Bone Age Assessment Using Artificial Intelligence in Korean Pediatric Population: A Comparison of Deep-Learning Models Trained With Healthy Chronological and Greulich-Pyle Ages as Labels

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.

Artificial Intelligence in Health Care: Current Applications and Issues

In recent years, artificial intelligence (AI) technologies have greatly advanced and become a reality in many areas of our daily lives. In the health care field, numerous efforts are being made to implement the AI technology for practical medical treatments. With the rapid developments in machine learning algorithms and improvements in hardware performances, the AI technology is expected to play an important role in effectively analyzing and utilizing extensive amounts of health and medical data. However, the AI technology has various unique characteristics that are different from the existing health care technologies. Subsequently, there are a number of areas that need to be supplemented within the current health care system for the AI to be utilized more effectively and frequently in health care. In addition, the number of medical practitioners and public that accept AI in the health care is still low;moreover, there are various concerns regarding the safety and reliability of AI technologyimplementations. Therefore, this paper aims to introduce the current research and application status of AI technology in health care and discuss the issues that need to be resolved.

Deep Learning Algorithm for Reducing CT Slice Thickness: Effect on Reproducibility of Radiomic Features in Lung Cancer

OBJECTIVE: To retrospectively assess the effect of CT slice thickness on the reproducibility of radiomic features (RFs) of lung cancer, and to investigate whether convolutional neural network (CNN)-based super-resolution (SR) algorithms can improve the reproducibility of RFs obtained from images with different slice thicknesses. MATERIALS AND METHODS: CT images with 1-, 3-, and 5-mm slice thicknesses obtained from 100 pathologically proven lung cancers between July 2017 and December 2017 were evaluated. CNN-based SR algorithms using residual learning were developed to convert thick-slice images into 1-mm slices. Lung cancers were semi-automatically segmented and a total of 702 RFs (tumor intensity, texture, and wavelet features) were extracted from 1-, 3-, and 5-mm slices, as well as the 1-mm slices generated from the 3- and 5-mm images. The stabilities of the RFs were evaluated using concordance correlation coefficients (CCCs). RESULTS: The mean CCCs for the comparisons of original 1 mm vs. 3 mm, 1 mm vs. 5 mm, and 3 mm vs. 5 mm images were 0.41, 0.27, and 0.65, respectively (p < 0.001 for all comparisons). Tumor intensity features showed the best reproducibility while wavelets showed the lowest reproducibility. The majority of RFs failed to achieve reproducibility (CCC ≥ 0.85; 3.6%, 1.0%, and 21.5%, respectively). After applying the CNN-based SR algorithms, the reproducibility significantly improved in all three pairings (mean CCCs: 0.58, 0.45, and 0.72; p < 0.001 for all comparisons). The reproducible RFs also increased (36.3%, 17.4%, and 36.9%, respectively). CONCLUSION: The reproducibility of RFs in lung cancer is significantly influenced by CT slice thickness, which can be improved by the CNN-based SR algorithms.

A Novel Fundus Image Reading Tool for Efficient Generation of a Multi-dimensional Categorical Image Database for Machine Learning Algorithm Training

BACKGROUND: We described a novel multi-step retinal fundus image reading system for providing high-quality large data for machine learning algorithms, and assessed the grader variability in the large-scale dataset generated with this system. METHODS: A 5-step retinal fundus image reading tool was developed that rates image quality, presence of abnormality, findings with location information, diagnoses, and clinical significance. Each image was evaluated by 3 different graders. Agreements among graders for each decision were evaluated. RESULTS: The 234,242 readings of 79,458 images were collected from 55 licensed ophthalmologists during 6 months. The 34,364 images were graded as abnormal by at-least one rater. Of these, all three raters agreed in 46.6% in abnormality, while 69.9% of the images were rated as abnormal by two or more raters. Agreement rate of at-least two raters on a certain finding was 26.7%–65.2%, and complete agreement rate of all-three raters was 5.7%–43.3%. As for diagnoses, agreement of at-least two raters was 35.6%–65.6%, and complete agreement rate was 11.0%–40.0%. Agreement of findings and diagnoses were higher when restricted to images with prior complete agreement on abnormality. Retinal/glaucoma specialists showed higher agreements on findings and diagnoses of their corresponding subspecialties. CONCLUSION: This novel reading tool for retinal fundus images generated a large-scale dataset with high level of information, which can be utilized in future development of machine learning-based algorithms for automated identification of abnormal conditions and clinical decision supporting system. These results emphasize the importance of addressing grader variability in algorithm developments.

Deep Learning for Medical Image Analysis: Applications to Computed Tomography and Magnetic Resonance Imaging / 한양의대학술지

Recent advances in deep learning have brought many breakthroughs in medical image analysis by providing more robust and consistent tools for the detection, classification and quantification of patterns in medical images. Specifically, analysis of advanced modalities such as computed tomography (CT) and magnetic resonance imaging (MRI) has benefited most from the data-driven nature of deep learning. This is because the need of knowledge and experience-oriented feature engineering process can be circumvented by automatically deriving representative features from the complex high dimensional medical images with respect to the target tasks. In this paper, we will review recent applications of deep learning in the analysis of CT and MR images in a range of tasks and target organs. While most applications are focused on the enhancement of the productivity and accuracy of current diagnostic analysis, we will also introduce some promising applications which will significantly change the current workflow of medical imaging. We will conclude by discussing opportunities and challenges of applying deep learning to advanced imaging and suggest future directions in this domain.

A Case of Cavernous Lymphangioma Originated in the Middle Ear Cavity / 대한이비인후과학회지

Lymphangioma is a rare benign congenital tumor involving both the head and the neck and causing obstructing symptoms in the upper airways as well as aesthetic anomalies. About 90% of the cases occur within 2 years of age, except for a few cases, which occur in adulthood. The lesions can grow rapidly with infection, truma or bleeding, resulting in disfigurement as well as severe impairment of respiraton, swallowing and speech. The middle ear lymphangioma is very extreme. There have been no previously reported cases of middle ear lymphangioma in Korea. The treatment of choice for lymphangioma located in the middle ear is surgical excision. We present and discuss this rare case with a review of the literature.

A Case of Bilateral Sudden Hearing Loss and Tinnitus after Salicylate Intoxication

Salicylate, the active ingredient of aspirin can cause sensorineural hearing loss and tinnitus when plasma concentrations reach a critical level. The ototoxic mechanisms of salicylate remain unclear but hearing and tinnitus usually recovers a few days after intoxication. There have been few reports of salicylate-induced ototoxicity in Korea, and the majority is caused by a low dose of aspirin. Herein, we report a case of sudden hearing loss and tinnitus after acute salicylate intoxication and review recent updates on salicylate ototoxicity.

Korean Thyroid Association Guidelines for Patients Undergoing Radioiodine Therapy for Differentiated Thyroid Cancers (First Edition, 2012) / 대한갑상선학회지

No abstract available.

Early Effect of Barbecue Rotation and Modified Semont Combination in Apogeotropic Horizontal Canal Benign Paroxysmal Positional Vertigo / 대한이비인후과학회지

BACKGROUND AND OBJECTIVES: Benign paroxysmal positional vertigo (BPPV) of horizontal canal shows reversible direction changing positional nystagmus and diverse clinical courses because of the frequent clinical presentation of cupulolithiasis. The aim of this study is to find out the early effect of particle repositioning maneuver (PRM) including the barbecue and the modified Semont in single treatment session for the apogeotropic horizontal canal BPPV. SUBJECTS AND METHOD: Thirty-three episodic vertigo patients with direction-changing apogeotropic horizontal nystagmus were enrolled in this study. The patients were initially treated with barbecue rotation and the second PRM was applied 15 minutes after the first PRM. Barbecue rotation was applied when the positional nystagmus was changed its direction to that of geotropic. The modified Semont maneuver or barbecue rotation was randomly applied when nystagmus was not changed. Patients were followed-up 2 or 3 days after the initial visit and the nystagmus was rechecked to determine the single session treatment result. RESULTS: The combination of PRMs in a single treatment session was effective in 23 out of 33 (69.7%) patients. An initial barbecue rotation was effective in 17 patients (51.5%); geotropic nystagmus was obtained in 12, and no nystagmus in 5. Eleven out of 16 (68.8%) patients with persistent apogeotropic nystagmus after initial treatment were successfully treated with the second PRM. There was no statistical difference between the second PRMs of the modified Semont maneuver and barbecue rotation (p=1.000). CONCLUSION: The combination of PRMs including barbecue rotation and the modified Semont maneuver in a single treatment session showed a comparable success rate as the previously reported studies.

A Child of Palatal Myoclonus Tinnitus Treated with Intraoral Injection of Botulinum Toxin / 대한이비인후과학회지

Palatal myoclonus (palatal tremor) was reported to be presented with a sole symptom of objective pulsating tinnitus and could be controlled by botulinum toxin injection alone. However, there were 3 cases reported in Korea of palatal myoclonus tinnitus controlled with botulinum toxin injection through the dual administration route of the mouth and nasal cavity. We present an 11-year-old boy of essential palatal myoclonus tinnitus, which was controlled by intraoral botulinum toxin injection to the tensor veli palatini muscle alone. Intraoral injection of botulinum toxin to the anatomical location of tensor veli palatini muscle with the guidance of electromyography was effective and safe for the child of objective tinnitus caused by palatal myoclonus.