A deep learning algorithm for automatic detection and classification of acute intracranial hemorrhages in head CT scans

Wang, Xiyue; Shen, Tao; Yang, Sen; Lan, Jun; Xu, Yanming; Wang, Minghui; Zhang, Jing; Han, Xiao

doi:10.1016/j.nicl.2021.102785

Cited by 74 publications

(61 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… 32 Recent approaches try to integrate medical imaging information to predict ICP 33 , 34 or intracranial pathologies. 35 On the other hand the ICP signal itself can be used to predict ventriculitis with machine learning. 36 Recurrent machine learning in contrast to other machine learning methods, is better suited to learn time-dependent information since the output is fed back into the input of the model during training.…”

Section: Introductionmentioning

confidence: 99%

A recurrent machine learning model predicts intracranial hypertension in neurointensive care patients

Schweingruber

Mader

Wiehe

et al. 2022

Brain

View full text Add to dashboard Cite

The evolution of intracranial pressure (ICP) of critically ill patients admitted to a neurointensive care unit (ICU) is difficult to predict. Besides the underlying disease and compromised intracranial space, ICP is affected by a multitude of factors, many of which are monitored on the ICU, but the complexity of the resulting patterns limits their clinical use. This paves the way for new machine learning (ML) techniques to assist clinical management of patients undergoing invasive ICP monitoring independent of the underlying disease. An institutional cohort (ICP-ICU) of patients with invasive ICP monitoring (n = 1346) was used to train recurrent ML models to predict the occurrence of ICP increases of ≥ 22mmHg over a long (> 2 hours) time period in the upcoming hours. External validation was performed on patients undergoing invasive ICP measurement in two publicly available datasets (Medical Information Mart for Intensive Care (MIMIC, n = 998) and eICU Collaborative Research Database (eICU, n = 1634)). Different distances (1h-24 h) between prediction time point and upcoming critical phase were evaluated, demonstrating a decrease in performance but still robust AUC-ROC with larger distances (24 h AUC-ROC: ICP-ICU 0.826 ± 0.0071, MIMIC 0.836 ± 0.0063, eICU 0.779 ± 0.0046, 1 h AUC-ROC: ICP-ICU 0.982 ± 0.0008, MIMIC 0.965 ± 0.0010, eICU 0.941 ± 0.0025). The model operates on sparse hourly data and is stable in handling variable input lengths and missingness through its nature of recurrence and internal memory. Calculation of gradient-based feature importance revealed individual underlying decisions for our Long Short Time Memory (LSTM) based model and thereby provided improved clinical interpretability. Recurrent ML models have the potential to be an effective tool for the prediction of ICP increases with high translational potential.

show abstract

Section: Introductionmentioning

confidence: 99%

A recurrent machine learning model predicts intracranial hypertension in neurointensive care patients

Schweingruber

Mader

Wiehe

et al. 2022

Brain

View full text Add to dashboard Cite

show abstract

“…Tables 1–7 shows the simulation results obtained from the proposed acute ICH detection and classification model for training–testing % are illustrated below in a tabular form. Here, the proposed ICH‐DC‐ResNet‐DenseNet‐IRF method compared with existing deep learning algorithm for automatic detection with classification of acute intracranial hemorrhages in head CT scans (ICH‐DC‐2D‐CNN), 33 fusion‐based deep learning along nature‐inspired algorithm for the diagnosis of intracerebral hemorrhage (ICH‐DC‐FSVM) 34 and detection of intracranial hemorrhage on CT scan images utilizing convolutional neural network (ICH‐DC‐CNN), 35 ICH‐DC‐CNN‐FCNs 31 methods, respectively.…”

Section: Results With Discussionmentioning

confidence: 99%

“…Then the sub types of ICH are intracerebral hemorrhage, intraventricular hemorrhage, epidural hemorrhage, subdural hemorrhage, subarachnoid hemorrhage is classified by using IRF classifier with high accuracy. automatic detection with classification of acute intracranial hemorrhages in head CT scans (ICH-DC-2D-CNN), 33 fusion-based deep learning along nature-inspired algorithm for the diagnosis of intracerebral hemorrhage (ICH-DC-FSVM) 34 and detection of intracranial hemorrhage on CT scan images utilizing convolutional neural network (ICH-DC-CNN), 35 ICH-DC-CNN-FCNs 31 methods, respectively. proposed ICH-DC-ResNet-DenseNet-IRF method provides 17.87%, 32.86%, 32.86%, and 34.97% higher recall than the existing methods.…”

Section: Comparison Of Performance Analysis Utilizing Various Models ...mentioning

confidence: 99%

Detection and categorization of acute intracranial hemorrhage subtypes using a multilayer DenseNet‐ResNet architecture with improved random forest classifier

Jenefer¹,

Senathipathi²,

Aarthi³

et al. 2022

Concurrency and Computation

View full text Add to dashboard Cite

Summary In this article, the detection and categorization of acute intracranial hemorrhage (ICH) subtypes using a multilayer DenseNet‐ResNet architecture with improved random forest classifier (IRF) is proposed to detect the subtypes of intracerebral hemorrhage with high accuracy and less computational time. Here, the brain CT images are taken from the physionet repository publicly dataset. Then the images are preprocessed to eliminate the unwanted noises. After that, the image features are extracted by using multilayer densely connected convolutional network (DenseNet) combined with residual network (ResNet) architecture with multiple convolutional layers. The subtypes are epidural hemorrhage (EDH), subarachnoid hemorrhage (SAH), intraparenchymal hemorrhage (IPH), subdural hemorrhage (SDH), intraventricular hemorrhage (IVH) are classified by using an IRF classifier with high accuracy. The simulation process is carried out in MATLAB site. The proposed multilayer‐DenseNet‐ResNet‐IRF attains higher accuracy 23.44%, 31.93%, 42.83%, 41.9% is compared with the existing methods, such as deep learning algorithm for automatic detection and classification of acute intracranial hemorrhages in head CT scans (ICH‐DC‐2D‐CNN), fusion‐based deep learning along nature‐inspired algorithm for the diagnosis of intracerebral hemorrhage (ICH‐DC‐FSVM), and detection of intracranial hemorrhage on CT scan images using convolutional neural network (ICH‐DC‐CNN) and double fully convolutional networks (FCNs), respectively.

show abstract

“…Previous studies [19] , [20] have attempted automatic detection of blood pool from ultrasound images for emergency diagnosis in the event of blunt traumatic injury using a machine learning framework. There have been several studies [40] , [41] attempting to detect and localize cranial hemorrhages using deep learning from CT images after traumatic injury. Moreover, previous studies using deep learning or machine learning-based method for hemorrhage detection used supervised techniques, while our method involves unsupervised identification of hemorrhage.…”

Section: Discussionmentioning

confidence: 99%

Automatic Hemorrhage Detection From Color Doppler Ultrasound Using a Generative Adversarial Network (GAN)-Based Anomaly Detection Method

Mitra

Qiu

MacDonald

et al. 2022

IEEE J. Transl. Eng. Health Med.

View full text Add to dashboard Cite

Hemorrhage control has been identified as a priority focus area both for civilian and military populations in the United States because exsanguination is the most common cause of preventable death in hemorrhagic injury. Non-compressible torso hemorrhage (NCTH) has high mortality rate and there are currently no broadly available therapies for NCTH outside of a surgical room environment. Novel therapies, which include High Intensity Focused Ultrasound (HIFU) have emerged as promising methods for hemorrhage control as they can non-invasively cauterize bleeding tissue deep within the body without injuring uninvolved regions. A major challenge in the application of HIFU with color Doppler US guidance is the interpretation and optimization of the blood flow images in real-time to identify the hemorrhagic focus. Today, this task requires an expert sonographer, limiting the utility of this therapy in non-clinical environments. In this work, we investigated the feasibility of an automated hemorrhage detection method using a Generative Adversarial Network (GAN) for anomaly detection that learns a manifold of normal blood flow variability and subsequently identifies anomalous flow patterns that fall outside the learned manifold. As an initial feasibility study, we collected ultrasound color Doppler images of femoral arteries in an animal model of vascular injury (N=11 pigs). Velocity information of the blood flow were extracted from the color Doppler images that were used for training and testing the anomaly detection network. Normotensive images from 8 pigs were used for training, and testing was performed on normotensive, immediately after injury, 10 minutes post-injury and 30 minutes post-injury images from 3 other pigs. The residual images or the reconstructed error maps show promise in detecting hemorrhages with an AUC of 0.90, 0.87, 0.62 immediately, 10 minutes post-injury and 30 minutes post-injury respectively with an overall AUC of 0.83.

show abstract

A deep learning algorithm for automatic detection and classification of acute intracranial hemorrhages in head CT scans

Cited by 74 publications

References 41 publications

A recurrent machine learning model predicts intracranial hypertension in neurointensive care patients

A recurrent machine learning model predicts intracranial hypertension in neurointensive care patients

Detection and categorization of acute intracranial hemorrhage subtypes using a multilayer DenseNet‐ResNet architecture with improved random forest classifier

Automatic Hemorrhage Detection From Color Doppler Ultrasound Using a Generative Adversarial Network (GAN)-Based Anomaly Detection Method

Contact Info

Product

Resources

About