Evaluation of Traumatic Subdural Hematoma Volume by Using Image Segmentation Assessment Based on Deep Learning

Chen, Dan; Bian, Lin; He, Hao-Yuan; Li, Ya-Dong; Ma, Chao; Mao, Liangang

doi:10.1155/2022/3830245

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…Farzaneh (2020) utilized a random forest model to detect and classify the severity of acute SDHs, achieving a sensitivity of 0.99 and specificity of 0.92 [88]. Chen (2022) employed a CNN for volumetric assessment, with the results closely mirroring manual segmentation with an AUROC of 0.83 [89]. Another CNN model designed for comprehensive SDH evaluation, including thickness, volume, and midline shift, showcased a sensitivity of 91.4% and specificity of 96.4% [90].…”

Section: Detecting and Quantifying Subdural Hematomas With Machine Le...mentioning

confidence: 99%

Machine Learning in Neuroimaging of Traumatic Brain Injury: Current Landscape, Research Gaps, and Future Directions

Pierre,

Turetsky,

Raviprasad

et al. 2024

Trauma Care

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In this narrative review, we explore the evolving role of machine learning (ML) in the diagnosis, prognosis, and clinical management of traumatic brain injury (TBI). The increasing prevalence of TBI necessitates advanced techniques for timely and accurate diagnosis, and ML offers promising tools to meet this challenge. Current research predominantly focuses on integrating clinical data, patient demographics, lab results, and imaging findings, but there remains a gap in fully harnessing the potential of image features. While advancements have been made in areas such as subdural hematoma segmentation and prognosis prediction, the translation of these techniques into clinical practice is still in its infancy. This is further compounded by challenges related to data privacy, clinician trust, and the interoperability of various health systems. Despite these hurdles, FDA-approved ML applications for TBI and their subsequent promising results underscore the potential of ML in revolutionizing TBI care. This review concludes by emphasizing the importance of bridging the gap between theoretical research and real-world clinical application and the necessity of addressing the ethical and privacy implications of integrating ML into healthcare.

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Section: Detecting and Quantifying Subdural Hematomas With Machine Le...mentioning

confidence: 99%

Machine Learning in Neuroimaging of Traumatic Brain Injury: Current Landscape, Research Gaps, and Future Directions

Pierre,

Turetsky,

Raviprasad

et al. 2024

Trauma Care

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Clinical Use of Hematoma Volume Based On Automated Segmentation of Chronic Subdural Hematoma Using 3D U-Net

Inomata,

Nakaya,

Matsuhiro

et al. 2024

Clin Neuroradiol

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Role of artificial intelligence and machine learning in the diagnosis of cerebrovascular disease

Gilotra,

Swarna,

Mani

et al. 2023

Front. Hum. Neurosci.

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IntroductionCerebrovascular diseases are known to cause significant morbidity and mortality to the general population. In patients with cerebrovascular disease, prompt clinical evaluation and radiographic interpretation are both essential in optimizing clinical management and in triaging patients for critical and potentially life-saving neurosurgical interventions. With recent advancements in the domains of artificial intelligence (AI) and machine learning (ML), many AI and ML algorithms have been developed to further optimize the diagnosis and subsequent management of cerebrovascular disease. Despite such advances, further studies are needed to substantively evaluate both the diagnostic accuracy and feasibility of these techniques for their application in clinical practice. This review aims to analyze the current use of AI and MI algorithms in the diagnosis of, and clinical decision making for cerebrovascular disease, and to discuss both the feasibility and future applications of utilizing such algorithms.MethodsWe review the use of AI and ML algorithms to assist clinicians in the diagnosis and management of ischemic stroke, hemorrhagic stroke, intracranial aneurysms, and arteriovenous malformations (AVMs). After identifying the most widely used algorithms, we provide a detailed analysis of the accuracy and effectiveness of these algorithms in practice.ResultsThe incorporation of AI and ML algorithms for cerebrovascular patients has demonstrated improvements in time to detection of intracranial pathologies such as intracerebral hemorrhage (ICH) and infarcts. For ischemic and hemorrhagic strokes, commercial AI software platforms such as RapidAI and Viz.AI have bene implemented into routine clinical practice at many stroke centers to expedite the detection of infarcts and ICH, respectively. Such algorithms and neural networks have also been analyzed for use in prognostication for such cerebrovascular pathologies. These include predicting outcomes for ischemic stroke patients, hematoma expansion, risk of aneurysm rupture, bleeding of AVMs, and in predicting outcomes following interventions such as risk of occlusion for various endovascular devices. Preliminary analyses have yielded promising sensitivities when AI and ML are used in concert with imaging modalities and a multidisciplinary team of health care providers.ConclusionThe implementation of AI and ML algorithms to supplement clinical practice has conferred a high degree of accuracy, efficiency, and expedited detection in the clinical and radiographic evaluation and management of ischemic and hemorrhagic strokes, AVMs, and aneurysms. Such algorithms have been explored for further purposes of prognostication for these conditions, with promising preliminary results. Further studies should evaluate the longitudinal implementation of such techniques into hospital networks and residency programs to supplement clinical practice, and the extent to which these techniques improve patient care and clinical outcomes in the long-term.

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Evaluation of Traumatic Subdural Hematoma Volume by Using Image Segmentation Assessment Based on Deep Learning

Cited by 3 publications

References 29 publications

Machine Learning in Neuroimaging of Traumatic Brain Injury: Current Landscape, Research Gaps, and Future Directions

Machine Learning in Neuroimaging of Traumatic Brain Injury: Current Landscape, Research Gaps, and Future Directions

Clinical Use of Hematoma Volume Based On Automated Segmentation of Chronic Subdural Hematoma Using 3D U-Net

Role of artificial intelligence and machine learning in the diagnosis of cerebrovascular disease

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