Artificial Intelligence Model Trained with Sparse Data to Detect Facial and Cranial Bone Fractures from Head CT

Wang, Huan-Chih; Wang, Shao-Chung; Yan, Jiun-Lin; Ko, Li-Wei

doi:10.1007/s10278-023-00829-6

Cited by 3 publications

(1 citation statement)

References 31 publications

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“…Furthermore, the technical development of the deep learning approach was not well described as pointed out by other authors (Liu et al, 2019). Wang et al (2023) presented a deep learning framework designed to automatically identify bone fractures in both cranial and facial regions. Their system integrates YOLOv4 for streamlined fracture detection and ResUNet++ (Jha et al, 2019) for segmentation of cranial and facial bones.…”

Section: Frontal-bone Fracturesmentioning

confidence: 99%

A review on artificial intelligence for the diagnosis of fractures in facial trauma imaging

Pham,

Holmes,

Coulthard

2024

Front. Artif. Intell.

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Patients with facial trauma may suffer from injuries such as broken bones, bleeding, swelling, bruising, lacerations, burns, and deformity in the face. Common causes of facial-bone fractures are the results of road accidents, violence, and sports injuries. Surgery is needed if the trauma patient would be deprived of normal functioning or subject to facial deformity based on findings from radiology. Although the image reading by radiologists is useful for evaluating suspected facial fractures, there are certain challenges in human-based diagnostics. Artificial intelligence (AI) is making a quantum leap in radiology, producing significant improvements of reports and workflows. Here, an updated literature review is presented on the impact of AI in facial trauma with a special reference to fracture detection in radiology. The purpose is to gain insights into the current development and demand for future research in facial trauma. This review also discusses limitations to be overcome and current important issues for investigation in order to make AI applications to the trauma more effective and realistic in practical settings. The publications selected for review were based on their clinical significance, journal metrics, and journal indexing.

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Section: Frontal-bone Fracturesmentioning

confidence: 99%

A review on artificial intelligence for the diagnosis of fractures in facial trauma imaging

Pham,

Holmes,

Coulthard

2024

Front. Artif. Intell.

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Artificial Intelligence Application in Skull Bone Fracture with Segmentation Approach

Lu,

Wang,

Chen

et al. 2024

J Digit Imaging. Inform. med.

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This study aims to evaluate an AI model designed to automatically classify skull fractures and visualize segmentation on emergent CT scans. The model’s goal is to boost diagnostic accuracy, alleviate radiologists’ workload, and hasten diagnosis, thereby enhancing patient outcomes. Unique to this research, both pediatric and post-operative patients were not excluded, and diagnostic durations were analyzed. Our testing dataset for the observer studies involved 671 patients, with a mean age of 58.88 years and fairly balanced gender representation. Model 1 of our AI algorithm, trained with 1499 fracture-positive cases, showed a sensitivity of 0.94 and specificity of 0.87, with a DICE score of 0.65. Implementing post-processing rules (specifically Rule B) improved the model’s performance, resulting in a sensitivity of 0.94, specificity of 0.99, and a DICE score of 0.63. AI-assisted diagnosis resulted in significantly enhanced performance for all participants, with sensitivity almost doubling for junior radiology residents and other specialists. Additionally, diagnostic durations were significantly reduced (p < 0.01) with AI assistance across all participant categories. Our skull fracture detection model, employing a segmentation approach, demonstrated high performance, enhancing diagnostic accuracy and efficiency for radiologists and clinical physicians. This underlines the potential of AI integration in medical imaging analysis to improve patient care.

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Very fast, high-resolution aggregation 3D detection CAM to quickly and accurately find facial fracture areas

Moon,

Lee,

Kim

et al. 2024

Computer Methods and Programs in Biomedicine

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Artificial Intelligence Model Trained with Sparse Data to Detect Facial and Cranial Bone Fractures from Head CT

Cited by 3 publications

References 31 publications

A review on artificial intelligence for the diagnosis of fractures in facial trauma imaging

A review on artificial intelligence for the diagnosis of fractures in facial trauma imaging

Artificial Intelligence Application in Skull Bone Fracture with Segmentation Approach

Very fast, high-resolution aggregation 3D detection CAM to quickly and accurately find facial fracture areas

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