• Korean Journal of Radiology
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• v.22 no.12
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• pp.2006-2016
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• 2021

The expanding scope of interventional musculoskeletal procedures has resulted in increased pressure on general radiologists. The confidence of general radiologists in performing ultrasound-guided musculoskeletal procedures varies with their clinical exposure. This didactic review provides a methodologically and clinically oriented approach to enhancing user understanding and confidence in performing ultrasound-guided musculoskeletal procedures. The body of the text is accompanied by figures depicting the procedural approach, injection site, and labeled ultrasonography images. This paper aims to provide a teaching and bedside aid for education on and the execution of musculoskeletal procedures to ensure the provision of quality health care.

• Journal of radiological science and technology
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• v.31 no.3
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• pp.217-222
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• 2008

Radiation dose to radiologists working at three hospitals in Seoul was investigated from Jan 1, 2006 to Dec. 31, 2006. The results are as follows. First, radiation dose to radiologists at a cardiac angiography room was measured as 1.41mSv, the highest while radiation dose to radiologists at a department of radiation oncology was measured as 0.64 mSv, the lowest. Second, radiation dose proves to be in direct proportion to the number of X-ray treatment. Third, as for the radiation dose in X-ray treatments, radiologists in cardiac angiography room are exposed to the largest amount of radiation while radiologists in diagnostic radiology department are exposed to the smallest amount of radiation. Last, radiation dose at a cardiac angiography room is the largest and is followed by nuclear medicine, diagnostic radiology, and radiation oncology departments in order. According to ICRP, exposure less than 20mSv per year is highly recommended while radiation dose is allowed as long as it is ranged less than 50mSv per year or 100mSv within a 5-year period. Taking into account the results, radiation exposure does not do any harm to radiologists at any related departments in Korean hospitals because the dose per year is less than 1.60mSv.

• Korean Journal of Radiology
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• v.23 no.7
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• pp.732-741
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• 2022

Objective: To determine the impact of dedicated subspecialized radiologists in multidisciplinary team (MDT) discussions on the management of lower gastrointestinal (GI) tract malignancies. Materials and Methods: We retrospectively analyzed the data of 244 patients (mean age ± standard deviation, 61.7 ± 11.9 years) referred to MDT discussions 249 times (i.e., 249 cases, as five patients were discussed twice for different issues) for lower GI tract malignancy including colorectal cancer, small bowel cancer, GI stromal tumor, and GI neuroendocrine tumor between April 2018 and June 2021 in a prospective database. Before the MDT discussions, dedicated GI radiologists reviewed all imaging studies again besides routine clinical reading. The referring clinician's initial diagnosis, initial treatment plan, change in radiologic interpretation compared with the initial radiology report, and the MDT's consensus recommendations for treatment were collected and compared. Factors associated with changes in treatment plans and the implementation of MDT decisions were analyzed. Results: Of the 249 cases, radiologic interpretation was changed in 73 cases (29.3%) after a review by dedicated GI radiologists, with 78.1% (57/73) resulting in changes in the treatment plan. The treatment plan was changed in 92 cases (36.9%), and the rate of change in the treatment plan was significantly higher in cases with changes in radiologic interpretation than in those without (78.1% [57/73] vs. 19.9% [35/176], p < 0.001). Follow-up records of patients showed that 91.2% (227/249) of MDT recommendations for treatment were implemented. Multiple logistic regression analysis revealed that the nonsurgical approach (vs. surgical approach) decided through MDT discussion was a significant factor for patients being managed differently than the MDT recommendations (odds ratio, 4.48; p = 0.017). Conclusion: MDT discussion involving additional review of radiology examinations by dedicated GI radiologists resulted in a change in the treatment plan in 36.9% of cases. Changes in treatment plans were significantly associated with changes in radiologic interpretation.

• Korean Journal of Radiology
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• v.21 no.10
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• pp.1150-1160
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• 2020

Objective: To describe the experience of implementing a deep learning-based computer-aided detection (CAD) system for the interpretation of chest X-ray radiographs (CXR) of suspected coronavirus disease (COVID-19) patients and investigate the diagnostic performance of CXR interpretation with CAD assistance. Materials and Methods: In this single-center retrospective study, initial CXR of patients with suspected or confirmed COVID-19 were investigated. A commercialized deep learning-based CAD system that can identify various abnormalities on CXR was implemented for the interpretation of CXR in daily practice. The diagnostic performance of radiologists with CAD assistance were evaluated based on two different reference standards: 1) real-time reverse transcriptase-polymerase chain reaction (rRT-PCR) results for COVID-19 and 2) pulmonary abnormality suggesting pneumonia on chest CT. The turnaround times (TATs) of radiology reports for CXR and rRT-PCR results were also evaluated. Results: Among 332 patients (male:female, 173:159; mean age, 57 years) with available rRT-PCR results, 16 patients (4.8%) were diagnosed with COVID-19. Using CXR, radiologists with CAD assistance identified rRT-PCR positive COVID-19 patients with sensitivity and specificity of 68.8% and 66.7%, respectively. Among 119 patients (male:female, 75:44; mean age, 69 years) with available chest CTs, radiologists assisted by CAD reported pneumonia on CXR with a sensitivity of 81.5% and a specificity of 72.3%. The TATs of CXR reports were significantly shorter than those of rRT-PCR results (median 51 vs. 507 minutes; p < 0.001). Conclusion: Radiologists with CAD assistance could identify patients with rRT-PCR-positive COVID-19 or pneumonia on CXR with a reasonably acceptable performance. In patients suspected with COVID-19, CXR had much faster TATs than rRT-PCRs.

• Journal of the Korean Society of Radiology
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• v.82 no.1
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• pp.49-65
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• 2021

With the increase in the number of cases of silicone implant insertion either for cosmetic surgery or breast reconstruction after mastectomy, it is not unusual to encounter patients with silicone implants in clinical settings. Recently, the first case of breast implant-associated anaplastic large cell lymphoma was reported in Korea. In addition to previously known complications, such as implant rupture or contracture, the number of implant-associated imaging examinations has also increased. Considering this background, radiologists should have sufficient knowledge about the type of examination required in patients who have undergone implant insertion and imaging findings to correctly identify implant-associated complications. In this article, various complications of silicone implants are discussed, including various imaging findings, which radiologists should know.

• Korean Journal of Radiology
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• v.24 no.2
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• pp.155-165
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• 2023

Objective: Little is known about the effects of using different expert-determined reference standards when evaluating the performance of deep learning-based automatic detection (DLAD) models and their added value to radiologists. We assessed the concordance of expert-determined standards with a clinical gold standard (herein, pathological confirmation) and the effects of different expert-determined reference standards on the estimates of radiologists' diagnostic performance to detect malignant pulmonary nodules on chest radiographs with and without the assistance of a DLAD model. Materials and Methods: This study included chest radiographs from 50 patients with pathologically proven lung cancer and 50 controls. Five expert-determined standards were constructed using the interpretations of 10 experts: individual judgment by the most experienced expert, majority vote, consensus judgments of two and three experts, and a latent class analysis (LCA) model. In separate reader tests, additional 10 radiologists independently interpreted the radiographs and then assisted with the DLAD model. Their diagnostic performance was estimated using the clinical gold standard and various expert-determined standards as the reference standard, and the results were compared using the t test with Bonferroni correction. Results: The LCA model (sensitivity, 72.6%; specificity, 100%) was most similar to the clinical gold standard. When expert-determined standards were used, the sensitivities of radiologists and DLAD model alone were overestimated, and their specificities were underestimated (all p-values < 0.05). DLAD assistance diminished the overestimation of sensitivity but exaggerated the underestimation of specificity (all p-values < 0.001). The DLAD model improved sensitivity and specificity to a greater extent when using the clinical gold standard than when using the expert-determined standards (all p-values < 0.001), except for sensitivity with the LCA model (p = 0.094). Conclusion: The LCA model was most similar to the clinical gold standard for malignant pulmonary nodule detection on chest radiographs. Expert-determined standards caused bias in measuring the diagnostic performance of the artificial intelligence model.

• Korean Journal of Radiology
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• v.22 no.4
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• pp.612-623
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• 2021

Objective: To evaluate the diagnostic performance of a deep learning algorithm for the automated detection of developmental dysplasia of the hip (DDH) on anteroposterior (AP) radiographs. Materials and Methods: Of 2601 hip AP radiographs, 5076 cropped unilateral hip joint images were used to construct a dataset that was further divided into training (80%), validation (10%), or test sets (10%). Three radiologists were asked to label the hip images as normal or DDH. To investigate the diagnostic performance of the deep learning algorithm, we calculated the receiver operating characteristics (ROC), precision-recall curve (PRC) plots, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) and compared them with the performance of radiologists with different levels of experience. Results: The area under the ROC plot generated by the deep learning algorithm and radiologists was 0.988 and 0.988-0.919, respectively. The area under the PRC plot generated by the deep learning algorithm and radiologists was 0.973 and 0.618-0.958, respectively. The sensitivity, specificity, PPV, and NPV of the proposed deep learning algorithm were 98.0, 98.1, 84.5, and 99.8%, respectively. There was no significant difference in the diagnosis of DDH by the algorithm and the radiologist with experience in pediatric radiology (p = 0.180). However, the proposed model showed higher sensitivity, specificity, and PPV, compared to the radiologist without experience in pediatric radiology (p < 0.001). Conclusion: The proposed deep learning algorithm provided an accurate diagnosis of DDH on hip radiographs, which was comparable to the diagnosis by an experienced radiologist.

• Korean Journal of Radiology
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• v.22 no.12
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• pp.2017-2025
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• 2021

Objective: To evaluate the accuracy and clinical efficacy of a hybrid Greulich-Pyle (GP) and modified Tanner-Whitehouse (TW) artificial intelligence (AI) model for bone age assessment. Materials and Methods: A deep learning-based model was trained on an open dataset of multiple ethnicities. A total of 102 hand radiographs (51 male and 51 female; mean age ± standard deviation = 10.95 ± 2.37 years) from a single institution were selected for external validation. Three human experts performed bone age assessments based on the GP atlas to develop a reference standard. Two study radiologists performed bone age assessments with and without AI model assistance in two separate sessions, for which the reading time was recorded. The performance of the AI software was assessed by comparing the mean absolute difference between the AI-calculated bone age and the reference standard. The reading time was compared between reading with and without AI using a paired t test. Furthermore, the reliability between the two study radiologists' bone age assessments was assessed using intraclass correlation coefficients (ICCs), and the results were compared between reading with and without AI. Results: The bone ages assessed by the experts and the AI model were not significantly different (11.39 ± 2.74 years and 11.35 ± 2.76 years, respectively, p = 0.31). The mean absolute difference was 0.39 years (95% confidence interval, 0.33-0.45 years) between the automated AI assessment and the reference standard. The mean reading time of the two study radiologists was reduced from 54.29 to 35.37 seconds with AI model assistance (p < 0.001). The ICC of the two study radiologists slightly increased with AI model assistance (from 0.945 to 0.990). Conclusion: The proposed AI model was accurate for assessing bone age. Furthermore, this model appeared to enhance the clinical efficacy by reducing the reading time and improving the inter-observer reliability.

• Korean Journal of Radiology
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• v.24 no.5
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• pp.406-423
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• 2023

Diagnosing bowel and mesenteric trauma poses a significant challenge to radiologists. Although these injuries are relatively rare, immediate laparotomy may be indicated when they occur. Delayed diagnosis and treatment are associated with increased morbidity and mortality; therefore, timely and accurate management is essential. Additionally, employing strategies to differentiate between major injuries requiring surgical intervention and minor injuries considered manageable via non-operative management is important. Bowel and mesenteric injuries are among the most frequently overlooked injuries on trauma abdominal computed tomography (CT), with up to 40% of confirmed surgical bowel and mesenteric injuries not reported prior to operative treatment. This high percentage of falsely negative preoperative diagnoses may be due to several factors, including the relative rarity of these injuries, subtle and non-specific appearances on CT, and limited awareness of the injuries among radiologists. To improve the awareness and diagnosis of bowel and mesenteric injuries, this article provides an overview of the injuries most often encountered, imaging evaluation, CT appearances, and diagnostic pearls and pitfalls. Enhanced diagnostic imaging awareness will improve the preoperative diagnostic yield, which will save time, money, and lives.

• Korean Journal of Radiology
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• v.24 no.8
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• pp.752-760
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• 2023

Radiologists and trauma surgeons should monitor for early killers among patients with thoracic trauma, such as tension pneumothorax, tracheobronchial injuries, flail chest, aortic injury, mediastinal hematomas, and severe pulmonary parenchymal injury. With the advent of cutting-edge technology, rapid volumetric computed tomography of the chest has become the most definitive diagnostic tool for establishing or excluding thoracic trauma. With the notion of "time is life" at emergency settings, radiologists must find ways to shorten the turnaround time of reports. One way to interpret chest findings is to use a systemic approach, as advocated in this study. Our interpretation of chest findings for thoracic trauma follows the acronym "ABC-Please" in which "A" stands for abnormal air, "B" stands for abnormal bones, "C" stands for abnormal cardiovascular system, and "P" in "Please" stands for abnormal pulmonary parenchyma and vessels. In the future, utilizing an artificial intelligence software can be an alternative, which can highlight significant findings as "warm zones" on the heatmap and can re-prioritize important examinations at the top of the reading list for radiologists to expedite the final reports.