In a mass casualty incident, the factors that determine the survival rate of injured patients are diverse, but one of the key factors is the time for triage. Additionally, the main factor that determines the time of triage is the number of medical personnel. However, when relying on a small number of medical personnel, the ability to increase survivability is limited. Therefore, developing a classification model for survival prediction that can quickly and precisely triage via wearable devices without medical personnel is important. In this study, we designed a consciousness index to substitute the factor by manpower and improved the classification accuracy by applying a machine learning algorithm. First, logistic regression analysis using vital signs and a consciousness index capable of remote monitoring through wearable devices confirmed the high efficiency of the consciousness index. We then developed a classification model with high accuracy which corresponds to existing injury severity scoring systems through the machine learning algorithms. We extracted 460,865 cases which met our criteria for developing the survival prediction from the national sample project in the national trauma databank which contains 408,316 cases of blunt injury and 52,549 cases of penetrating injury. Among the dataset, 17,918 (3.9%) cases died while the other survived. The AUCs with 95% confidence intervals (CIs) for the different models with the proposed simplified consciousness score as follows: RTS (as baseline), 0.78 (95% CI = 0.775 to 0.785); logistic regression, 0.87 (95% CI = 0.862 to 0.870); random forest, 0.87 (95% CI = 0.862 to 0.872); deep neural network, 0.89 (95% CI = 0.882 to 0.890). As a result, we confirmed the possibility of remote triage using a wearable device. It is expected that the time required for triage can be effectively reduced by using the developed classification model of survival prediction.
Ménière's Disease (MD) is difficult to diagnose and evaluate objectively over the course of treatment. Recently, several studies have reported MD diagnoses by MRI-based endolymphatic hydrops (EH) analysis. However, this method is time-consuming and complicated. Therefore, a fast, objective, and accurate evaluation tool is necessary. The purpose of this study was to develop an algorithm that can accurately analyze EH on intravenous (IV) gadolinium (Gd)-enhanced inner-ear MRI using artificial intelligence (AI) with deep learning. In this study, we developed a convolutional neural network (CNN)based deep-learning model named INHEARIT (INner ear Hydrops Estimation via ARtificial InTelligence) for the automatic segmentation of the cochlea and vestibule, and calculation of the EH ratio in the segmented region. Measurement of the EH ratio was performed manually by a neuro-otologist and neuro-radiologist and by estimation with the INHEARIT model and were highly consistent (intraclass correlation coefficient = 0.971). This is the first study to demonstrate that automated EH ratio measurements are possible, which is important in the current clinical context where the usefulness of IV-Gd inner-ear MRI for MD diagnosis is increasing. Ménière's disease (MD) is a multifactorial disorder with typical symptoms of recurrent spontaneous attacks of vertigo, fluctuating hearing loss, tinnitus, and sensations of ear fullness. Endolymphatic hydrops (EH) is a pathological finding where the endolymphatic spaces are distended by enlargements of endolymphatic volume, a histologic hallmark of MD 1-3. According to a 1995 consensus statement from the Committee on Hearing and Equilibrium of the American Association of Otolaryngology-Head and Neck Surgery (AAO-HNS), "certain" MD cases can only be confirmed by the histological demonstration of EH in postmortem temporal bone specimens 4. Therefore in 2015, a committee of the Bárány Society revised the diagnostic criteria to remove the concept of "certain MD" 5. Thus far, the diagnostic criteria have been changed due to the lack of tools to objectively find EH during life. However, with the advancement of imaging technology, MRI can be used to identify endolymphatic hydrops in MD patients as an objective marker. In 2004, Duan et al. succeeded in visualizing EH in vivo for the first time in a guinea pig using 4.7 T MRI 6. Nakashima et al. succeeded in confirming EH after injecting contrast media through intratympanic (IT) and intravenous (IV) injections into MD patients using 3 T MRI 7,8. Recently, many reports have been published regarding the use of MRI to assess EH. In particular, IV gadolinium (Gd)-enhanced inner-ear MRI has shown good results 9,10. We have also proven through previous studies that IV-Gd inner-ear MRI is very useful for diagnosing MD by demonstrating the correlation of hydrops with
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