• Journal of the korean academy of Pediatric Dentistry
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• v.49 no.4
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• pp.357-367
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• 2022

Sleep disordered breathing (SDB) is a disease characterized by repeated hypopnea and apnea during sleep due to complete or partial obstruction of upper airway. The prevalence of pediatric SDB is approximately 12 - 15%, and the most common age group is preschool children aged 3 - 5 years. Children show more varied presentations, from snoring and frequent arousals to enuresis and hyperactivity. The main cause of pediatric SDB is obstruction of the upper airway related to enlarged tonsils and adenoids. If SDB is left untreated, it can cause complications such as learning difficulties, cognitive impairment, behavioral problems, cardiovascular disease, metabolic syndrome, and poor growth. Pediatric dentists are in a special position to identify children at risk for SDB. Pediatric dentists recognize clinical features related to SDB, and they should screen for SDB by using the pediatric sleep questionnaire (PSQ), lateral cephalometry radiograph, and portable sleep monitoring test and refer to sleep specialists. As a therapeutic approach, maxillary arch expansion treatment, mandible advancement device, and lingual frenectomy can be performed. Pediatric dentists should recognize that prolonged mouth breathing, lower tongue posture, and ankyloglossia can cause abnormal facial skeletal growth patterns and sleep problems. Pediatric dentists should be able to prevent these problems through early intervention.

• Journal of Korean Foot and Ankle Society
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• v.25 no.4
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• pp.149-156
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• 2021

Purpose: Given the lack of definite evidence-based guidelines in clinical practice, there may be a wide variation in treatment protocols for osteochondral lesions of the talus (OLT). Based on the Korean Foot and Ankle Society (KFAS) member survey, this study aimed to report the current trends in the management of OLT. Materials and Methods: A web-based questionnaire containing 30 questions was sent to all KFAS members in September 2021. The questions were mainly related to clinical experience and preferences in diagnosis, conservative, and surgical treatments for patients with OLT. Answers with a prevalence of ≥50% of respondents were considered a tendency. Results: Sixty-two (11.3%) of the 550 surgeons queried responded to the survey. The responses to 9 (30.0%) of the total of 30 questions established a tendency. Answers exhibiting a tendency were as follows; additional diagnostic tools except for plain radiograph (magnetic resonance imaging), most common conservative treatment method (oral medication, rest), most important radiological factor in decision making for surgical treatment and method (size of the lesion, ankle instability, loose bodies), most important patient factors in decision making for surgical treatment and method (age, activity or occupation), infrequently requiring posterior arthroscopy (less than 3%), most common revision surgery for failed bone marrow stimulation procedure (osteochondral autograft transplantation [OAT]), not requiring additional procedure for donor site in OAT, the main reason for unsatisfactory result after OAT (persistent pain without radiological abnormality), no generalization of autologous chondrocyte implantation or chondrogenesis using stem cells. Conclusion: This study presents updated information on current trends in the management of OLT in Korea. Both consensus and variations in the approach to patients with OLT were revealed through this survey. Since recent biologic efforts to regenerate cartilage have been unsuccessful, further studies to identify clinical evidence would be needed.

• Journal of the Korean Orthopaedic Association
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• v.54 no.2
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• pp.192-196
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• 2019

Iatrogenic calcinosis cutis is due to the intravenous administration of calcium gluconate or calcium chloride to treat hypocalcemia. The arthors report three cases of calcinosis cutis with calcifications involving the upper or lower extremities in neonates following the extravasation of calcium gluconate. Three neonates, a 2-week-old girl, 4-week-old boy, and a 4-week-old girl, were consulted for indurated nodules after the intravenous administration of calcium gluconate at the intensive care unit. Complete remission of palpable nodule and calcification was observed on the radiograph at three weeks, four weeks and six months after the initial presentation in each. All three neonates with iatrogenic calcinosis curtis were resolved spontaneously without functional and cosmetic complications. According to enhancement of the patient's cognition about benign disease, a suitable explanation of the disease and avoiding unnecessary treatment through an early diagnosis of iatrogenic calcinosis cutis will reduce a number of potential medical malpractice disputes.

• Korean Journal of Radiology
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• v.22 no.11
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• pp.1918-1928
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• 2021

Objective: With the recent development of various MRI-conditional cardiac implantable electronic devices (CIEDs), the accurate identification and characterization of CIEDs have become critical when performing MRI in patients with CIEDs. We aimed to develop and evaluate a deep learning-based algorithm (DLA) that performs the detection and characterization of parameters, including MRI safety, of CIEDs on chest radiograph (CR) in a single step and compare its performance with other related algorithms that were recently developed. Materials and Methods: We developed a DLA (X-ray CIED identification [XCID]) using 9912 CRs of 958 patients with 968 CIEDs comprising 26 model groups from 4 manufacturers obtained between 2014 and 2019 from one hospital. The performance of XCID was tested with an external dataset consisting of 2122 CRs obtained from a different hospital and compared with the performance of two other related algorithms recently reported, including PacemakerID (PID) and Pacemaker identification with neural networks (PPMnn). Results: The overall accuracies of XCID for the manufacturer classification, model group identification, and MRI safety characterization using the internal test dataset were 99.7% (992/995), 97.2% (967/995), and 98.9% (984/995), respectively. These were 95.8% (2033/2122), 85.4% (1813/2122), and 92.2% (1956/2122), respectively, with the external test dataset. In the comparative study, the accuracy for the manufacturer classification was 95.0% (152/160) for XCID and 91.3% for PPMnn (146/160), which was significantly higher than that for PID (80.0%,128/160; p < 0.001 for both). XCID demonstrated a higher accuracy (88.1%; 141/160) than PPMnn (80.0%; 128/160) in identifying model groups (p < 0.001). Conclusion: The remarkable and consistent performance of XCID suggests its applicability for detection, manufacturer and model identification, as well as MRI safety characterization of CIED on CRs. Further studies are warranted to guarantee the safe use of XCID in clinical practice.

• Journal of the Korean Society of Radiology
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• v.83 no.2
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• pp.265-283
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• 2022

To develop Korean coronavirus disease (COVID-19) chest imaging justification guidelines, eight key questions were selected and the following recommendations were made with the evidence-based clinical imaging guideline adaptation methodology. It is appropriate not to use chest imaging tests (chest radiograph or CT) for the diagnosis of COVID-19 in asymptomatic patients. If reverse transcription-polymerase chain reaction testing is not available or if results are delayed or are initially negative in the presence of symptoms suggestive of COVID-19, chest imaging tests may be considered. In addition to clinical evaluations and laboratory tests, chest imaging may be contemplated to determine hospital admission for asymptomatic or mildly symptomatic un-hospitalized patients with confirmed COVID-19. In hospitalized patients with confirmed COVID-19, chest imaging may be advised to determine or modify treatment alternatives. CT angiography may be considered if hemoptysis or pulmonary embolism is clinically suspected in a patient with confirmed COVID-19. For COVID-19 patients with improved symptoms, chest imaging is not recommended to make decisions regarding hospital discharge. For patients with functional impairment after recovery from COVID-19, chest imaging may be considered to distinguish a potentially treatable disease.

• Journal of the Korean Society of Radiology
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• v.81 no.4
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• pp.990-995
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• 2020

Chronic granulomatous disease (CGD) is an uncommon primary immune deficiency caused by phagocytes defective in oxygen metabolite production. It results in recurrent bacterial or fungal infections. Herein, we present a case of CGD with a large pulmonary granuloma in a neonate and review the imaging findings. The patient was a 24-day-old neonate admitted to the hospital with fever. A round opacified lesion was identified on the chest radiograph. Subsequent CT and MRI revealed a round mass with heterogeneous enhancement in the right lower lobe. There were foci of diffusion restriction in the mass. Surgical biopsy of the mass revealed chronic granuloma. Finally, the neonate was diagnosed with CGD caused by mutation of the gp91^phox gene. Herein, we present the clinical and imaging findings of this unusual case of CGD.

• Korean Journal of Radiology
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• v.24 no.3
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• pp.259-270
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• 2023

Objective: It is unknown whether artificial intelligence-based computer-aided detection (AI-CAD) can enhance the accuracy of chest radiograph (CR) interpretation in real-world clinical practice. We aimed to compare the accuracy of CR interpretation assisted by AI-CAD to that of conventional interpretation in patients who presented to the emergency department (ED) with acute respiratory symptoms using a pragmatic randomized controlled trial. Materials and Methods: Patients who underwent CRs for acute respiratory symptoms at the ED of a tertiary referral institution were randomly assigned to intervention group (with assistance from an AI-CAD for CR interpretation) or control group (without AI assistance). Using a commercial AI-CAD system (Lunit INSIGHT CXR, version 2.0.2.0; Lunit Inc.). Other clinical practices were consistent with standard procedures. Sensitivity and false-positive rates of CR interpretation by duty trainee radiologists for identifying acute thoracic diseases were the primary and secondary outcomes, respectively. The reference standards for acute thoracic disease were established based on a review of the patient's medical record at least 30 days after the ED visit. Results: We randomly assigned 3576 participants to either the intervention group (1761 participants; mean age ± standard deviation, 65 ± 17 years; 978 males; acute thoracic disease in 472 participants) or the control group (1815 participants; 64 ± 17 years; 988 males; acute thoracic disease in 491 participants). The sensitivity (67.2% [317/472] in the intervention group vs. 66.0% [324/491] in the control group; odds ratio, 1.02 [95% confidence interval, 0.70-1.49]; P = 0.917) and false-positive rate (19.3% [249/1289] vs. 18.5% [245/1324]; odds ratio, 1.00 [95% confidence interval, 0.79-1.26]; P = 0.985) of CR interpretation by duty radiologists were not associated with the use of AI-CAD. Conclusion: AI-CAD did not improve the sensitivity and false-positive rate of CR interpretation for diagnosing acute thoracic disease in patients with acute respiratory symptoms who presented to the ED.

• Korean Journal of Radiology
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• v.23 no.10
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• pp.1009-1018
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• 2022

Objective: This study aimed to investigate the feasibility of using artificial intelligence (AI) to identify normal chest radiography (CXR) from the worklist of radiologists in a health-screening environment. Materials and Methods: This retrospective simulation study was conducted using the CXRs of 5887 adults (mean age ± standard deviation, 55.4 ± 11.8 years; male, 4329) from three health screening centers in South Korea using a commercial AI (Lunit INSIGHT CXR3, version 3.5.8.8). Three board-certified thoracic radiologists reviewed CXR images for referable thoracic abnormalities and grouped the images into those with visible referable abnormalities (identified as abnormal by at least one reader) and those with clearly visible referable abnormalities (identified as abnormal by at least two readers). With AI-based simulated exclusion of normal CXR images, the percentages of normal images sorted and abnormal images erroneously removed were analyzed. Additionally, in a random subsample of 480 patients, the ability to identify visible referable abnormalities was compared among AI-unassisted reading (i.e., all images read by human readers without AI), AI-assisted reading (i.e., all images read by human readers with AI assistance as concurrent readers), and reading with AI triage (i.e., human reading of only those rendered abnormal by AI). Results: Of 5887 CXR images, 405 (6.9%) and 227 (3.9%) contained visible and clearly visible abnormalities, respectively. With AI-based triage, 42.9% (2354/5482) of normal CXR images were removed at the cost of erroneous removal of 3.5% (14/405) and 1.8% (4/227) of CXR images with visible and clearly visible abnormalities, respectively. In the diagnostic performance study, AI triage removed 41.6% (188/452) of normal images from the worklist without missing visible abnormalities and increased the specificity for some readers without decreasing sensitivity. Conclusion: This study suggests the feasibility of sorting and removing normal CXRs using AI with a tailored cut-off to increase efficiency and reduce the workload of radiologists.

• Korean Journal of Radiology
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• v.24 no.11
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• pp.1151-1163
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• 2023

Objective: To develop a deep-learning-based bone age prediction model optimized for Korean children and adolescents and evaluate its feasibility by comparing it with a Greulich-Pyle-based deep-learning model. Materials and Methods: A convolutional neural network was trained to predict age according to the bone development shown on a hand radiograph (bone age) using 21036 hand radiographs of Korean children and adolescents without known bone development-affecting diseases/conditions obtained between 1998 and 2019 (median age [interquartile range {IQR}], 9 [7-12] years; male:female, 11794:9242) and their chronological ages as labels (Korean model). We constructed 2 separate external datasets consisting of Korean children and adolescents with healthy bone development (Institution 1: n = 343; median age [IQR], 10 [4-15] years; male: female, 183:160; Institution 2: n = 321; median age [IQR], 9 [5-14] years; male: female, 164:157) to test the model performance. The mean absolute error (MAE), root mean square error (RMSE), and proportions of bone age predictions within 6, 12, 18, and 24 months of the reference age (chronological age) were compared between the Korean model and a commercial model (VUNO Med-BoneAge version 1.1; VUNO) trained with Greulich-Pyle-based age as the label (GP-based model). Results: Compared with the GP-based model, the Korean model showed a lower RMSE (11.2 vs. 13.8 months; P = 0.004) and MAE (8.2 vs. 10.5 months; P = 0.002), a higher proportion of bone age predictions within 18 months of chronological age (88.3% vs. 82.2%; P = 0.031) for Institution 1, and a lower MAE (9.5 vs. 11.0 months; P = 0.022) and higher proportion of bone age predictions within 6 months (44.5% vs. 36.4%; P = 0.044) for Institution 2. Conclusion: The Korean model trained using the chronological ages of Korean children and adolescents without known bone development-affecting diseases/conditions as labels performed better in bone age assessment than the GP-based model in the Korean pediatric population. Further validation is required to confirm its accuracy.

• Korean Journal of Radiology
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• v.23 no.3
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• pp.343-354
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• 2022

Objective: To develop and evaluate a deep learning-based artificial intelligence (AI) model for detecting skull fractures on plain radiographs in children. Materials and Methods: This retrospective multi-center study consisted of a development dataset acquired from two hospitals (n = 149 and 264) and an external test set (n = 95) from a third hospital. Datasets included children with head trauma who underwent both skull radiography and cranial computed tomography (CT). The development dataset was split into training, tuning, and internal test sets in a ratio of 7:1:2. The reference standard for skull fracture was cranial CT. Two radiology residents, a pediatric radiologist, and two emergency physicians participated in a two-session observer study on an external test set with and without AI assistance. We obtained the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity along with their 95% confidence intervals (CIs). Results: The AI model showed an AUROC of 0.922 (95% CI, 0.842-0.969) in the internal test set and 0.870 (95% CI, 0.785-0.930) in the external test set. The model had a sensitivity of 81.1% (95% CI, 64.8%-92.0%) and specificity of 91.3% (95% CI, 79.2%-97.6%) for the internal test set and 78.9% (95% CI, 54.4%-93.9%) and 88.2% (95% CI, 78.7%-94.4%), respectively, for the external test set. With the model's assistance, significant AUROC improvement was observed in radiology residents (pooled results) and emergency physicians (pooled results) with the difference from reading without AI assistance of 0.094 (95% CI, 0.020-0.168; p = 0.012) and 0.069 (95% CI, 0.002-0.136; p = 0.043), respectively, but not in the pediatric radiologist with the difference of 0.008 (95% CI, -0.074-0.090; p = 0.850). Conclusion: A deep learning-based AI model improved the performance of inexperienced radiologists and emergency physicians in diagnosing pediatric skull fractures on plain radiographs.