Automated Intracranial Pressure Prediction Using Multiple Features Sources

Qi, Xuguang; Belle, Ashwin; Shandilya, Sharad; Najarian, Kayvan; Hargraves, Rosalyn Hobson; Cockrell, Charles

doi:10.1109/icisa.2013.6579432

Cited by 6 publications

(7 citation statements)

References 15 publications

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“…Aghazadeh et al [ 127 ] applied the Morlet wavelet transform to acquire textural features, and used a genetic algorithm with KNN as optimized feature selectors to label ICP as mild or severe. Qi et al [ 128 ] developed another machine learning technique that utilized multiple features along with demographic information to categorize ICP. In another recent study by Chen et al [ 129 ], a hybrid approach that automatically estimates MLS initially to perform ICP classification was reported.…”

Section: Generics Of Computer Aided Diagnosismentioning

confidence: 99%

Automated Detection and Screening of Traumatic Brain Injury (TBI) Using Computed Tomography Images: A Comprehensive Review and Future Perspectives

Vidhya

Gudigar

Raghavendra

et al. 2021

IJERPH

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Traumatic brain injury (TBI) occurs due to the disruption in the normal functioning of the brain by sudden external forces. The primary and secondary injuries due to TBI include intracranial hematoma (ICH), raised intracranial pressure (ICP), and midline shift (MLS), which can result in significant lifetime disabilities and death. Hence, early diagnosis of TBI is crucial to improve patient outcome. Computed tomography (CT) is the preferred modality of choice to assess the severity of TBI. However, manual visualization and inspection of hematoma and its complications from CT scans is a highly operator-dependent and time-consuming task, which can lead to an inappropriate or delayed prognosis. The development of computer aided diagnosis (CAD) systems could be helpful for accurate, early management of TBI. In this paper, a systematic review of prevailing CAD systems for the detection of hematoma, raised ICP, and MLS in non-contrast axial CT brain images is presented. We also suggest future research to enhance the performance of CAD for early and accurate TBI diagnosis.

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Section: Generics Of Computer Aided Diagnosismentioning

confidence: 99%

Automated Detection and Screening of Traumatic Brain Injury (TBI) Using Computed Tomography Images: A Comprehensive Review and Future Perspectives

Vidhya

Gudigar

Raghavendra

et al. 2021

IJERPH

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show abstract

“…Machine learning (ML) techniques have become a powerful tool to make predictions or perform classifications for medical diagnoses [12] . Three previous studies proposed a noninvasive methodology on early intracranial hypertension detections based on ML techniques using ICP waveform analysis or image features (including midline shift, intracranial cavities, and ventricle size) extracted from medical imaging modalities (e.g., CT or MRI) [13] – [15] with varying degrees of prediction accuracy. The classification accuracy in the first study [13] was about 74% in ICP levels predicted with multiple features sources.…”

Section: Introductionmentioning

confidence: 99%

“…Three previous studies proposed a noninvasive methodology on early intracranial hypertension detections based on ML techniques using ICP waveform analysis or image features (including midline shift, intracranial cavities, and ventricle size) extracted from medical imaging modalities (e.g., CT or MRI) [13] – [15] with varying degrees of prediction accuracy. The classification accuracy in the first study [13] was about 74% in ICP levels predicted with multiple features sources. The second study [14] reported an accuracy of 91% for predicting increased ICP classification for children.…”

Section: Introductionmentioning

confidence: 99%

“…Another study based on noninvasive ICP data with thirteen patients reported a root-mean-square error (RMSE) of 3.7 mmHg with their ICP estimation [15] . All these three studies [13] – [15] used noninvasive methods to achieve ICP estimates. The first two methods [13] , [14] predicted the ICP based on CT scans.…”

Section: Introductionmentioning

confidence: 99%

“…All these three studies [13] – [15] used noninvasive methods to achieve ICP estimates. The first two methods [13] , [14] predicted the ICP based on CT scans. The CT scan can only be executed on the patients a limited number of times due to the risks of ionizing radiation exposure.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning-Based Continuous Intracranial Pressure Prediction for Traumatic Injury Patients

Balasubramanian

et al. 2022

IEEE J. Transl. Eng. Health Med.

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Structured Abstract—Objective : Abnormal elevation of intracranial pressure (ICP) can cause dangerous or even fatal outcomes. The early detection of high intracranial pressure events can be crucial in saving lives in an intensive care unit (ICU). Despite many applications of machine learning (ML) techniques related to clinical diagnosis, ML applications for continuous ICP detection or short-term predictions have been rarely reported. This study proposes an efficient method of applying an artificial recurrent neural network on the early prediction of ICP evaluation continuously for TBI patients. Methods : After ICP data preprocessing, the learning model is generated for thirteen patients to continuously predict the ICP signal occurrence and classify events for the upcoming 10 minutes by inputting the previous 20-minutes of the ICP signal. Results : As the overall model performance, the average accuracy is 94.62%, the average sensitivity is 74.91%, the average specificity is 94.83%, and the average root mean square error is approximately 2.18 mmHg. Conclusion : This research addresses a significant clinical problem with the management of traumatic brain injury patients. The machine learning model data enables early prediction of ICP continuously in a real-time fashion, which is crucial for appropriate clinical interventions. The results show that our machine learning-based model has high adaptive performance, accuracy, and efficiency.

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Intracranial Pressure Prediction with a Recurrent Neural Network Model

Balasubramanian

et al. 2021

2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART)

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Automated Intracranial Pressure Prediction Using Multiple Features Sources

Cited by 6 publications

References 15 publications

Automated Detection and Screening of Traumatic Brain Injury (TBI) Using Computed Tomography Images: A Comprehensive Review and Future Perspectives

Automated Detection and Screening of Traumatic Brain Injury (TBI) Using Computed Tomography Images: A Comprehensive Review and Future Perspectives

Machine Learning-Based Continuous Intracranial Pressure Prediction for Traumatic Injury Patients

Intracranial Pressure Prediction with a Recurrent Neural Network Model

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