The effect of patient, fracture and surgery on outcomes of high energy neck of femur fractures in patients aged 15–50

Hoskins, Wayne; Rayner, Johnny; Sheehy, Rohan; Claireaux, Harry; Bingham, Roger; Santos, Roselyn; Bucknill, Andrew

doi:10.1177/1120700018761154

Cited by 7 publications

(5 citation statements)

References 41 publications

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“…Open reduction is dictated when the neck fracture remains displaced following closed or other minimal invasive manoeuvres. The reported complications of the proximal segment of FN-FD fractures are associated mostly with malreduction and often lead to failure of the primary goal of hip preservation in this young patient population ( 56 ).…”

Section: Discussionmentioning

confidence: 99%

Surgical fixation of ipsilateral femoral neck and shaft fractures: a matter of debate?

Bastian,

Ivanova,

Mabrouk

et al. 2023

EFORT Open Reviews

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Segmental femoral fractures represent a rare but complex clinical challenge. They mostly result from high-energy mechanisms, dictate a careful initial assessment and are managed with various techniques. These often include an initial phase of damage control orthopaedics while the initial manoeuvres of patient and soft tissue resuscitation are employed. Definitive fixation consists of either single-implant (reconstruction femoral nails) or dual-implant constructs. There is no consensus in favour of one of these two strategies. At present, there is no high-quality comparative evidence between the various methods of treatment. The development of advanced design nailing and plating systems has offered fixation constructs with improved characteristics. A comprehensive review of the existing evidence with a step-by-step description of these different definitive fixation strategies based on three case examples was conducted. Furthermore, the rationale for using single vs dual-implant strategy in its case is presented with supportive references. The prevention of complications relies mainly on the strict adherence to basic principles of fracture fixation with an emphasis on careful preoperative planning, the quality of the reduction, and the application of soft tissue-friendly surgical methods.

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Section: Discussionmentioning

confidence: 99%

Surgical fixation of ipsilateral femoral neck and shaft fractures: a matter of debate?

Bastian,

Ivanova,

Mabrouk

et al. 2023

EFORT Open Reviews

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“…Type IV is described as a complete fracture with complete displacement. The features of displaced FNFs are clinically distinct and distinct through imaging, whereas those of nondisplaced FNFs are challenging and receive less attention [5][6][7][8]. The radiographic imaging of nondisplaced FNFs can be compromised by osteoporosis, obesity, patient position-related reasons, the use of portable radiographic equipment, and poor image quality, which creates additional difficulties for clinicians [6,9].…”

Section: Introductionmentioning

confidence: 99%

A Computer-Assisted Diagnostic Method for Accurate Detection of Early Nondisplaced Fractures of the Femoral Neck

Hsieh,

Chiang,

Chuang

et al. 2023

Biomedicines

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Nondisplaced femoral neck fractures are sometimes misdiagnosed by radiographs, which may deteriorate into displaced fractures. However, few efficient artificial intelligent methods have been reported. We developed an automatic detection method using deep learning networks to pinpoint femoral neck fractures on radiographs to assist physicians in making an accurate diagnosis in the first place. Our proposed accurate automatic detection method, called the direction-aware fracture-detection network (DAFDNet), consists of two steps, namely region-of-interest (ROI) segmentation and fracture detection. The first step removes the noise region and pinpoints the femoral neck region. The fracture-detection step uses a direction-aware deep learning algorithm to mark the exact femoral neck fracture location in the region detected in the first step. A total of 3840 femoral neck parts in anterior–posterior (AP) pelvis radiographs collected from the China Medical University Hospital database were used to test our method. The simulation results showed that DAFDNet outperformed the U-Net and DenseNet methods in terms of the IOU value, Dice value, and Jaccard value. Our proposed DAFDNet demonstrated over 94.8% accuracy in differentiating non-displaced Garden type I and type II femoral neck fracture cases. Our DAFDNet method outperformed the diagnostic accuracy of general practitioners and orthopedic surgeons in accurately locating Garden type I and type II fracture locations. This study can determine the feasibility of applying artificial intelligence in a clinical setting and how the use of deep learning networks assists physicians in improving correct diagnoses compared to the current traditional orthopedic manual assessments.

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“…Femoral neck fractures account for approximately half of all hip fractures and are one of the most common injuries in the elderly population and result in high-financial burden [3,4]. Femoral neck fractures in patients under the age of 60 are usually due to high-energy trauma, which poses challenges for surgical treatment and only a small number of patients can return to their pre-injury activity level [5,6]. Somehow, imaging misdiagnosis of non-displaced femoral neck fractures may occur in non-orthopedic surgeons or young radiologists in the emergency department.…”

Section: Introductionmentioning

confidence: 99%

A Combination of FPU-Net and Feature Clustering Methods for Accurate Segmentation of Femoral Neck in Radiographic Diagnosis

Chen,

Chuang,

Hsieh

et al. 2023

Diagnostics

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In this study, we develop an innovative method that assists computer-aided diagnosis in the determination process of the exact location of the femoral neck junction in plain radiographs. Our algorithm consists of two phases, i.e., coarse prediction and fine matching, which are implemented by supervised deep learning method and unsupervised clustering, respectively. In coarse prediction, standard masks are first produced by a specialist and trained in our proposed feature propagation network (FPU-Net) with supervised learning on the femoral neck dataset. In fine matching, the standard masks are first classified into different categories using our proposed three parameters with unsupervised learning. The predicted mask from FPU-Net is matched with each category of standard masks by calculating the values of intersection of union (IOU), and finally the predicted mask is substituted by the standard mask with the largest IOU value. A total of 4320 femoral neck parts in anterior–posterior (AP) pelvis radiographs collected from China Medical University Hospital database were used to test our method. Simulation results show that, on the one hand, compared with other segmentation methods, the method proposed in this paper has a larger IOU value and better suppression of noise outside the region of interest; on the other hand, the introduction of unsupervised learning for fine matching can help in the accurate localization segmentation of femoral neck images. Accurate femoral neck segmentation can assist surgeons to diagnose and reduce the misdiagnosis rate and burden.

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The effect of patient, fracture and surgery on outcomes of high energy neck of femur fractures in patients aged 15–50

Abstract: At a mean 4.9-year follow-up, the incidence of high-energy femoral neck fractures leading to THA was 9.4%, as a consequence of osteonecrosis or nonunion. Malunion was common.

Cited by 7 publications

References 41 publications

Surgical fixation of ipsilateral femoral neck and shaft fractures: a matter of debate?

Surgical fixation of ipsilateral femoral neck and shaft fractures: a matter of debate?

A Computer-Assisted Diagnostic Method for Accurate Detection of Early Nondisplaced Fractures of the Femoral Neck

A Combination of FPU-Net and Feature Clustering Methods for Accurate Segmentation of Femoral Neck in Radiographic Diagnosis

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