Deep Convolutional Neural Networks for Automatic Detection of Orbital Blowout Fractures

Li, Lunhao; Song, Xuefei; Guo, Yucheng; Liu, Yuchen; Sun, Rou; Zou, Hao; Zhou, Huifang; Fan, Xianqun

doi:10.1097/scs.0000000000006069

Cited by 20 publications

(10 citation statements)

References 18 publications

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“…Orbital and eyelid diseases are primarily caused by inflammatory (Lutt et al, 2008), metabolic, and traumatic factors (Li et al, 2020). The anatomy integrity of the orbital and eyelid not only protects and supports important structures, such as the eyeball and optic nerve, but is also critical to the aesthetic appearance of the patient's face (Huggins et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

“…Orbital blowout fractures are one of the most common injuries caused by orbital trauma. Li et al (2020) used the Inception V3 DCNN to automatically classify CT images exhibiting orbital burst fractures. Song et al (2021a) proposed a 3D-ResNet to automatically detect TAO from orbital CT images, and the trained AI algorithm showed excellent performance in a real clinical setting.…”

Section: Sabatesmentioning

confidence: 99%

“…Imaging data, including orbital computed tomography (CT), orbital magnetic resonance imaging (MRI), and external ocular photographs, play a crucial role in the diagnosis and treatment of orbital and eyelid diseases (Bailey and Robinson, 2007;Abdullah et al, 2010). Currently, AI automatically diagnoses and grades some orbital and eyelid diseases, such as orbital blowout fractures and thyroidassociated ophthalmopathy (TAO) (Li et al, 2020;Song et al, 2021a). Automatic measurement of eyelid morphological parameters and automatic surgical decision-making based on AI technology are two recent research hotspots (Bahceci Simsek and Sirolu, 2021;Lou et al, 2021;Hung et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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Orbital and eyelid diseases: The next breakthrough in artificial intelligence?

Bao

Sun

Zhan

et al. 2022

Front. Cell Dev. Biol.

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Orbital and eyelid disorders affect normal visual functions and facial appearance, and precise oculoplastic and reconstructive surgeries are crucial. Artificial intelligence (AI) network models exhibit a remarkable ability to analyze large sets of medical images to locate lesions. Currently, AI-based technology can automatically diagnose and grade orbital and eyelid diseases, such as thyroid-associated ophthalmopathy (TAO), as well as measure eyelid morphological parameters based on external ocular photographs to assist surgical strategies. The various types of imaging data for orbital and eyelid diseases provide a large amount of training data for network models, which might be the next breakthrough in AI-related research. This paper retrospectively summarizes different imaging data aspects addressed in AI-related research on orbital and eyelid diseases, and discusses the advantages and limitations of this research field.

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Section: Discussionmentioning

confidence: 99%

Section: Sabatesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Orbital and eyelid diseases: The next breakthrough in artificial intelligence?

Bao

Sun

Zhan

et al. 2022

Front. Cell Dev. Biol.

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“…Ebism et al [ 32 ] detected the radius in both posteroanterior PA and lateral wrist images using a random forest (RF) classifier. Guo et al [ 33 ] collected CT images of orbital blowout fractures from the Shanghai Ninth People’s Hospital and used the Inception-V3 convolutional neural network (CNN) framework with the XGBoost model to classify the orbital blowout fractures. Zeelan et al [ 34 ] detected bone fracture from X-ray images using the machine learning models probabilistic neural network (PNN), backpropagation neural network (BPNN), and support vector machine (SVM) and classified the input images into the classes skull, head, chest, hand, and spine.…”

Section: Related Workmentioning

confidence: 99%

A Minority Class Balanced Approach Using the DCNN-LSTM Method to Detect Human Wrist Fracture

Rashid

Zia

Rehman

et al. 2023

Life

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The emergency department of hospitals receives a massive number of patients with wrist fracture. For the clinical diagnosis of a suspected fracture, X-ray imaging is the major screening tool. A wrist fracture is a significant global health concern for children, adolescents, and the elderly. A missed diagnosis of wrist fracture on medical imaging can have significant consequences for patients, resulting in delayed treatment and poor functional recovery. Therefore, an intelligent method is needed in the medical department to precisely diagnose wrist fracture via an automated diagnosing tool by considering it a second option for doctors. In this research, a fused model of the deep learning method, a convolutional neural network (CNN), and long short-term memory (LSTM) is proposed to detect wrist fractures from X-ray images. It gives a second option to doctors to diagnose wrist facture using the computer vision method to lessen the number of missed fractures. The dataset acquired from Mendeley comprises 192 wrist X-ray images. In this framework, image pre-processing is applied, then the data augmentation approach is used to solve the class imbalance problem by generating rotated oversamples of images for minority classes during the training process, and pre-processed images and augmented normalized images are fed into a 28-layer dilated CNN (DCNN) to extract deep valuable features. Deep features are then fed to the proposed LSTM network to distinguish wrist fractures from normal ones. The experimental results of the DCNN-LSTM with and without augmentation is compared with other deep learning models. The proposed work is also compared to existing algorithms in terms of accuracy, sensitivity, specificity, precision, the F1-score, and kappa. The results show that the DCNN-LSTM fusion achieves higher accuracy and has high potential for medical applications to use as a second option.

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“…In recent years, deep learning plays an increasingly significant role in the field of medical image processing. Like in the field of lesion detection, the identification of coronavirus disease 2019 patients, 19 the establishment of a cervical cancer screening system, 20 and the identification of patients with blowout fracture of orbit, 21 etc. utilize this technology.…”

mentioning

confidence: 99%

Deep Learning-Based CT Radiomics for Feature Representation and Analysis of Aging Characteristics of Asian Bony Orbit

Zhu

Chen

Yang

et al. 2021

Journal of Craniofacial Surgery

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Objective: This paper puts forward a new method for automatic segmentation of bony orbit as well as automatic extraction and classification of aging features of segmented orbit contour based on depth learning, with which the aging mode of bony orbit contour is preliminarily validated. Method: Three-dimensional reconstruction was carried out by using the craniofacial Computed Tomography scanning data of 595 adult Mongolians at different ages (119 young males, 78 young females, 109 middle-aged males, 89 middle-aged females, 95 elderly males, and 105 elderly females), the craniofacial images were exported, orbit contour images were obtained with U-Net segmentation network, and then the orbit contour features of young group, the middle-aged group and the elderly group were classified with the classification network. Next, contour area, height, and other features put forward in existing research were automatically calculated by using the connected component shape description method; and it was validated whether the aging features of the bony orbit only occur to partial or the whole orbit. Results: With the method put forward in this paper, high-precision identification (97.94% and 99.18%) of 3 categories in the male and female group experiments. In the meanwhile, it was found in the comparison experiment with other features that bony orbit contour definitely has features relating to aging, but these features only occur to partial areas of the orbit, which enables the convolutional neural network to achieve good identification effects. And, bone resorption of the superior orbital rim of males is more obvious than that of the inferior orbital rim, but the overall shape features like the bony orbit area and height do not change significantly along with the increase of the age. Conclusions: U-Net can realize high-precision segmentation of the orbit contour, and with the Convolutional Neural Network-based orbit contour sorting algorithm, the aging degree of the bony orbit can be identified precisely. It is preliminarily validated that the aging mode of Mongolian bony orbit contour is that the bone resorption of the superior orbital rim is more obvious than that of the inferior orbital rim, and the change of the orbit area, perimeter, height and circularity is not obvious in the aging process.

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Deep Convolutional Neural Networks for Automatic Detection of Orbital Blowout Fractures

Cited by 20 publications

References 18 publications

Orbital and eyelid diseases: The next breakthrough in artificial intelligence?

Orbital and eyelid diseases: The next breakthrough in artificial intelligence?

A Minority Class Balanced Approach Using the DCNN-LSTM Method to Detect Human Wrist Fracture

Deep Learning-Based CT Radiomics for Feature Representation and Analysis of Aging Characteristics of Asian Bony Orbit

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