No abstract available.
Respiratory Distress Syndrome, Adult*

No abstract available.
Antibodies* ; Rheumatic Diseases*

OBJECTIVE: Excessive external rotation of the hemiplegic foot is a common problem of hemiplegic gait. There has been a few report on etiological investigation and corrective measurement of an excessive external rotation of hemiplegic foot. Thus we present a newly designed Torque heel to correct the external rotation of hemiplegic foot. METHOD: Ten hemiparetic patients with an excessive external rotation of affected foot participated in this study. All of the participants were able to walk at least 10 meters with metal a ankle foot orthosis (AFO) using a single cane. Each of these patients was placed on four tries of walk: (1) on a bare foot; (2) with an AFO; (3) with an AFO and a quarter inch of lateral wedge; and (4) with an AFO and the Torque heel . Gait patterns were analysed by the Vicon 370, three dimensional motion analyser. RESULTS: The speed and stride length increased in all tries except for the bare foot walk. Those who walked with the assistive devices showed no difference in the speed and stride length. All the participants showed an increased in external rotation of pelvis and ankle. Those who walked with an AFO and Torque heel presented a decrease in the external rotation of foot and pelvis. The hip and ankle motions of the hemiplegic limbs were not affected with the AFO and Torque heel . A significant degree of correction in pelvic rotation with an AFO and Torque heel was noted. CONCLUSIONS: This study indicates that an AFO with Torque heel is beneficial to the correction of external rotation of a hemiplegic foot. And the excessive external rotation of hemiplegic foot may be due possibly to the external rotation of pelvis.
Ankle ; Canes ; Extremities ; Foot Orthoses ; Foot* ; Gait Disorders, Neurologic ; Gait* ; Heel* ; Hip ; Humans ; Pelvis ; Self-Help Devices ; Torque*

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.

New surgical techniques utilizing computer-aided engineering have been recently developed to improve the quality of surgery and reduce the risk to patients. This paper reports the surgical cases using rapid prototyping assisted orthopeadic fracture surgery (RPAOFS). RPAOFS utilizes the symmetric characteristics of the human body, and the potential for RE and RP in which the physical shape is manufactured repidly from the CT data. The physical shape before the injury was manufactured from the RP using the mirror transformed CAD data of the uninjured extents. Subsequently, pre-operative planning, such as the selection of the proper implant, preforming of the implant, selection of the fixation positions, and surgery are performed utilizing the physical shape. RPAOFS was applied to two cases such as a distal tibia communited fracture and a proximal tibia plateau fracture. The surgical results showed that RPAOFS is an effective surgical tools.
Human Body ; Humans ; Surgery, Computer-Assisted ; Tibia

No abstract available.
Antibodies, Antiphospholipid* ; Rheumatic Diseases*