Automated computation of radiographic parameters of distal radial metaphyseal fractures in forearm X-rays

Suna, Avigail; Davidson, Amit; Weil, Yoram A.; Joskowicz, Leo

doi:10.1007/s11548-023-02907-0

Cited by 1 publication

(3 citation statements)

References 28 publications

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“…Our algorithm, however, excels in measuring even negative radial inclination or radial height, particularly when the radial styloid is positioned proximal to the articular surface of the ulnar head. Notably, Suna et al 16) also proposed automated computation of radiologic parameters using deep learning but avoided addressing severely collapsed fractures with negative radial inclination or radial heights.…”

Section: Discussionmentioning

confidence: 99%

“…Indeed, our evaluation metrics yielded high performance values with 634 AP and 634 lateral radiographs. In comparison, similar studies such as Suna et al’s 16) used 90 AP and 93 lateral X-rays for radius and ulna segmentation, but relied on 1,833 radiographs from the Stanford ML group’s MURA dataset for forearm segmentation. Korez et al 9) trained their model with 242 images to measure the sagittal spinopelvic balance using 97 X-rays.…”

Section: Discussionmentioning

confidence: 99%

“…In our research, we focused on the automated measurement of radiologic parameters in distal radius fractures using deep learning techniques. Suna et al 16) also focused on the automated computation of radiologic parameters using a deep learning technique. However, their study primarily concentrated on segmentation techniques and investigated distal radius fractures with less severe deformations.…”

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confidence: 99%

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Automatic Segmentation and Radiologic Measurement of Distal Radius Fractures Using Deep Learning

Lee,

Kim,

Kim

et al. 2024

Clin Orthop Surg

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Background Recently, deep learning techniques have been used in medical imaging studies. We present an algorithm that measures radiologic parameters of distal radius fractures using a deep learning technique and compares the predicted parameters with those measured by an orthopedic hand surgeon. Methods We collected anteroposterior (AP) and lateral X-ray images of 634 wrists in 624 patients with distal radius fractures treated conservatively with a follow-up of at least 2 months. We allocated 507 AP and 507 lateral images to the training set (80% of the images were used to train the model, and 20% were utilized for validation) and 127 AP and 127 lateral images to the test set. The margins of the radius and ulna were annotated for ground truth, and the scaphoid in the lateral views was annotated in the box configuration to determine the volar side of the images. Radius segmentation was performed using attention U-Net, and the volar/dorsal side was identified using a detection and classification model based on RetinaNet. The proposed algorithm measures the radial inclination, dorsal or volar tilt, and radial height by index axes and points from the segmented radius and ulna. Results The segmentation model for the radius exhibited an accuracy of 99.98% and a Dice similarity coefficient (DSC) of 98.07% for AP images, and an accuracy of 99.75% and a DSC of 94.84% for lateral images. The segmentation model for the ulna showed an accuracy of 99.84% and a DSC of 96.48%. Based on the comparison of the radial inclinations measured by the algorithm and the manual method, the Pearson correlation coefficient was 0.952, and the intraclass correlation coefficient was 0.975. For dorsal/volar tilt, the correlation coefficient was 0.940, and the intraclass correlation coefficient was 0.968. For radial height, it was 0.768 and 0.868, respectively. Conclusions The deep learning-based algorithm demonstrated excellent segmentation of the distal radius and ulna in AP and lateral radiographs of the wrist with distal radius fractures and afforded automatic measurements of radiologic parameters.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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confidence: 99%

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