Near-infrared spectroscopy for intracranial hemorrhage detection in traumatic brain injury patients: A systematic review

Viderman, Dmitriy; Ayapbergenov, Alibek; Abilman, Nazerkem; Abdildin, Yerkin G.

doi:10.1016/j.ajem.2021.09.070

Cited by 6 publications

(1 citation statement)

References 26 publications

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“…Recently, a series of tools has been developed to serve as a clinical assistant to identify ICH, such as DL model 46 , near-infrared spectroscopy (NIRS) 63 . A systematic review, conducted in 2021, indicated NIRS presented as average sensitivity 0.90 and specificity 0.77 in ICH detection 64 . However, low specificity may limit the application of NIRS.…”

Section: Discussionmentioning

confidence: 99%

Deep learning-assisted detection and segmentation of intracranial hemorrhage in noncontrast computed tomography scans of acute stroke patients: a systematic review and meta-analysis

Hu,

Yan,

Xiao

et al. 2024

International Journal of Surgery

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Background: Deep learning (DL)-assisted detection and segmentation of intracranial hemorrhage stroke in noncontrast computed tomography (NCCT) scans are well-established, but evidence on this topic is lacking. Materials and methods: PubMed and Embase databases were searched from their inception to November 2023 to identify related studies. The primary outcomes included sensitivity, specificity, and the Dice Similarity Coefficient (DSC); while the secondary outcomes were positive predictive value (PPV), negative predictive value (NPV), precision, area under the receiver operating characteristic curve (AUROC), processing time, and volume of bleeding. Random-effect model and bivariate model were used to pooled independent effect size and diagnostic meta-analysis data, respectively. Results: A total of 36 original studies were included in this meta-analysis. Pooled results indicated that DL technologies have a comparable performance in intracranial hemorrhage detection and segmentation with high values of sensitivity (0.89, 95% CI: 0.88–0.90), specificity (0.91, 95% CI: 0.89–0.93), AUROC (0.94, 95% CI: 0.93–0.95), PPV (0.92, 95% CI: 0.91–0.93), NPV (0.94, 95% CI: 0.91–0.96), precision (0.83, 95% CI: 0.77–0.90), DSC (0.84, 95% CI: 0.82–0.87). There is no significant difference between manual labeling and DL technologies in hemorrhage quantification (MD 0.08, 95% CI: −5.45–5.60, P=0.98), but the latter takes less process time than manual labeling (WMD 2.26, 95% CI: 1.96–2.56, P=0.001). Conclusion: This systematic review has identified a range of DL algorithms that the performance was comparable to experienced clinicians in hemorrhage lesions identification, segmentation, and quantification but with greater efficiency and reduced cost. It is highly emphasized that multicenter randomized controlled clinical trials will be needed to validate the performance of these tools in the future, paving the way for fast and efficient decision-making during clinical procedure in patients with acute hemorrhagic stroke.

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Section: Discussionmentioning

confidence: 99%

Deep learning-assisted detection and segmentation of intracranial hemorrhage in noncontrast computed tomography scans of acute stroke patients: a systematic review and meta-analysis

Hu,

Yan,

Xiao

et al. 2024

International Journal of Surgery

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Wearable Single‐Electrode Capacitive Sensor with Large Penetration Depth for Intelligent Deep Tissue and Hemorrhage Monitoring

Cheng,

Kim,

White

et al. 2024

Advanced Sensor Research

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Monitoring deep tissue biometrics is crucial in various clinical settings, including internal hemorrhage. Although optical and impedance tomography techniques offer real‐time monitoring with minimal medical infrastructure, they still face challenges in accurately assessing deeper tissues in wearable formats. This study introduces a novel single‐electrode capacitive sensor designed to measure deep tissue capacitance changes caused by variations in dielectric constant and pressure. The sensor features a carbon nanotube‐paper composite (CPC) electrode integrated with a multi‐walled carbon nanotube (MWCNT)‐embedded foam. The CPC electrode, with its large surface area and high‐aspect‐ratio fibers, generates a high electric field for deeper tissue penetration, improving deep tissue monitoring performance. Penetration depth is characterized using surrogate tissue, heart, and lung models. Additionally, the integration of pressure‐sensitive MWCNT foam significantly enhances the sensitivity, enabling precise detection of regional blood volume and tissue displacement. The novel sensing mechanism is applied to detect internal hemorrhage in a porcine model. By employing a machine learning algorithm, the sensor accurately estimates the severity of internal hemorrhage, offering a noninvasive alternative to catheter‐based systems. This advancement lays the foundation for a real‐time wearable system that monitors deep tissue health metrics, such as blood volume, blood pressure, as well as heart and lung functions.

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Online Analysis of Malachite Content in the Beneficiation Process Based on Visible-NIR Spectroscopy and GWO-SVM Algorithm

Zhan

Guo

Zuo

et al. 2023

Mining, Metallurgy & Exploration

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Near-infrared spectroscopy for intracranial hemorrhage detection in traumatic brain injury patients: A systematic review

Cited by 6 publications

References 26 publications

Deep learning-assisted detection and segmentation of intracranial hemorrhage in noncontrast computed tomography scans of acute stroke patients: a systematic review and meta-analysis

Deep learning-assisted detection and segmentation of intracranial hemorrhage in noncontrast computed tomography scans of acute stroke patients: a systematic review and meta-analysis

Wearable Single‐Electrode Capacitive Sensor with Large Penetration Depth for Intelligent Deep Tissue and Hemorrhage Monitoring

Online Analysis of Malachite Content in the Beneficiation Process Based on Visible-NIR Spectroscopy and GWO-SVM Algorithm

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