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.

Background: The choice of an appropriate type of orthosis depends on the patient’s specific condition and needs. Different types of orthoses can affect plantar pressure distribution during certain gait patterns. Toe and heel gaits are common patterns of gait assigned for optimal recovery in patients with foot or ankle injuries. This study aimed to evaluate differences in plantar pressure between postoperative shoes and walker boots during toe and heel gaits in healthy individuals. Methods: A total of 30 healthy individuals with a mean age of 21.7 ± 1.2 years were included in this study. Two types of gaits, toe and heel, were performed while wearing each orthosis on the right side of the foot. A standardized running shoe was worn on the left side of the foot. Plantar pressure variables including contact area, peak pressure, and maximum force were collected using the Pedar-X in-shoe pressure measuring system. Results: During toe gait, while both orthoses demonstrated similar offloading in the hindfoot areas, walker boots were superior in reducing the peak pressure (first toe, p = 0.003; second to fifth toes, p < 0.001) and contact area (first toe, p = 0.003; second to fifth toes, p = 0.003) in the forefoot areas. During heel gait, both orthoses demonstrated similar offloading in the toe areas; however, the walker boots were superior in reducing the peak pressure in the lateral hindfoot (p < 0.001). Conclusions The results of our study can serve as a guideline for orthopedic physicians in prescribing an appropriate type of orthosis during specific types of gait for patients following foot and ankle injury and postoperative recovery.

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.

Background: The choice of an appropriate type of orthosis depends on the patient’s specific condition and needs. Different types of orthoses can affect plantar pressure distribution during certain gait patterns. Toe and heel gaits are common patterns of gait assigned for optimal recovery in patients with foot or ankle injuries. This study aimed to evaluate differences in plantar pressure between postoperative shoes and walker boots during toe and heel gaits in healthy individuals. Methods: A total of 30 healthy individuals with a mean age of 21.7 ± 1.2 years were included in this study. Two types of gaits, toe and heel, were performed while wearing each orthosis on the right side of the foot. A standardized running shoe was worn on the left side of the foot. Plantar pressure variables including contact area, peak pressure, and maximum force were collected using the Pedar-X in-shoe pressure measuring system. Results: During toe gait, while both orthoses demonstrated similar offloading in the hindfoot areas, walker boots were superior in reducing the peak pressure (first toe, p = 0.003; second to fifth toes, p < 0.001) and contact area (first toe, p = 0.003; second to fifth toes, p = 0.003) in the forefoot areas. During heel gait, both orthoses demonstrated similar offloading in the toe areas; however, the walker boots were superior in reducing the peak pressure in the lateral hindfoot (p < 0.001). Conclusions The results of our study can serve as a guideline for orthopedic physicians in prescribing an appropriate type of orthosis during specific types of gait for patients following foot and ankle injury and postoperative recovery.

Background: The choice of an appropriate type of orthosis depends on the patient’s specific condition and needs. Different types of orthoses can affect plantar pressure distribution during certain gait patterns. Toe and heel gaits are common patterns of gait assigned for optimal recovery in patients with foot or ankle injuries. This study aimed to evaluate differences in plantar pressure between postoperative shoes and walker boots during toe and heel gaits in healthy individuals. Methods: A total of 30 healthy individuals with a mean age of 21.7 ± 1.2 years were included in this study. Two types of gaits, toe and heel, were performed while wearing each orthosis on the right side of the foot. A standardized running shoe was worn on the left side of the foot. Plantar pressure variables including contact area, peak pressure, and maximum force were collected using the Pedar-X in-shoe pressure measuring system. Results: During toe gait, while both orthoses demonstrated similar offloading in the hindfoot areas, walker boots were superior in reducing the peak pressure (first toe, p = 0.003; second to fifth toes, p < 0.001) and contact area (first toe, p = 0.003; second to fifth toes, p = 0.003) in the forefoot areas. During heel gait, both orthoses demonstrated similar offloading in the toe areas; however, the walker boots were superior in reducing the peak pressure in the lateral hindfoot (p < 0.001). Conclusions The results of our study can serve as a guideline for orthopedic physicians in prescribing an appropriate type of orthosis during specific types of gait for patients following foot and ankle injury and postoperative recovery.

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.

Background: The choice of an appropriate type of orthosis depends on the patient’s specific condition and needs. Different types of orthoses can affect plantar pressure distribution during certain gait patterns. Toe and heel gaits are common patterns of gait assigned for optimal recovery in patients with foot or ankle injuries. This study aimed to evaluate differences in plantar pressure between postoperative shoes and walker boots during toe and heel gaits in healthy individuals. Methods: A total of 30 healthy individuals with a mean age of 21.7 ± 1.2 years were included in this study. Two types of gaits, toe and heel, were performed while wearing each orthosis on the right side of the foot. A standardized running shoe was worn on the left side of the foot. Plantar pressure variables including contact area, peak pressure, and maximum force were collected using the Pedar-X in-shoe pressure measuring system. Results: During toe gait, while both orthoses demonstrated similar offloading in the hindfoot areas, walker boots were superior in reducing the peak pressure (first toe, p = 0.003; second to fifth toes, p < 0.001) and contact area (first toe, p = 0.003; second to fifth toes, p = 0.003) in the forefoot areas. During heel gait, both orthoses demonstrated similar offloading in the toe areas; however, the walker boots were superior in reducing the peak pressure in the lateral hindfoot (p < 0.001). Conclusions The results of our study can serve as a guideline for orthopedic physicians in prescribing an appropriate type of orthosis during specific types of gait for patients following foot and ankle injury and postoperative recovery.

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.

Background: s/Aims: Reported incidence of extrahepatic bile duct cancer is higher in Asians than in Western populations. Korea, in particular, is one of the countries with the highest incidence rates of extrahepatic bile duct cancer in the world. Although research and innovative therapeutic modalities for extrahepatic bile duct cancer are emerging, clinical guidelines are currently unavailable in Korea. The Korean Society of Hepato-Biliary-Pancreatic Surgery in collaboration with related societies (Korean Pancreatic and Biliary Surgery Society, Korean Society of Abdominal Radiology, Korean Society of Medical Oncology, Korean Society of Radiation Oncology, Korean Society of Pathologists, and Korean Society of Nuclear Medicine) decided to establish clinical guideline for extrahepatic bile duct cancer in June 2021. Methods: Contents of the guidelines were developed through subgroup meetings for each key question and a preliminary draft was finalized through a Clinical Guidelines Committee workshop. Results: In November 2021, the finalized draft was presented for public scrutiny during a formal hearing. Conclusions The extrahepatic guideline committee believed that this guideline could be helpful in the treatment of patients.