Decision effect of a deep-learning model to assist a head computed tomography order for pediatric traumatic brain injury

Heo, Sejin; Ha, Juhyung; Jung, Weon; Yoo, Suyoung; Song, Yeejun; Kim, Taerim; Chul, Won

doi:10.1038/s41598-022-16313-0

Cited by 6 publications

(2 citation statements)

References 40 publications

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“…The results demonstrate the effectiveness of convolutional neural networks in assisting neurologists in classifying stroke types based on CT head image classifications. Additionally, a significant enhancement in stroke identification and segmentation has been observed when employing the proposed modified U-Net model compared to the traditional U-Net architecture [37].…”

Section: Discussionmentioning

confidence: 95%

Deep Learning Enhanced Internet of Medical Things to Analyze Brain Computed Tomography Images of Stroke Patients

Omarov,

Tursynova,

Uzak

2023

IJACSA

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In the realm of advancing medical technology, this paper explores a revolutionary amalgamation of deep learning algorithms and the Internet of Medical Things (IoMT), demonstrating their efficacy in decoding the labyrinthine intricacies of brain Computed Tomography (CT) images from stroke patients. Deploying an avant-garde deep learning framework, we lay bare the system's ability to distill complex patterns, from multifarious imaging data, that often elude traditional analysis techniques. Our research punctuates the pioneering leap from conventional, mostly uniform methods towards harnessing the power of a nuanced, more perplexing approach that embraces the intricacies of the human brain. This system goes beyond the mere novelty, evidencing a substantial enhancement in early detection and prognosis of strokes, expediting clinical decisions, and thereby potentially saving lives. Contrasting sentencessome more terse, others elongated and packed with detailsdelineate our innovative concept's contours, underpinning the notion of burstiness. Moreover, the inclusion of IoMT provides a digital highway for seamless and real-time data flow, enabling quick responses in critical situations. We demonstrate, through an array of comprehensive tests and clinical studies, how this synergy of deep learning and IoMT elevates the precision, speed, and overall effectiveness of stroke diagnosis and treatment. By embracing the untapped potential of this combined approach, our paper nudges the medical world closer to a future where technology is woven seamlessly into the fabric of healthcare, allowing for a more personalized and efficient approach to patient treatment.

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Section: Discussionmentioning

confidence: 95%

Deep Learning Enhanced Internet of Medical Things to Analyze Brain Computed Tomography Images of Stroke Patients

Omarov,

Tursynova,

Uzak

2023

IJACSA

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“…Since the WIC 4 developed in this study is mainly used to predict severe brain injury, it is a typical binary classi cation task since it is divided the head injury level in 50 accidents into two categories severe and non-severe. The Area under the receiver operating characteristic (AUROC, abbreviated as AUC) is a common performance evaluation metric for binary classi cation models 19,35,45 , with values ranging from 0.5 to 1. The closer the value is to 1, the better the model prediction performance.…”

Section: Validating Wicmentioning

confidence: 99%

Establishment of a weighted injury criterion for evaluation of multiple types of head severe injuries for vulnerable road users

Han

Yang

et al. 2023

Preprint

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In traffic accidents, multiple head injury types often occur simultaneously and cause severe brain injury for vulnerable road users (VRUs). In this study, a head-weighted injury criterion was developed to assess the risk of severe brain injury considering various injury types. Firstly, 50 in-depth accidents were reconstructed using a high-precision reconstruction method to reconstruct the overall kinematic response and head injury severity of VRUs and analyze the correlation between various head injury criteria and severe brain injury. Then, four injury criteria were selected that correlated well with severe brain injury, namely HIC15, angular acceleration, coup pressure, and maximum principal strain (MPS). Finally, weighted head injury criteria (WIC4) of severe injuries were established based on the four selected injury criteria, and the correlation between WIC4 and severe brain injury was validated based on the area under of receiver operating characteristic curve (AUROC) and the reconstructed results of another 10 selected accidents. The results showed that WIC4 had a good predictive capability for both severe and non-severe brain injury cases, and the AUROC was 0.983, which was significantly higher than that of the single head injury criterion. This study further improved the correlation between head injury criteria and severe brain injury.

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A comprehensive review and experimental comparison of deep learning methods for automated hemorrhage detection

Neethi,

Kannath,

Kumar

et al. 2024

Engineering Applications of Artificial Intelligence

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Decision effect of a deep-learning model to assist a head computed tomography order for pediatric traumatic brain injury

Cited by 6 publications

References 40 publications

Deep Learning Enhanced Internet of Medical Things to Analyze Brain Computed Tomography Images of Stroke Patients

Deep Learning Enhanced Internet of Medical Things to Analyze Brain Computed Tomography Images of Stroke Patients

Establishment of a weighted injury criterion for evaluation of multiple types of head severe injuries for vulnerable road users

A comprehensive review and experimental comparison of deep learning methods for automated hemorrhage detection

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