Quantitative analysis of brain herniation from non-contrast CT images using deep learning

Nag, Manas Kumar; Gupta, Abhinav; Hariharasudhan, A.S.; Sadhu, Anup; Das, Abir; Ghosh, Nirmalya

doi:10.1016/j.jneumeth.2020.109033

Cited by 7 publications

(6 citation statements)

References 18 publications

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“…We identified 2 ML studies to automatically measure the extent of midline shift to reduce the human factor. The authors in Nag et al [ 62 ] constructed a U-net model to predict the deformed boundaries between the left and right hemispheres followed by an estimation of midline shift. The authors validated their algorithm with private CT datasets and confirmed that the midline shift could be estimated with an average distance error of 1.29 ± 0.60mm.…”

Section: Resultsmentioning

confidence: 99%

“…•Any hematoma [32,33] •ICH [34][35][36][37] •Normal/abnormal [38][39][40] •ICP level (high/low) [41][42][43] •Hematoma expansion [44] •Any hematoma [45][46][47][48] •ICH [49][50][51][52][53][54][55] •SDH [56,57] •Normal/abnormal [58] •ICH [59] •SDH [60] •Normal/Abnormal [61] Others •Midline delineation [62][63][64] •Cisterns [59] •Midline [59]…”

Section: Measurement Of Midline Shiftmentioning

confidence: 99%

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Automated identification and quantification of traumatic brain injury from CT scans: Are we there yet?

et al. 2022

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Background: The purpose of this study was to conduct a systematic review for understanding the availability and limitations of artificial intelligence (AI) approaches that could automatically identify and quantify computed tomography (CT) findings in traumatic brain injury (TBI). Methods: Systematic review, in accordance with PRISMA 2020 and SPIRIT-AI extension guidelines, with a search of 4 databases (Medline, Embase, IEEE Xplore, and Web of Science) was performed to find AI studies that automated the clinical tasks for identifying and quantifying CT findings of TBI-related abnormalities. Results: A total of 531 unique publications were reviewed, which resulted in 66 articles that met our inclusion criteria. The following components for identification and quantification regarding TBI were covered and automated by existing AI studies: identification of TBI-related abnormalities; classification of intracranial hemorrhage types; slice-, pixel-, and voxel-level localization of hemorrhage; measurement of midline shift; and measurement of hematoma volume. Automated identification of obliterated basal cisterns was not investigated in the existing AI studies. Most of the AI algorithms were based on deep neural networks that were trained on 2- or 3-dimensional CT imaging datasets. Conclusion: We identified several important TBI-related CT findings that can be automatically identified and quantified with AI. A combination of these techniques may provide useful tools to enhance reproducibility of TBI identification and quantification by supporting radiologists and clinicians in their TBI assessments and reducing subjective human factors.

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Section: Resultsmentioning

confidence: 99%

Section: Measurement Of Midline Shiftmentioning

confidence: 99%

Automated identification and quantification of traumatic brain injury from CT scans: Are we there yet?

et al. 2022

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“…Machine learning's automated MLS measurement can minimize observer variability. Both Nag (2021) and Yan (2022) employed CNNs for MLS estimation, achieving commendable accuracies greater than 85% and consistency across different types of intracranial hemorrhages when compared to hand-drawn MLSs by clinicians across a range of MLS values from a 2 mm cutoff value to greater than 10 mm [83,84].…”

Section: Predicting Tbi With Ctmentioning

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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“…The increase of intracranial pressure can be measured through the midline shifts towards the contralateral site, which can be automatically computed through deep learning (DL)-based algorithms to predict the severity and support the clinical decision. Nag et al [ 11 ] used a convolutional neural network (CNN) to predict the deformed left and right hemispheres on non-contrast CT in patients with epidural and intracranial hemorrhage. As expected, according to the Monro-Kellie hypothesis, the midline shift is an entity well correlated with hematoma volume and a similar result could be hypothesized for cerebral herniation caused by cancer [ 12 ].…”

Section: Ai Applications In Oncologic Central Nervous System Emergenciesmentioning

confidence: 99%

Role of artificial intelligence in oncologic emergencies: a narrative review

Fanni

Greco

Rossi

et al. 2023

Exploration of Targeted Anti-Tumor Therapy

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Oncologic emergencies are a wide spectrum of oncologic conditions caused directly by malignancies or their treatment. Oncologic emergencies may be classified according to the underlying physiopathology in metabolic, hematologic, and structural conditions. In the latter, radiologists have a pivotal role, through an accurate diagnosis useful to provide optimal patient care. Structural conditions may involve the central nervous system, thorax, or abdomen, and emergency radiologists have to know the characteristics imaging findings of each one of them. The number of oncologic emergencies is growing due to the increased incidence of malignancies in the general population and also to the improved survival of these patients thanks to the advances in cancer treatment. Artificial intelligence (AI) could be a solution to assist emergency radiologists with this rapidly increasing workload. To our knowledge, AI applications in the setting of the oncologic emergency are mostly underexplored, probably due to the relatively low number of oncologic emergencies and the difficulty in training algorithms. However, cancer emergencies are defined by the cause and not by a specific pattern of radiological symptoms and signs. Therefore, it can be expected that AI algorithms developed for the detection of these emergencies in the non-oncological field can be transferred to the clinical setting of oncologic emergency. In this review, a craniocaudal approach was followed and central nervous system, thoracic, and abdominal oncologic emergencies have been addressed regarding the AI applications reported in literature. Among the central nervous system emergencies, AI applications have been reported for brain herniation and spinal cord compression. In the thoracic district the addressed emergencies were pulmonary embolism, cardiac tamponade and pneumothorax. Pneumothorax was the most frequently described application for AI, to improve sensibility and to reduce the time-to-diagnosis. Finally, regarding abdominal emergencies, AI applications for abdominal hemorrhage, intestinal obstruction, intestinal perforation, and intestinal intussusception have been described.

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Quantitative analysis of brain herniation from non-contrast CT images using deep learning

Cited by 7 publications

References 18 publications

Automated identification and quantification of traumatic brain injury from CT scans: Are we there yet?

Automated identification and quantification of traumatic brain injury from CT scans: Are we there yet?

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

Role of artificial intelligence in oncologic emergencies: a narrative review

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