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The schematic diagram of the offset angles. (A) PI of VAR: (i) draw the connecting line of the lowest point of the ischium (white); (ii) draw the connecting line at the junction of the head and neck of the healthy femur head (pink); (iii) draw and measure the NSA, which is 141.58° (blue); (iv) draw the angle between the axis of the healthy femoral neck and the white line, which is 58.45° (yellow); (v) draw the connecting line at the junction of the head and neck of the affected femur head (green); and (vi) draw the angle between the vertical line of the green line and the white line, which is 28.99° (red); thus, PI = 29.46° (58.45°–28.99°). (B) PI of VAL: PI = 16.24° (61.42°–45.18°), and the measurement method is the same as that for the VAR. (C) HDA: (i) draw the axis of femoral head on the pelvis orthophoto (white); (ii) draw the connecting line at the junction of the head and neck of the femur head on the hip axial photo (green); (iii) draw a green vertical line (red); and (iv) the HDA (yellow), which is 45.02° (a backward tilt of the femoral head is defined as positive), is made up by the intersection of the red and the white lines. PI, pelvic incidence, VAR, varus offset, NSA, neck stem angle, and HDA, hip deflection angle.

The schematic diagram of the offset angles. (A) PI of VAR: (i) draw the connecting line of the lowest point of the ischium (white); (ii) draw the connecting line at the junction of the head and neck of the healthy femur head (pink); (iii) draw and measure the NSA, which is 141.58° (blue); (iv) draw the angle between the axis of the healthy femoral neck and the white line, which is 58.45° (yellow); (v) draw the connecting line at the junction of the head and neck of the affected femur head (green); and (vi) draw the angle between the vertical line of the green line and the white line, which is 28.99° (red); thus, PI = 29.46° (58.45°–28.99°). (B) PI of VAL: PI = 16.24° (61.42°–45.18°), and the measurement method is the same as that for the VAR. (C) HDA: (i) draw the axis of femoral head on the pelvis orthophoto (white); (ii) draw the connecting line at the junction of the head and neck of the femur head on the hip axial photo (green); (iii) draw a green vertical line (red); and (iv) the HDA (yellow), which is 45.02° (a backward tilt of the femoral head is defined as positive), is made up by the intersection of the red and the white lines. PI, pelvic incidence, VAR, varus offset, NSA, neck stem angle, and HDA, hip deflection angle.

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Objective Femoral neck fracture (FNF) is a common clinical trauma with high mortality and disability rates. Furthermore, its incidence increases exponentially with increasing age. Existing classifications have some disadvantages. Thus, this study aimed to establish a novel typing system for FNF. Methods We retrospectively analyzed all adult patien...

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... Figure 2 shows the four types of classification of Garden at the base of the front X-ray. Fractures of the femoral neck are classified according to the Garden classification there are four degrees [8,27]: ...
... The Garden classification is based on the identification of the displacement which is essential and, therefore, the fractures must be considered as "not displaced" (grade 1 and 2 fractures), or "displaced" (Garden fractures 3 and 4) [27]. Although the diagnosis of a displaced type 3 or 4 fracture is usually trivial, more subtle type 1 and 2 fractures can The different classes of fractures of the femoral neck: 1) Incomplete and valgus-impacted fracture (stade I); 2) Complete and nondisplaced fracture (stade II); 3) Complete and partially displaced fracture (stade III); 4) Complete and fully displaced fracture (stade 4) [4] be difficult for a clinician, a trainee, or a radiologist without sub-specialized experience in musculoskeletal imaging. ...
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In this study, we propose an efficient fusion framework that utilizes deep learning and a genetic algorithm for the classification of femoral neck fracture images. This is the first study to utilize a genetic algorithm (GA) to optimize the architecture of a Convolutional neural network (CNN) model for the classification of femoral neck fractures. The proposed CNN was trained on a large dataset of 10 000 real patient cases, who underwent both skeletal bone mineral density measurement and hip X-ray at the University Hospital Center of Oujda between 2016 and 2023. The performance of the model was extensively evaluated and compared to various machine learning and deep learning models, including Random Forest, SVM, VGG19, ResNet50, InceptionV3, and EfficientNet. The experimental results demonstrate that the proposed CNN achieved an accuracy of 97%, and it is currently being used by seven doctors at the University Hospital Center of Oujda, Marocco.