• Clinical and Experimental Pediatrics
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• v.49 no.5
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• pp.507-512
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• 2006

Purpose : Early detection and intervention of hearing impairment is believed to improve speech and language development and behavior of children. The aim of this preliminary study was to determine the prevalence of hearing impairments, and to identify the association of risk factors relating to refer response in high risk neonates who were screened using distortion product otoacoustic emissions (DPOAE). Methods : The subjects included 871 neonates who were admitted to the neonatal intensive care unit of the Pediatric Department in Soonchunhyang University Bucheon Hospital from May, 2001 to December, 2004. They were screened using DPOAE. Based on DPOAE, we divided the neonates in two groups : 'Pass' and 'Refer'. The differences in risk factors between the pass group and the refer group were analyzed. Results : The incidence of the refer group was 12.1 percent(106 out of 871). The bilateral refer rate was 5.4 percent(47 out of 871). And the unilateral refer rate was 6.7 percent(59 out of 871). Gender, birth place, family history of hearing loss, small/large for gestational age, obstetrical factor, hyperbilirubinemia and use of gentamicin were not statistically related to the refer rate. Statistically related to refer rate were birth weight, resuscitated neonates, Apgar score, craniofacial anomaly, mechanical ventilator application, sepsis, using of vancomycin(P<0.05). The prevalence of hearing impairment (${\geq}60dB$) in this study was 2 percent(18 out of 871). Conclusion : This study showed a higher prevalence of hearing impairment in high-risk neonates. Thus neonatal hearing screening should be carried out in high-risk neonates.

• Journal of Service Research and Studies
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• v.14 no.1
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• pp.13-26
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• 2024

Multi-modal generation is the process of generating results based on a variety of information, such as text, images, and audio. With the rapid development of AI technology, there is a growing number of multi-modal based systems that synthesize different types of data to produce results. In this paper, we present an AI system that uses speech and text recognition to describe a person and generate a montage image. While the existing montage generation technology is based on the appearance of Westerners, the montage generation system developed in this paper learns a model based on Korean facial features. Therefore, it is possible to create more accurate and effective Korean montage images based on multi-modal voice and text specific to Korean. Since the developed montage generation app can be utilized as a draft montage, it can dramatically reduce the manual labor of existing montage production personnel. For this purpose, we utilized persona-based virtual person montage data provided by the AI-Hub of the National Information Society Agency. AI-Hub is an AI integration platform aimed at providing a one-stop service by building artificial intelligence learning data necessary for the development of AI technology and services. The image generation system was implemented using VQGAN, a deep learning model used to generate high-resolution images, and the KoDALLE model, a Korean-based image generation model. It can be confirmed that the learned AI model creates a montage image of a face that is very similar to what was described using voice and text. To verify the practicality of the developed montage generation app, 10 testers used it and more than 70% responded that they were satisfied. The montage generator can be used in various fields, such as criminal detection, to describe and image facial features.