Objective: This study aimed to quantitatively assess abnormalities in the talar dome cartilage using MRI T2 mapping, with additional analyses based on the Lauge-Hansen (LH) classification and anatomical locations. Materials and Methods: This retrospective study analyzed 78 patients who underwent ankle MRI with T2 mapping for acute ankle trauma between January 2021 and October 2022. Patients were classified into the supination (S) and pronation (P) groups based on the LH classification, and then divided into subgroups based on posterior malleolus (PM) involvement. The T2 values for the talar cartilage were quantitatively measured in six anatomical regions defined by the combination of medial vs.lateral and anterior vs. central vs. posterior. The T2 mapping values in each region of the talus were compared between the S and P groups and between the PM and non-PM injury groups using t-tests. The T2 values were also compared between the medial and lateral sides within each group. Results: Among the 78 patients (mean age, 38.62 ± 14.82 years; 47 male), 53 and 25 were in the S and P groups, respectively, and 53 patients showed PM involvement. In comparison with the P group, the S group exhibited higher T2 values in the medial portion (61.27 ± 8.30 vs. 54.03 ± 6.96; P < 0.001) and lower T2 values in the lateral talus (54.95 ± 8.47 vs. 64.15 ± 7.31; P < 0.001). The PM injury group showed higher T2 values in the posterior region than the non-PM injury group (P ≤ 0.011). Within the PM injury group, T2 values were higher in the anteromedial and posterolateral regions than on the opposite sides (P= 0.037 and 0.011, respectively). Conclusion MRI T2 values demonstrated significant regional variations in the talar dome cartilage in acute ankle trauma, and the T2 values may reflect different ankle trauma mechanisms and PM involvement. Thus, T2 mapping can facilitate evaluation of talar cartilage alterations.

Objective: This study aimed to quantitatively assess abnormalities in the talar dome cartilage using MRI T2 mapping, with additional analyses based on the Lauge-Hansen (LH) classification and anatomical locations. Materials and Methods: This retrospective study analyzed 78 patients who underwent ankle MRI with T2 mapping for acute ankle trauma between January 2021 and October 2022. Patients were classified into the supination (S) and pronation (P) groups based on the LH classification, and then divided into subgroups based on posterior malleolus (PM) involvement. The T2 values for the talar cartilage were quantitatively measured in six anatomical regions defined by the combination of medial vs.lateral and anterior vs. central vs. posterior. The T2 mapping values in each region of the talus were compared between the S and P groups and between the PM and non-PM injury groups using t-tests. The T2 values were also compared between the medial and lateral sides within each group. Results: Among the 78 patients (mean age, 38.62 ± 14.82 years; 47 male), 53 and 25 were in the S and P groups, respectively, and 53 patients showed PM involvement. In comparison with the P group, the S group exhibited higher T2 values in the medial portion (61.27 ± 8.30 vs. 54.03 ± 6.96; P < 0.001) and lower T2 values in the lateral talus (54.95 ± 8.47 vs. 64.15 ± 7.31; P < 0.001). The PM injury group showed higher T2 values in the posterior region than the non-PM injury group (P ≤ 0.011). Within the PM injury group, T2 values were higher in the anteromedial and posterolateral regions than on the opposite sides (P= 0.037 and 0.011, respectively). Conclusion MRI T2 values demonstrated significant regional variations in the talar dome cartilage in acute ankle trauma, and the T2 values may reflect different ankle trauma mechanisms and PM involvement. Thus, T2 mapping can facilitate evaluation of talar cartilage alterations.

Objective: This study aimed to quantitatively assess abnormalities in the talar dome cartilage using MRI T2 mapping, with additional analyses based on the Lauge-Hansen (LH) classification and anatomical locations. Materials and Methods: This retrospective study analyzed 78 patients who underwent ankle MRI with T2 mapping for acute ankle trauma between January 2021 and October 2022. Patients were classified into the supination (S) and pronation (P) groups based on the LH classification, and then divided into subgroups based on posterior malleolus (PM) involvement. The T2 values for the talar cartilage were quantitatively measured in six anatomical regions defined by the combination of medial vs.lateral and anterior vs. central vs. posterior. The T2 mapping values in each region of the talus were compared between the S and P groups and between the PM and non-PM injury groups using t-tests. The T2 values were also compared between the medial and lateral sides within each group. Results: Among the 78 patients (mean age, 38.62 ± 14.82 years; 47 male), 53 and 25 were in the S and P groups, respectively, and 53 patients showed PM involvement. In comparison with the P group, the S group exhibited higher T2 values in the medial portion (61.27 ± 8.30 vs. 54.03 ± 6.96; P < 0.001) and lower T2 values in the lateral talus (54.95 ± 8.47 vs. 64.15 ± 7.31; P < 0.001). The PM injury group showed higher T2 values in the posterior region than the non-PM injury group (P ≤ 0.011). Within the PM injury group, T2 values were higher in the anteromedial and posterolateral regions than on the opposite sides (P= 0.037 and 0.011, respectively). Conclusion MRI T2 values demonstrated significant regional variations in the talar dome cartilage in acute ankle trauma, and the T2 values may reflect different ankle trauma mechanisms and PM involvement. Thus, T2 mapping can facilitate evaluation of talar cartilage alterations.

Objective: This study aimed to quantitatively assess abnormalities in the talar dome cartilage using MRI T2 mapping, with additional analyses based on the Lauge-Hansen (LH) classification and anatomical locations. Materials and Methods: This retrospective study analyzed 78 patients who underwent ankle MRI with T2 mapping for acute ankle trauma between January 2021 and October 2022. Patients were classified into the supination (S) and pronation (P) groups based on the LH classification, and then divided into subgroups based on posterior malleolus (PM) involvement. The T2 values for the talar cartilage were quantitatively measured in six anatomical regions defined by the combination of medial vs.lateral and anterior vs. central vs. posterior. The T2 mapping values in each region of the talus were compared between the S and P groups and between the PM and non-PM injury groups using t-tests. The T2 values were also compared between the medial and lateral sides within each group. Results: Among the 78 patients (mean age, 38.62 ± 14.82 years; 47 male), 53 and 25 were in the S and P groups, respectively, and 53 patients showed PM involvement. In comparison with the P group, the S group exhibited higher T2 values in the medial portion (61.27 ± 8.30 vs. 54.03 ± 6.96; P < 0.001) and lower T2 values in the lateral talus (54.95 ± 8.47 vs. 64.15 ± 7.31; P < 0.001). The PM injury group showed higher T2 values in the posterior region than the non-PM injury group (P ≤ 0.011). Within the PM injury group, T2 values were higher in the anteromedial and posterolateral regions than on the opposite sides (P= 0.037 and 0.011, respectively). Conclusion MRI T2 values demonstrated significant regional variations in the talar dome cartilage in acute ankle trauma, and the T2 values may reflect different ankle trauma mechanisms and PM involvement. Thus, T2 mapping can facilitate evaluation of talar cartilage alterations.

Objective: This study aimed to quantitatively assess abnormalities in the talar dome cartilage using MRI T2 mapping, with additional analyses based on the Lauge-Hansen (LH) classification and anatomical locations. Materials and Methods: This retrospective study analyzed 78 patients who underwent ankle MRI with T2 mapping for acute ankle trauma between January 2021 and October 2022. Patients were classified into the supination (S) and pronation (P) groups based on the LH classification, and then divided into subgroups based on posterior malleolus (PM) involvement. The T2 values for the talar cartilage were quantitatively measured in six anatomical regions defined by the combination of medial vs.lateral and anterior vs. central vs. posterior. The T2 mapping values in each region of the talus were compared between the S and P groups and between the PM and non-PM injury groups using t-tests. The T2 values were also compared between the medial and lateral sides within each group. Results: Among the 78 patients (mean age, 38.62 ± 14.82 years; 47 male), 53 and 25 were in the S and P groups, respectively, and 53 patients showed PM involvement. In comparison with the P group, the S group exhibited higher T2 values in the medial portion (61.27 ± 8.30 vs. 54.03 ± 6.96; P < 0.001) and lower T2 values in the lateral talus (54.95 ± 8.47 vs. 64.15 ± 7.31; P < 0.001). The PM injury group showed higher T2 values in the posterior region than the non-PM injury group (P ≤ 0.011). Within the PM injury group, T2 values were higher in the anteromedial and posterolateral regions than on the opposite sides (P= 0.037 and 0.011, respectively). Conclusion MRI T2 values demonstrated significant regional variations in the talar dome cartilage in acute ankle trauma, and the T2 values may reflect different ankle trauma mechanisms and PM involvement. Thus, T2 mapping can facilitate evaluation of talar cartilage alterations.

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.