Dataset Information

Surgical navigation improves reductions accuracy of unilateral complicated zygomaticomaxillary complex fractures: a randomized controlled trial.

ABSTRACT: Accurate reduction is the key to successful treatment of bone fractures. Complicated zygomaticomaxillary complex fracture, known as one of the most challenging facial bone fractures, is often hard to achieve an accurate reduction, thus leading to facial deformity. In this study, twenty patients with unilateral complicated zygomaticomaxillary complex fractures were included and randomly divided into experimental and control groups, which is with and without the aid of surgical navigation, respectively. The pre- and postoperative imaging data were collected and then analysed using Geomagic Studio 11 software and Brainlab iPlan CMF 3.0. A more precise reduction was showed in the experimental group according to the measurement results of both software programmes than in the control group. In conclusion, surgical navigation showed great value in performing accurate reductions of complicated zygomaticomaxillary complex fractures and restoring facial contour.

SUBMITTER: Zhang X

PROVIDER: S-EPMC5932064 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Surgical navigation improves reductions accuracy of unilateral complicated zygomaticomaxillary complex fractures: a randomized controlled trial.

Zhang Xiao X Ye Lanfeng L Li Hui H Wang Yi Y Dilxat Dilnur D Liu Weilong W Chen Yuanwei Y Liu Lei L

Scientific reports 20180502 1

Accurate reduction is the key to successful treatment of bone fractures. Complicated zygomaticomaxillary complex fracture, known as one of the most challenging facial bone fractures, is often hard to achieve an accurate reduction, thus leading to facial deformity. In this study, twenty patients with unilateral complicated zygomaticomaxillary complex fractures were included and randomly divided into experimental and control groups, which is with and without the aid of surgical navigation, respect ...[more]

PMID: 29720719

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Project description:BackgroundLess experienced clinicians sometimes make misdiagnosis of hip fractures. We developed computer-aided diagnosis (CAD) system for hip fractures on plain X-rays using a deep learning model trained on a large dataset. In this study, we examined whether the accuracy of the diagnosis of hip fracture of the residents could be improved by using this system.MethodsA deep convolutional neural network approach was used for machine learning. Pytorch 1.3 and Fast.ai 1.0 were applied as frameworks, and an EfficientNet-B4 model (a pre-trained ImageNet model) was used. We handled the 5295 X-rays from the patients with femoral neck fracture or femoral trochanteric fracture from 2009 to 2019. We excluded cases in which the bilateral hips were not included within an image range, and cases of femoral shaft fracture and periprosthetic fracture. Finally, we included 5242 AP pelvic X-rays from 4851 cases. We divided these 5242 images into two images per image, and prepared 5242 images including fracture site and 5242 images without fracture site. Thus, a total of 10,484 images were used for machine learning. The accuracy, sensitivity, specificity, F-value, and area under the curve (AUC) were assessed. Gradient-weighted class activation mapping (Grad-CAM) was used to conceptualize the basis for the diagnosis of the fracture by the deep learning algorithm. Secondly, we conducted a controlled experiment with clinicians. Thirty-one residents;young doctors within 2 years of graduation from medical school who rotate through various specialties, were tested using 300 hip fracture images that were randomly extracted from the dataset. We evaluated the diagnostic accuracy with and without the use of the CAD system for each of the 300 images.ResultsThe accuracy, sensitivity, specificity, F-value, and AUC were 96.1, 95.2, 96.9%, 0.961, and 0.99, respectively, with the correct diagnostic basis generated by Grad-CAM. In the controlled experiment, the diagnostic accuracy of the residents significantly improved when they used the CAD system.ConclusionsWe developed a newly CAD system with a deep learning algorithm from a relatively large dataset from multiple institutions. Our system achieved high diagnostic performance. Our system improved the diagnostic accuracy of residents for hip fractures.Level of evidenceLevel III, Foundational evidence, before-after study.Clinical relevancehigh.

Project description:PURPOSE:This paper introduces the SciKit-Surgery libraries, designed to enable rapid development of clinical applications for image-guided interventions. SciKit-Surgery implements a family of compact, orthogonal, libraries accompanied by robust testing, documentation, and quality control. SciKit-Surgery libraries can be rapidly assembled into testable clinical applications and subsequently translated to production software without the need for software reimplementation. The aim is to support translation from single surgeon trials to multicentre trials in under 2 years. METHODS:At the time of publication, there were 13 SciKit-Surgery libraries provide functionality for visualisation and augmented reality in surgery, together with hardware interfaces for video, tracking, and ultrasound sources. The libraries are stand-alone, open source, and provide Python interfaces. This design approach enables fast development of robust applications and subsequent translation. The paper compares the libraries with existing platforms and uses two example applications to show how SciKit-Surgery libraries can be used in practice. RESULTS:Using the number of lines of code and the occurrence of cross-dependencies as proxy measurements of code complexity, two example applications using SciKit-Surgery libraries are analysed. The SciKit-Surgery libraries demonstrate ability to support rapid development of testable clinical applications. By maintaining stricter orthogonality between libraries, the number, and complexity of dependencies can be reduced. The SciKit-Surgery libraries also demonstrate the potential to support wider dissemination of novel research. CONCLUSION:The SciKit-Surgery libraries utilise the modularity of the Python language and the standard data types of the NumPy package to provide an easy-to-use, well-tested, and extensible set of tools for the development of applications for image-guided interventions. The example application built on SciKit-Surgery has a simpler dependency structure than the same application built using a monolithic platform, making ongoing clinical translation more feasible.

Dataset Information

Surgical navigation improves reductions accuracy of unilateral complicated zygomaticomaxillary complex fractures: a randomized controlled trial.

Publications

Surgical navigation improves reductions accuracy of unilateral complicated zygomaticomaxillary complex fractures: a randomized controlled trial.

Similar Datasets

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