation horizontally in the coronal plane connecting the posterior superior iliac spine (PSIS). We made a S2AI screw trajectory through the cancellous channel using the gearshift. We measured caudal angle in the sagittal plane and horizontal angle in the coronal plane. A total of eight S2AI screws were inserted in four cadavers. All screws inserted into the iliac crest were evaluated by C-arm and naked eye examination by two spine surgeons. Among 8 S2AI screws, all screws were accurately placed (100%). The average caudal angle in the sagittal plane was $17.3{\pm}5.4^{\circ}$. The average horizontal angle in the coronal plane connecting the PSIS was $32.0{\pm}1.8^{\circ}$. The placement of S2AI screws using the free hand technique without any radiographic guidance appears to an acceptable method of insertion without more radiation or time consuming.

• Tuberculosis and Respiratory Diseases
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• v.44 no.6
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• pp.1366-1381
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• 1997

Background : Uvulopalatopharyngoplasty(UPPP) has become the most common surgical treatment for obstructive sleep apnea syndrome(OSAS). However, the results of this therapeutic modality have been quite variable with successful results by several authors and poor results by others. Until recently, in Korea, there is only a few reports about the clinical efficacy of UPPP. A prospective study was undertaken to evaluate the effectiveness and complications of UPPP. Method : Twenty-six OSAS patients who had undergone UPPP with preoperative and postoperative polysomnographic studies were included in this study. Two definitions of surgical success were used. The responder was defined, using a conventional criteria, as a 50% or more reduction in apnea index(AI) or apneahypopnea index(AHI) after UPPP, or a postoperative AI of <10 or AHI of <20. The initial cure was defined, using our own criteria, as a postoperative AI of <5 or AHI of <10. Complications were categorized in two groups : early(disorders during the first 10 postoperative days) and late. Results : Eighteen patients(69.2%) were responders, and ten patients(38.5%) were considered as initial cure. On the other hand, in five patients (19.2%), postoperative polysomnographic data demonstrated deterioration compared with preoperative data. Reduction rate of AI or AHI following UPPP was not significantly related to the preoperative body mass index, AI or AHI. There was no significant change of sleep architecture before and after UPPP in responder and initial cure groups. Early complications such as pain, dyspnea, bleeding, nasal reflux, dysphagia or wound disruption were observed in all patients. Late complications such as nasal reflux, voice change, dysphagia, loss of taste, pharyngeal dryness or foreign body sensation were discovered in 22 patients (84.6%). However, all early and late complications were of minor importance. Conclusion : The response to UPPP was favorable in approximately 70% of OSAS patient. However, the initial Cure rate of UPPP was relatively low. We suggest that selection of more appropriate surgical candidates and adequate surgical protocol is necessary to obtain a more successful result with UPPP.