Computer Aided Facial Bone Fracture Diagnosis (CA-FBFD) System Based on Object Detection Model

Moon, GwiSeong; Kim, Seola; Kim, Woojin; Kim, Yoon; Jeong, Yeonjin; Choi, Hyun-Soo

doi:10.1109/access.2022.3192389

Cited by 20 publications

(14 citation statements)

References 32 publications

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“…In traumatic cases, CNN has shown positive results in detecting bone fractures in plain x-rays and CT images with accuracies comparable to or, in some cases, superior to those of experts 13 – 15 . There are few studies on the adaptation of CNN to detect fractures in maxillofacial regions in CT images, including the detection of mandibular fractures 16 , nasal fractures 17 and maxillofacial fractures 18 , 19 . In general, binary classification or detection is used to discriminate or identify whether a lesion is present in the medical image.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, multiclass classification or detection could provide specific information or localization of the object of interest in the image 20 . Using a CNN classification model to detect the fracture in the image has the limitation of not being able to determine the exact location of the fracture 18 . Using an object detection model would overcome this limitation and pinpoint the exact location of the fracture in the image 18 .…”

Section: Introductionmentioning

confidence: 99%

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Maxillofacial fracture detection and classification in computed tomography images using convolutional neural network-based models

Warin

Limprasert

Suebnukarn

et al. 2023

Sci Rep

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The purpose of this study was to evaluate the performance of convolutional neural network-based models for the detection and classification of maxillofacial fractures in computed tomography (CT) maxillofacial bone window images. A total of 3407 CT images, 2407 of which contained maxillofacial fractures, were retrospectively obtained from the regional trauma center from 2016 to 2020. Multiclass image classification models were created by using DenseNet-169 and ResNet-152. Multiclass object detection models were created by using faster R-CNN and YOLOv5. DenseNet-169 and ResNet-152 were trained to classify maxillofacial fractures into frontal, midface, mandibular and no fracture classes. Faster R-CNN and YOLOv5 were trained to automate the placement of bounding boxes to specifically detect fracture lines in each fracture class. The performance of each model was evaluated on an independent test dataset. The overall accuracy of the best multiclass classification model, DenseNet-169, was 0.70. The mean average precision of the best multiclass detection model, faster R-CNN, was 0.78. In conclusion, DenseNet-169 and faster R-CNN have potential for the detection and classification of maxillofacial fractures in CT images.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Maxillofacial fracture detection and classification in computed tomography images using convolutional neural network-based models

Warin

Limprasert

Suebnukarn

et al. 2023

Sci Rep

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show abstract

“…In recent years, the number of requests for computed tomography (CT), magnetic resonance imaging (MRI), and, in general, radiology services have grown dramatically [1]. Nevertheless, there is a lack of radiologists due to recruitment challenges and many retirements.…”

Section: Introductionmentioning

confidence: 99%

“…To diagnose maxillofacial fractures, several radiographic approaches have been utilized. C. AlexNet [1,5] A large perceptron (RNN) may be able to achieve high excellent on a highly difficult dataset by using solely supervised learning methodologies, according to the findings of the AlexNet study. In the year after the debut of AlexNet, a competition was launched that has continued to this day.…”

Section: Introductionmentioning

confidence: 99%

“…is a convolutional neural network that is 50 layers deep. You can load a pretrained version of the network trained on more than a million images from the ImageNet database[1]. It is a construction piece that was destroyed but which still contains a bridged connector (formerly known as a legacy connection) that permits data to flow through it without being altered in any way despite the destruction.…”

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confidence: 99%

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Maxillofacial Fracture Detection Using Transfer Learning Models : A Review

et al. 2022

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Early detection and treatment of face bone fractures reduce long-term problems. Fracture identification needs CT scan interpretation, but there aren't enough experts. To address these issues, researchers are classifying and identifying objects. Categorization-based studies can't pinpoint fractures. Proposed Study Convolutional neural networks with transfer learning may detect maxillofacial fractures. CT scans were utilized to retrain and fine-tune a convolutional neural network trained on non-medical images to categorize incoming CTs as "Positive" or "Negative." Model training employed maxillofacial fractogram data. If two successive slices had a 95% fracture risk, the patient had a fracture. In terms of sensitivity/person for facial fractures, the recommended strategy beat the machine learning model. The recommended approach may minimize physicians' effort identifying facial bone fractures in face CT. Even though technology can't fully replace a radiologist, the recommended technique may be helpful. It reduces human error, diagnostic delays, and hospitalization costs.

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Applications of Artificial Intelligence, Machine Learning, and Deep Learning on Facial Plastic Surgeries

Tokgoz

Carro

2023

Cosmetic and Reconstructive Facial Plastic Surgery

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Computer Aided Facial Bone Fracture Diagnosis (CA-FBFD) System Based on Object Detection Model

Cited by 20 publications

References 32 publications

Maxillofacial fracture detection and classification in computed tomography images using convolutional neural network-based models

Maxillofacial fracture detection and classification in computed tomography images using convolutional neural network-based models

Maxillofacial Fracture Detection Using Transfer Learning Models : A Review

Applications of Artificial Intelligence, Machine Learning, and Deep Learning on Facial Plastic Surgeries

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