Leigh Disease, or subacute necrotizing encephalopathy (SNE), is a degenerative disorder characterized by lesions of the gray and white matter in the bran and spinal cord. The pathogenesis was known as mitochondrial enzyme defect of the respiratory chain system. We experienced 2 cases of Leigh disease. The first case, a seven-month old girl who was presented with weak respiration and failure to thrive, showed lactic acidemia and increased lactic acid in CSF fluid, high signal intensity in the bilateral putamen and head of caudate of nucleus at T2 weighted MR imaging. The second case, a 3-year-old girl with ataxic gait and bilateral ptosis also showed lactic acidemia, increased lactic acid in CSF fluid and high signal intensity in the bilateral basal ganglia. Respiratory difficuly developed in both cases and died within 1 month after visiting our hospital. The diagnosis was made by lactic acidosis and specific MRI finding. We report these cases with a brief review of its related literature.
Acidosis, Lactic ; Basal Ganglia ; Child, Preschool ; Diagnosis ; Electron Transport ; Failure to Thrive ; Female ; Gait ; Head ; Humans ; Lactic Acid ; Leigh Disease* ; Magnetic Resonance Imaging ; Putamen ; Respiration ; Spinal Cord

Background: Swallowing function deterioration is a common problem experienced by older adults worldwide. Many studies have been conducted to improve swallowing function in older adults; however, due to differences in intervention methods and study designs, it is difficult to draw a common conclusion. This study aimed to analyze trends and intervention methods in studies of swallowing function intervention for older adults conducted from 2010 to 2022, to establish a systematic approach for developing interventions to improve swallowing function in older adults and to provide evidence for this approach. Methods: Literature research was conducted for studies published between 2010 and 2022 that applied to swallow function interventions to adults aged 60 years or older. Databases including PubMed, Medline, RISS, Science On, KISS, and KCI were used. From a total of 1,164 articles searched using keywords, 20 articles were selected for final analysis. Results: The number of published articles steadily increased over time, and the intervention period was most commonly 6 or 8 weeks. The types of interventions included focused exercises to improve oral muscle strength in 12 articles and programs incorporating education, practice, and expert management in 8 articles. Among the focused exercises, tongue-strengthening exercises were most common in 4 articles. The evaluation variables for intervention effects were muscle strength evaluation, oral function evaluation, quality of life, and oral health and hygiene status. Muscle strength and oral function evaluations were statistically significant in focused exercise interventions, while the quality of life and oral health and hygiene status was significant in program interventions. Conclusion This literature review is meaningful as a study that can be used to select the intervention period and program contents when planning an elderly swallowing intervention program.

Metastases to the heart are rarely diagnosed before the patient dies. A 26-year-old man was admitted with multiple metastasis of a testicular embryonal carcinoma and he was found to have intracardiac metastasis. Echocardiography showed that he had a mass rising from the interventricular septum and it was floating through the right ventricular outflow tract. The histology of the mass we removed from the right ventricle was consistent with testicular embryonal carcinoma. The patient made a smooth recovery after surgical intervention and chemotherapy. We believe this is the first reported case of testicular embryonal carcinoma that metastasized to the heart and that was successfully removed via surgery in Korea.
Adult ; Carcinoma, Embryonal ; Echocardiography ; Heart ; Heart Neoplasms ; Heart Ventricles ; Humans ; Korea ; Neoplasm Metastasis ; Testicular Neoplasms

Objective: This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Methods: Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics—compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)—from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. Results: The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. Conclusion The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.

Objective: This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Methods: Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics—compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)—from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. Results: The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. Conclusion The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.

Objective: This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Methods: Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics—compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)—from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. Results: The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. Conclusion The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.

Objective: This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Methods: Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics—compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)—from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. Results: The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. Conclusion The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.

Objective: This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Methods: Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics—compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)—from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. Results: The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. Conclusion The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.