Review article

Wrist Fractures

Wrist and elbow radiographs, which plays a key role in diagnosing both fractures and degenerative conditions, present a diagnostic challenge due to intricate structures and subtle pathological signs. Artificial intelligence (AI) through deep learning models, has transformed diagnostic imaging, achieving accuracy rates, with explainable AI (XAI) and Gradient-weighted Class Activation Mapping (Grad-CAM) enhancing transparency of AI-driven diagnosis.

The MURA-dataset, a comprehensive collection of musculoskeletal radiographs, specifically focuses on wrist and elbow images, ensuring a spectrum of normal and abnormal conditions. An ensemble of transfer-learning models, including VGG16, VGG19, ResNet, DenseNet, InceptionV3 and Xception, was applied, with implemented Grad-CAM techniques, providing interpretable heat maps. The Dice Similarity Coefficient (DSC) evaluated the algorithm's efficiency in recognizing regions of interest.

The average test accuracy of the 20 models were 0.81 (0.72–0.84), and 0.60 (0.49–0.73) for the wrist and elbow radiographs, respectively. The highest performing models were VGG16 with a test accuracy of 0.84, and DenseNet169 with a test accuracy of 0.73. The DSC were calculated for the six highest performing models, and agreements between algorithms were found on radiographs with metal, and only minimal agreement for radiographs with fractures.

The study employed twenty transfer-learning models on wrist and elbow radiographs presenting accuracy and partial agreement with Grad-CAM technique evaluation. This study enables comprehension of model performance and avenues for potential enhancement.

The utilization of artificial intelligence, specifically transfer-learning models, could greatly enhance the accuracy and efficiency of diagnosing conditions from wrist and elbow radiographs. Additionally, the application of explainable AI techniques such as Grad-CAM can provide visual validation and transparency, thereby strengthening trust and adoption in clinical settings.

The aim of this study was to assess the treatment and complications of a distinct type of partial intra-articular distal radius fracture.

Seven patients treated by the senior author between 2008 and 2013 for a partial intra-articular distal radius fracture with isolated involvement of the volar lunate facet were included. All fragments had the distinctive shape of a triangular-base pyramid (tetrahedron) extending from the metaphysis distally. All fractures were preoperatively assessed with computed tomography (CT) scans. Patients underwent surgical treatment using a standard flexor carpi radialis approach (2 patients) or a volar ulnar approach (5 patients) and were followed postoperatively for a minimum of 12 months.

Patient age ranged from 33 to 66 years. On average, fragments measured 34 ± 6 mm in length (range, 27–43 mm) and were 48% as wide as the distal radius (range, 40% to 56%) and 58% as deep as the anterior-posterior dimension of the lesser sigmoid notch (range, 33% to 83%). Loss of reduction requiring revision surgery occurred at 4 weeks in 1 patient who underwent internal fixation through the flexor carpi radialis approach. The remaining cases healed uneventfully. At the final follow-up, all, except the patient requiring revision surgery, had a painless wrist. Average total wrist motion measured 87% of the opposite side. Radiographic healing with anatomic wrist alignment was observed in all except the patient requiring revision. This patient had persistent joint subluxation. The remaining patients all achieved good or excellent functional outcomes.

Isolated tetrahedron volar lunate facet fractures of the distal radius are rare. In our experience, the use of a volar ulnar approach leads to satisfactory fixation and outcomes, yielding excellent radiographic and clinical outcomes.

Therapeutic V.

This article presents a novel method for bone segmentation from three-dimensional (3-D) ultrasound images that derives intensity-invariant 3-D local image phase measures that are then employed for extracting ridge-like features similar to those that occur at soft tissue/bone interfaces. The main contributions in this article include: (1) the extension of our previously proposed phase-symmetry-based bone surface extraction from two-dimensional (2-D) to 3-D images using 3-D Log-Gabor filters; (2) the design of a new framework for accuracy evaluation based on using computed tomography as a gold standard that allows the assessment of surface localization accuracy across the entire 3-D surface; (3) the quantitative validation of accuracy of our 3-D phase-processing approach on both intact and fractured bone surfaces using phantoms and ex vivo 3-D ultrasound scans; and (4) the qualitative validation obtained by scanning emergency room patients with distal radius and pelvis fractures. We show a 41% improvement in surface localization error over the previous 2-D phase symmetry method. The results demonstrate clearly visible segmentations of bone surfaces with a localization accuracy of <0.6 mm and mean errors in estimating fracture displacements below 0.6 mm. The results show that the proposed method is successful even for situations when the bone surface response is weak due to shadowing from muscle and fascia interfaces above the bone, which is a situation where the 2-D method fails.

Intensity-invariant local phase features based on Log-Gabor filters have been recently shown to produce highly accurate localizations of bone surfaces from three-dimensional (3-D) ultrasound. A key challenge, however, remains in the proper selection of filter parameters, whose values have so far been chosen empirically and kept fixed for a given image. Since Log-Gabor filter responses widely change when varying the filter parameters, actual parameter selection can significantly affect the quality of extracted features. This article presents a novel method for contextual parameter selection that autonomously adapts to image content. Our technique automatically selects the scale, bandwidth and orientation parameters of Log-Gabor filters for optimizing local phase symmetry. The proposed approach incorporates principle curvature computed from the Hessian matrix and directional filter banks in a phase scale-space framework. Evaluations performed on carefully designed in vitro experiments demonstrate 35% improvement in accuracy of bone surface localization compared with empirically-set parameterization results. Results from a pilot in vivo study on human subjects, scanned in the operating room, show similar improvements.