Purpose: This study explores the potential of artificial intelligence (AI) in optimizing radiotherapy protocols for personalized cancer treatment. Specifically, it investigates the role of AI-based segmentation tools in improving accuracy and efficiency across various anatomical regions. Methods: A dataset of 500 anonymized patient computed tomography scans from Chungbuk National University Hospital was used to develop and validate AI models for segmenting organs-atrisk. The models were tailored for five anatomical regions: head and neck, chest, abdomen, breast, and pelvis. Performance was evaluated using Dice Similarity Coefficient (DSC), Mean Surface Distance, and the 95th Percentile Hausdorff Distance (HD95). Results: The AI models achieved high segmentation accuracy for large, well-defined structures such as the brain, lungs, and liver, with DSC values exceeding 0.95 in many cases. However, challenges were observed for smaller or complex structures, including the optic chiasm and rectum, with instances of segmentation failure and infinity values for HD95. These findings highlight the variability in performance depending on anatomical complexity and structure size. Conclusions AI-based segmentation tools demonstrate significant potential to streamline radiotherapy workflows, reduce inter-observer variability, and enhance treatment accuracy. Despite challenges with smaller structures, the integration of AI enables dynamic, patient-specific adaptations to anatomical changes, contributing to more precise and effective cancer treatments.Future work should focus on refining models for anatomically complex structures and validating these methods in diverse clinical settings.

Purpose: This study explores the potential of artificial intelligence (AI) in optimizing radiotherapy protocols for personalized cancer treatment. Specifically, it investigates the role of AI-based segmentation tools in improving accuracy and efficiency across various anatomical regions. Methods: A dataset of 500 anonymized patient computed tomography scans from Chungbuk National University Hospital was used to develop and validate AI models for segmenting organs-atrisk. The models were tailored for five anatomical regions: head and neck, chest, abdomen, breast, and pelvis. Performance was evaluated using Dice Similarity Coefficient (DSC), Mean Surface Distance, and the 95th Percentile Hausdorff Distance (HD95). Results: The AI models achieved high segmentation accuracy for large, well-defined structures such as the brain, lungs, and liver, with DSC values exceeding 0.95 in many cases. However, challenges were observed for smaller or complex structures, including the optic chiasm and rectum, with instances of segmentation failure and infinity values for HD95. These findings highlight the variability in performance depending on anatomical complexity and structure size. Conclusions AI-based segmentation tools demonstrate significant potential to streamline radiotherapy workflows, reduce inter-observer variability, and enhance treatment accuracy. Despite challenges with smaller structures, the integration of AI enables dynamic, patient-specific adaptations to anatomical changes, contributing to more precise and effective cancer treatments.Future work should focus on refining models for anatomically complex structures and validating these methods in diverse clinical settings.

Purpose: This study explores the potential of artificial intelligence (AI) in optimizing radiotherapy protocols for personalized cancer treatment. Specifically, it investigates the role of AI-based segmentation tools in improving accuracy and efficiency across various anatomical regions. Methods: A dataset of 500 anonymized patient computed tomography scans from Chungbuk National University Hospital was used to develop and validate AI models for segmenting organs-atrisk. The models were tailored for five anatomical regions: head and neck, chest, abdomen, breast, and pelvis. Performance was evaluated using Dice Similarity Coefficient (DSC), Mean Surface Distance, and the 95th Percentile Hausdorff Distance (HD95). Results: The AI models achieved high segmentation accuracy for large, well-defined structures such as the brain, lungs, and liver, with DSC values exceeding 0.95 in many cases. However, challenges were observed for smaller or complex structures, including the optic chiasm and rectum, with instances of segmentation failure and infinity values for HD95. These findings highlight the variability in performance depending on anatomical complexity and structure size. Conclusions AI-based segmentation tools demonstrate significant potential to streamline radiotherapy workflows, reduce inter-observer variability, and enhance treatment accuracy. Despite challenges with smaller structures, the integration of AI enables dynamic, patient-specific adaptations to anatomical changes, contributing to more precise and effective cancer treatments.Future work should focus on refining models for anatomically complex structures and validating these methods in diverse clinical settings.

Purpose: This study explores the potential of artificial intelligence (AI) in optimizing radiotherapy protocols for personalized cancer treatment. Specifically, it investigates the role of AI-based segmentation tools in improving accuracy and efficiency across various anatomical regions. Methods: A dataset of 500 anonymized patient computed tomography scans from Chungbuk National University Hospital was used to develop and validate AI models for segmenting organs-atrisk. The models were tailored for five anatomical regions: head and neck, chest, abdomen, breast, and pelvis. Performance was evaluated using Dice Similarity Coefficient (DSC), Mean Surface Distance, and the 95th Percentile Hausdorff Distance (HD95). Results: The AI models achieved high segmentation accuracy for large, well-defined structures such as the brain, lungs, and liver, with DSC values exceeding 0.95 in many cases. However, challenges were observed for smaller or complex structures, including the optic chiasm and rectum, with instances of segmentation failure and infinity values for HD95. These findings highlight the variability in performance depending on anatomical complexity and structure size. Conclusions AI-based segmentation tools demonstrate significant potential to streamline radiotherapy workflows, reduce inter-observer variability, and enhance treatment accuracy. Despite challenges with smaller structures, the integration of AI enables dynamic, patient-specific adaptations to anatomical changes, contributing to more precise and effective cancer treatments.Future work should focus on refining models for anatomically complex structures and validating these methods in diverse clinical settings.

Purpose: This study explores the potential of artificial intelligence (AI) in optimizing radiotherapy protocols for personalized cancer treatment. Specifically, it investigates the role of AI-based segmentation tools in improving accuracy and efficiency across various anatomical regions. Methods: A dataset of 500 anonymized patient computed tomography scans from Chungbuk National University Hospital was used to develop and validate AI models for segmenting organs-atrisk. The models were tailored for five anatomical regions: head and neck, chest, abdomen, breast, and pelvis. Performance was evaluated using Dice Similarity Coefficient (DSC), Mean Surface Distance, and the 95th Percentile Hausdorff Distance (HD95). Results: The AI models achieved high segmentation accuracy for large, well-defined structures such as the brain, lungs, and liver, with DSC values exceeding 0.95 in many cases. However, challenges were observed for smaller or complex structures, including the optic chiasm and rectum, with instances of segmentation failure and infinity values for HD95. These findings highlight the variability in performance depending on anatomical complexity and structure size. Conclusions AI-based segmentation tools demonstrate significant potential to streamline radiotherapy workflows, reduce inter-observer variability, and enhance treatment accuracy. Despite challenges with smaller structures, the integration of AI enables dynamic, patient-specific adaptations to anatomical changes, contributing to more precise and effective cancer treatments.Future work should focus on refining models for anatomically complex structures and validating these methods in diverse clinical settings.

After tooth extraction, alveolar bone is resorbed over time. Loss of alveolar bone and reduction of upper soft tissue poses difficulties in future implant placement and long-term survival of the implant. This case report focuses on increasing the soft and hard tissues at the implant placement site by using alveolar ridge augmentation and a xenogeneic collagen matrix as a soft tissue substitute in an extraction socket affected by periodontal disease. In each case, the width of the alveolar bone increased to 6 mm, 8 mm, and 4 mm, and regeneration of the interdental papilla around the implant was shown, as well as buccal keratinized gingiva of 4 mm, 6 mm, and 4 mm, respectively. Enlarged alveolar bone facilitates implant surgery, and interdental papillae and keratinized gingiva enable aes-thetic prosthesis. This study performed alveolar ridge augmentation on patients with extraction sockets affected by periodontal dis-ease and additionally used soft tissue substitutes to provide a better environment for implant placement and have positive effects for aesthetic and predictive implant surgery.

Oxidative stress brings about apoptosis through various mechanisms. In particular, oxidative stress in neuronal cells can causes a variety of brain diseases. This study was conducted to investigate the effect of Bidens tripartita on oxidative stress in neuronal cells. B. tripartita has traditionally been used in Russia as a medicine for diseases such as rhinitis, angina and colitis. Over-production of glutamate induces oxidative stress. When the oxidative stress occurs in the cells, reactive oxygen species (ROS) and Ca 2+ increase. In addition, the abrupt decline of mitochondrial membrane potential and the decrease of glutathione related enzymes such as glutathione reductase (GR) and glutathione peroxidase (GPx) are also observed. The samples used in the experiment showed cytoprotective effect in the MTT assay. It also lowered the ROS and Ca 2+ level, and increased degree of mitochondrial membrane potential, GR and GPx. As a result, B. tripartita had a positive effect against oxidative stress. Thus, it is expected to have potential for treatment and prevention of degenerative brain diseases such as Alzheimer’s disease.

Background and Objectives: Recently, the improvement of music perception abilities for emotional stability and high quality of life has become important for the hearing loss group. This study aimed to examine and compare the music perception abilities of the normal hearing (NH) and hearing amplification system (HAS) groups to find the needs and methods of music rehabilitation. Subjects and Methods: The data were collected from 15 NH adults (33.1±11.4 years) and 15 HAS adults (38.7±13.4 years), of whom eight wore cochlear implant [CI] systems and seven wore CI and hearing aid systems depending on pitch, melody, rhythm, timbre, emotional reaction, and harmony perception tests. A mismatch negativity test was also conducted, and attitudes toward and satisfaction with listening to music were measured. Results: The correction percentages for the NH and HAS groups were 94.0%±6.1% and 75.3%±23.2% in the pitch test; 94.0%±7.1% and 30.3%±25.9% in the melody test; 99.3%±1.8% and 94.0%± 7.6% in the rhythm test; 78.9%±41.8% and 64.4%±48.9% in the timbre test; 96.7%±10.4% and 81.7%±16.3% in the emotional reaction test; and 85.7%±14.1% and 58.4%±13.9% in the harmony test, respectively, showing statistical significance (p<0.05). For the mismatch negativity test, the area of the waveform was smaller in the HAS groups than in the NH groups, with 70 dB of stimulation showing no statistical significance. The response rates for satisfaction with listening to music were 80% and 93.3% for the NH and HAS groups, showing no statistical significance. Conclusions Although the HAS group showed lower music perception ability than the NH group overall, they showed a strong desire for music listening. Also, the HAS group revealed a higher degree of satisfaction even when listening to unfamiliar music played with unusual instruments. It is suggested that systematic and constant musical rehabilitation based on musical elements and different listening experiences will improve music perception qualities and abilities for HAS users.

Neurocritical patients who can self-report pain use the 0-10 numerical rating scale (NRS, verbal or visual form). However, critically ill patients whose nervous systems cannot express pain use the behavioral pain scale (BPS) and the critical care pain observation tool (CPOT) behavioral pain assessment tools. These tools reveal pain-related changes in movement, facial expression, posture, and physiological indicators such as heart rate, blood pressure, and respiratory rate. In pain control, it is first essential to reduce unnecessary painkillers through non-drug therapy and maximize the effect of the administered analgesics. For nonneuropathic pain, narcotic analgesics such as fentanyl, hydromorphone, morphine, and remifentanil are administered intravenously. Gabapentin, pregabalin, and carbamazepine are recommended along with narcotic analgesics for neuropathic pain control. In addition, nonnarcotic analgesics for multi-modal analgesia are used to reduce the use of narcotic analgesics or the side effects of narcotic analgesics. In the intensive care unit (ICU), the sedation-agitation scale (SAS) and the Richmond agitation-sedation scale (RASS) are used to determine the depth of sedation to be maintained during shallow or deep sedation, considering the condition of the critically ill patient. When selecting sedatives for critically ill patients, preferentially consider nonbenzodiazepines such as propofol or dexmedetomidine rather than benzodiazepines such as midazolam or lorazepam. In addition, patients use painkillers or sedatives for over a week, and neurological changes or physiological dependence may occur. Therefore, clinicians should evaluate the critically ill patient’s condition, and sedatives and painkillers should be reduced or discontinued.