Background and Objectives: Patients with voice disorders often face difficulties in managing their vocal health in daily life, highlighting the need for voice management and hygiene education without temporal or spatial limitations. This study aimed to evaluate the usability of a smartphone-based voice hygiene application developed for patients with voice disorders.Materials and Method Thirty patients diagnosed with voice disorders who underwent voice therapy participated in the study. After using the voice hygiene application for 4 weeks, participants completed a usability questionnaire composed of ten items across five key categories: aesthetics, functionality, engagement, usability, and reliability (two items per category). Results: The overall usability score of the voice hygiene application was 3.94 out of 5. The highest ratings were observed in aesthetic design and convenience. Engagement was positively influenced by the app’s reminder feature, and satisfaction regarding information reliability and system functionality was also generally high. Conclusion The voice hygiene application was found to be a useful tool for supporting self-management and maintaining vocal hygiene among patients with voice disorders through unrestricted accessibility. This study suggests that such applications can promote consistent voice hygiene practices and contribute to the improvement of vocal health.

Sarcoidosis is an inflammatory condition affecting multiple systems in the body, distinguished by the presence of noncaseating granulomas. It is believed that specific exposures to external antigens in individuals with genetic predisposition lead to the development of these granulomas. When diagnosing sarcoidosis, tuberculosis (TB) is a potential alternative explanation for the symptoms. Our case describes a rare coexistence of cutaneous sarcoidosis and TB pleurisy in a 75-year-old male. He was diagnosed with cutaneous sarcoidosis on his face. During the investigation for possible involvement of other organs, pleural effusion was discovered, and it was determined to be caused by mycobacterial infection. The patient received a 6-month course of anti-TB drugs to treat the TB pleurisy, while a topical calcineurin inhibitor was applied to the cutaneous sarcoidosis. This case serves as a reminder to dermatologists that the coexistence of TB with sarcoidosis is possible, not just as a differential diagnosis.

Sarcoidosis is an inflammatory condition affecting multiple systems in the body, distinguished by the presence of noncaseating granulomas. It is believed that specific exposures to external antigens in individuals with genetic predisposition lead to the development of these granulomas. When diagnosing sarcoidosis, tuberculosis (TB) is a potential alternative explanation for the symptoms. Our case describes a rare coexistence of cutaneous sarcoidosis and TB pleurisy in a 75-year-old male. He was diagnosed with cutaneous sarcoidosis on his face. During the investigation for possible involvement of other organs, pleural effusion was discovered, and it was determined to be caused by mycobacterial infection. The patient received a 6-month course of anti-TB drugs to treat the TB pleurisy, while a topical calcineurin inhibitor was applied to the cutaneous sarcoidosis. This case serves as a reminder to dermatologists that the coexistence of TB with sarcoidosis is possible, not just as a differential diagnosis.

Sarcoidosis is an inflammatory condition affecting multiple systems in the body, distinguished by the presence of noncaseating granulomas. It is believed that specific exposures to external antigens in individuals with genetic predisposition lead to the development of these granulomas. When diagnosing sarcoidosis, tuberculosis (TB) is a potential alternative explanation for the symptoms. Our case describes a rare coexistence of cutaneous sarcoidosis and TB pleurisy in a 75-year-old male. He was diagnosed with cutaneous sarcoidosis on his face. During the investigation for possible involvement of other organs, pleural effusion was discovered, and it was determined to be caused by mycobacterial infection. The patient received a 6-month course of anti-TB drugs to treat the TB pleurisy, while a topical calcineurin inhibitor was applied to the cutaneous sarcoidosis. This case serves as a reminder to dermatologists that the coexistence of TB with sarcoidosis is possible, not just as a differential diagnosis.

Sarcoidosis is an inflammatory condition affecting multiple systems in the body, distinguished by the presence of noncaseating granulomas. It is believed that specific exposures to external antigens in individuals with genetic predisposition lead to the development of these granulomas. When diagnosing sarcoidosis, tuberculosis (TB) is a potential alternative explanation for the symptoms. Our case describes a rare coexistence of cutaneous sarcoidosis and TB pleurisy in a 75-year-old male. He was diagnosed with cutaneous sarcoidosis on his face. During the investigation for possible involvement of other organs, pleural effusion was discovered, and it was determined to be caused by mycobacterial infection. The patient received a 6-month course of anti-TB drugs to treat the TB pleurisy, while a topical calcineurin inhibitor was applied to the cutaneous sarcoidosis. This case serves as a reminder to dermatologists that the coexistence of TB with sarcoidosis is possible, not just as a differential diagnosis.

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.

Purpose: This study aimed to compare the short-term outcomes and safety profiles of androgen-deprivation therapy (ADT)+abiraterone/prednisone with those of ADT+docetaxel in patients with de novo metastatic hormone-sensitive prostate cancer (mHSPC). Materials and Methods: A web-based database system was established to collect prospective cohort data for patients with mHSPC in Korea. From May 2019 to November 2022, 928 patients with mHSPC from 15 institutions were enrolled. Among these patients, data from 122 patients who received ADT+abiraterone/prednisone or ADT+docetaxel as the primary systemic treatment for mHSPC were collected. The patients were divided into two groups: ADT+abiraterone/prednisone group (n=102) and ADT+docetaxel group (n=20). We compared the demographic characteristics, medical histories, baseline cancer status, initial laboratory tests, metastatic burden, oncological outcomes for mHSPC, progression after mHSPC treatment, adverse effects, follow-up, and survival data between the two groups. Results: No significant differences in the demographic characteristics, medical histories, metastatic burden, and baseline cancer status were observed between the two groups. The ADT+abiraterone/prednisone group had a lower prostate-specific antigen (PSA) progression rate (7.8% vs. 30.0%; p=0.011) and lower systemic treatment discontinuation rate (22.5% vs. 45.0%; p=0.037). No significant differences in adverse effects, oncological outcomes, and total follow-up period were observed between the two groups. Conclusions ADT+abiraterone/prednisone had lower PSA progression and systemic treatment discontinuation rates than ADT+docetaxel. In conclusion, further studies involving larger, double-blinded randomized trials with extended follow-up periods are necessary.

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.