• Journal of the Korean Society of Radiology
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• v.18 no.3
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• pp.187-201
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• 2024

Osteoporosis is a major health issue globally, often remaining undetected until a fracture occurs. To facilitate early detection, deep learning (DL) models were developed to classify osteoporosis using abdominal computed tomography (CT) scans. This study was conducted using retrospectively collected data from 3,012 contrast-enhanced abdominal CT scans. The DL models developed in this study were constructed for using image data, demographic/clinical information, and multi-modality data, respectively. Patients were categorized into the normal, osteopenia, and osteoporosis groups based on their T-scores, obtained from dual-energy X-ray absorptiometry, into normal, osteopenia, and osteoporosis groups. The models showed high accuracy and effectiveness, with the combined data model performing the best, achieving an area under the receiver operating characteristic curve of 0.94 and an accuracy of 0.80. The image-based model also performed well, while the demographic data model had lower accuracy and effectiveness. In addition, the DL model was interpreted by gradient-weighted class activation mapping (Grad-CAM) to highlight clinically relevant features in the images, revealing the femoral neck as a common site for fractures. The study shows that DL can accurately identify osteoporosis stages from clinical data, indicating the potential of abdominal CT scans in early osteoporosis detection and reducing fracture risks with prompt treatment.

• Investigative Magnetic Resonance Imaging
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• v.13 no.1
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• pp.74-80
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• 2009

Purpose : To evaluate the characteristic MR imaging findings of Langerhans cell histiocytosis (LCH) in the skull and to compare them with those of plain radiography and computed tomography. Materials and Methods : A total of 10 lesions in 9 patients (Age range; 5-42 years, Mean age; 18, all women) with Langerhans cell histiocytosis in the skull were included in our study. Nine lesions in nine patients were histologically confirmed by surgery or fine needle aspiration biopsy. All patients performed with MRI, and plain radiography and CT scan were done in 7 patients (8 lesions). Two experienced neuroradiologists reviewed the radiological examinations independently with attention to location, size, shape and nature of the lesions in the skull and compared the extent and extension of the lesions to adjacent structures. Results : The lesions were distributed in all of the skulls without predilection site. On MRI, the masses were shown as well-enhancing soft tissue masses (10/10) mainly in diploic spaces (8/10) with extension to scalp (9/10) and dura mater (7/10). Dural enhancement (7/10) and thickening (4/10) were seen. The largest diameter of the soft tissue masses ranged 1.1 cm to 6.8 cm, shaped as round (5/10) or oval (5/10). On CT scans, the lesions were presented as soft tissue masses involving diploic space (6/8) and scalp extension (7/8) were also well visualized. Although bony erosion or destruction was more clearly seen on CT rather than those of MRI, enhancement of soft tissue masses and dura were not well visualized on CT. In contrast, all of the lesions in LCH were seen as punched out (4/8), beveled-edge appearance (4/8) osteolytic masses in plain radiography, but scalp and dural extension could not be seen. Conclusion : Characteristic MR findings in patients with LCH are soft tissue mass in diploic space with extension to dura and scalp, and MRI would be better imaging modality than plain radiography or CT.

• The Korean Journal of Nuclear Medicine
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• v.36 no.6
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• pp.344-356
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• 2002

Purpose: RBC blood pool SPECT has been used to diagnose focal liver lesion such as hemangioma owing to its high specificity. However, low spatial resolution is a major limitation of this modality. Recently, ordered subset expectation maximization (OSEM) has been introduced to obtain tomographic images for clinical application. We compared this new modified iterative reconstruction method, OSEM with conventional filtered back projection (FBP) in imaging of liver hemangioma. Materials and Methods: Sixty four projection data were acquired using dual head gamma camera in 28 lesions of 24 patients with cavernous hemangioma of liver and these raw data were transferred to LINUX based personal computer. After the replacement of header file as interfile, OSEM was performed under various conditions of subsets (1,2,4,8,16, and 32) and iteration numbers (1,2,4,8, and 16) to obtain the best setting for liver imaging. The best condition for imaging in our investigation was considered to be 4 iterations and 16 subsets. After then, all the images were processed by both FBP and OSEM. Three experts reviewed these images without any information. Results: According to blind review of 28 lesions, OSEM images revealed at least same or better image quality than those of FBP in nearly all cases. Although there showed no significant difference in detection of large lesions more than 3 cm, 5 lesions with 1.5 to 3 cm in diameter were detected by OSEM only. However, both techniques failed to depict 4 cases of small lesions less than 1.5 cm. Conclusion: OSEM revealed better contrast and define in depiction of liver hemangioma as well as higher sensitivity in detection of small lesions. Furthermore this reconstruction method dose not require high performance computer system or long reconstruction time, therefore OSEM is supposed to be good method that can be applied to RBC blood pool SPECT for the diagnosis of liver hemangioma.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.143-174
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• 2023

With the development of deep learning technologies, Artificial Intelligence powered Optical Character Recognition (AI-OCR) has evolved to read multiple languages from various forms of images accurately. For the financial industry, where a large number of diverse documents are processed through manpower, the potential for using AI-OCR is great. In this study, we present a configuration and a design of an AI-OCR modality for use in the financial industry and discuss the platform construction with application cases. Since the use of financial domain data is prohibited under the Personal Information Protection Act, we developed a deep learning-based data generation approach and used it to train the AI-OCR models. The AI-OCR models are trained for image preprocessing, text recognition, and language processing and are configured as a microservice architected platform to process a broad variety of documents. We have demonstrated the AI-OCR platform by applying it to financial domain tasks of document sorting, document verification, and typing assistance The demonstrations confirm the increasing work efficiency and conveniences.

• Tuberculosis and Respiratory Diseases
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• v.44 no.6
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• pp.1308-1317
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• 1997

Background : Pulmonary aspergillomas usually arise from colonization and proliferation of Aspergillus in preexisting cavitary lung disease of any cause. About 15% of patients with tuberculous pulmonary cavities were found to have aspergilloma. We analyzed the clinical features and course of 91 patients with pulmonary aspergilloma. Method : During the ten-year period from June 1986 to May 1996, 91 patients whose condition was diagnosed as pulmonary aspergilloma at 4 university hospitals in Taegu city were reviewed. All patients fulfilled one of the following criteria : 1) histologic evidence of aspergilloma within abnormal air space in tissue sections, or 2) a positive Aspergillus serum precipitin test with the radiologic finding of a fungus ball. The histological diagno-sis was established in 81 patients(89.0%) and clinical diagnosis in 10 patients(11.0%). Results : 1) The age range was 22 to 65 years, with an average of 45 years. A male and female ratio was 1.7 : 1 (57 men and 34 women). 2) Hemoptysis was far the most frequent symptom(89%), followed by cough, dyspnea, weakness, weight loss, fever, chest pain. 3) In all but 14 cases(15.4%) there had been associated conditions. Pulmonary tuberculosis was far the most frequent underlying condition found(74.7%), followed by bronchiectasis (6.6%), cavitary neoplasm(2.2%), pulmonary sequestration(1.1%). 4) The involved area was usually in the upper lobes; the right upper lobe was involved in 39(42.9%), the left upper lobe in 31(34.1%), the left lower lobe in 13(14.3%), the right lower lobe in 7(7.7%), and the right middle lobe in 1(1.1%). 5) On standard chest roent geno gram the classic "bell-like" image of a fungus ball was found in 62.6% of the subjects. On CT scan, 88.1% of the subjects in which they were done. 6) The surgical therapy was undertaken in 76 patients, and medical therapy in 15 patients, including 4 patients with intracavitary instillation of amphotericin B. 7) The surgical modality was lobectomy in 55 patients(72.4%), segmentectomy in 16 patients(21.1%), pneumonectomy in 4 patients(5.3%), wedge resection in 1 patient(1.3%). The mortality rate was 3.9% (3 patients) ; 2 patients died of sepsis and 1 died of hemoptysis. The postoperative complications were encountered in 6 patients (7.9%), including each one patient with respiratory failure, bleeding, bronchopleural fistula, empyema, and vocal cord paralysis. 8) In the follow-up cases, each 2 patients of 71 patients with surgical treatment and 10 patients with medical treatment had recurrent hemoptysis. Conclusion : During follow-up of the chronic pulmonary disease with abnormal air space, if the standard chest roentgenograms are insufficient to detect a fungus ball, computed tomographic scan and serum precipitin test are likely to aid the diagnosis of patients with suspected pulmonary aspergilloma. A reasonable recommendation for management of a patient with aspergilloma would be to reserve surgical resection for those patients who have had severe, recurrent hemoptysis. And a well controlled cooperative study to the medical treatment such as intracavitary antifungal therapy is further needed.