Deep learning algorithms for brain disease detection with magnetic induction tomography

Simulation of dynamic monitoring for intracerebral hemorrhage based on magnetic induction phase shift technology

Intracerebral hemorrhage (ICH) is a common and severe brain disease associated with high mortality and morbidity. Accurate measurement of the ICH area is an essential indicator for doctors to determine whether a surgical operation is necessary. However, although currently used clinical detection methods, such as computed tomography (CT) and magnetic resonance imaging (MRI), provide high-quality images, they may have limitations such as high costs, large equipment size, and radiation exposure to the human body in the case of CT. It makes long-term bedside monitoring infeasible. This paper presents a dynamic monitoring method for ICH areas based on magnetic induction. This study investigates the influence of the bleeding area and the position of ICH on the phase difference at the detection point near the area to be measured. The study applies a neural network algorithm to predict the bleeding area using the phase difference data received by the detection coil as the network input and the bleeding area as the network output. The relative error between the predicted and actual values of the neural network is calculated, and the error of each group of data is less than 4%, which confirms the feasibility of this method for detecting and even trend monitoring of the ICH area.

Deep Learning Aided Neuroimaging and Brain Regulation

Currently, deep learning aided medical imaging is becoming the hot spot of AI frontier application and the future development trend of precision neuroscience. This review aimed to render comprehensive and informative insights into the recent progress of deep learning and its applications in medical imaging for brain monitoring and regulation. The article starts by providing an overview of the current methods for brain imaging, highlighting their limitations and introducing the potential benefits of using deep learning techniques to overcome these limitations. Then, we further delve into the details of deep learning, explaining the basic concepts and providing examples of how it can be used in medical imaging. One of the key strengths is its thorough discussion of the different types of deep learning models that can be used in medical imaging including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial network (GAN) assisted magnetic resonance imaging (MRI), positron emission tomography (PET)/computed tomography (CT), electroencephalography (EEG)/magnetoencephalography (MEG), optical imaging, and other imaging modalities. Overall, our review on deep learning aided medical imaging for brain monitoring and regulation provides a referrable glance for the intersection of deep learning aided neuroimaging and brain regulation.

Artificial Intelligence-Enabled Medical Analysis for Intracranial Cerebral Hemorrhage Detection and Classification

Intracranial hemorrhage (ICH) becomes a crucial healthcare emergency, which requires earlier detection and accurate assessment. Owing to the increased death rate (around 40%), the earlier recognition and classification of disease using computed tomography (CT) images are necessary to ensure a favourable prediction and restrain the existence of neurologic deficits. Since the manual diagnosis approach is time-consuming, automated ICH detection and classification models using artificial intelligence (AI) models are required. With this motivation, this study introduces an AI-enabled medical analysis tool for ICH detection and classification (AIMA-ICHDC) using CT images. The proposed AIMA-ICHDC technique aims at identifying the presence of ICH and identifying the different grades. In addition, the AIMA-ICHDC technique involves the design of glowworm swarm optimization with fuzzy entropy clustering (GSO-FEC) technique for the segmentation process. Besides, the VGG-19 model was executed for generating a collection of feature vectors and the optimal mixed-kernel-based extreme learning machine (OMKELM) model is utilized as a classifier. To optimally select the weight parameter of the MKELM technique, the coyote optimization algorithm (COA) was utilized. A wide range of simulation analyses are carried out under varying aspects. As part of the AIMA-ICHDC method, ICH can be detected and graded using a single sample. For segmentation, the AIMA-ICHDC technique uses the GSO-FEC method, which is the design of glowworm swarm optimization (GSO). The comparative outcomes highlighted the betterment of the AIMA-ICHDC technique compared to the recent state-of-the-art ICH classification approaches in terms of several measures.

Can Artificial Intelligence Be Applied to Diagnose Intracerebral Hemorrhage under the Background of the Fourth Industrial Revolution? A Novel Systemic Review and Meta-Analysis

Aim

We intended to provide the clinical evidence that artificial intelligence (AI) could be used to assist doctors in the diagnosis of intracerebral hemorrhage (ICH).

Methods

Studies published in 2021 were identified after the literature search of PubMed, Embase, and Cochrane. Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) was used to perform the quality assessment of studies. Data extraction of diagnosis effect included accuracy (ACC), sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV), area under curve (AUC), and Dice scores (Dices). The pooled effect with its 95% confidence interval (95%CI) was calculated by the random effects model. I-Square (I 2) was used to test heterogeneity. To check the stability of the overall results, sensitivity analysis was conducted by recalculating the pooled effect of the remaining studies after omitting the study with the highest quality or the random effects model was switched to the fixed effects model. Funnel plot was used to evaluate publication bias. To reduce heterogeneity, recalculating the pooled effect of the remaining studies after omitting the study with the lowest quality or perform subgroup analysis.

Results

Twenty-five diagnostic tests of ICH via AI and doctors with overall high quality were included. Pooled ACC, SEN, SPE, PPV, NPV, AUC, and Dices were 0.88 (0.83∼0.93), 0.85 (0.81∼0.89), 0.90 (0.88∼0.92), 0.80 (0.75∼0.85), 0.93 (0.91∼0.95), 0.84 (0.80∼0.89), and 0.90 (0.85∼0.95), respectively. There was no publication bias. All of results were stable as revealed by sensitivity analysis and were accordant as outcomes via subgroups analysis.

Conclusion

Under the background of the fourth industrial revolution, AI might be an effective and efficient tool to assist doctors in the clinical diagnosis of ICH.

Rapid diagnosis and continuous monitoring of intracerebral hemorrhage with magnetic induction tomography based on stacked autoencoder

Magnetic induction tomography (MIT) is a promising approach in rapid diagnosis and continuous monitoring of cerebral hemorrhage. A new algorithm for the reconstruction of intracerebral hemorrhage with MIT, including the location and volume of hemorrhage, is proposed in this study. First, 2D magnetic resonance imaging and computed tomography images of patients with cerebral hemorrhage were used for the development of simulation models. The Stacked Autoencoder (SAE) network was then used to predict the location and volume of hemorrhage by conductivity reconstruction. Finally, the one-dimensional quantitative monitoring index is proposed as an auxiliary diagnostic indicator for assessment of real-time intracranial electrical characteristics. The 2D simulation results showed that the SAE was able to quickly image the location and volume of the hemorrhages. Compared with the back-projection algorithm, the prediction speed of each frame was improved 15-fold, and the accuracy improved by 90.53%. The extracted one-dimensional quantitative monitoring indicators can describe the bleeding status. The diagnostic accuracy and the imaging speed of cerebral hemorrhage were both improved by constructing a realistic head section model and using the proposed SAE network. This research provides a new alternative for dynamic monitoring of hemorrhages and shows the potential advantages of MIT in noninvasive detection.

Application of a Generative Adversarial Network in Image Reconstruction of Magnetic Induction Tomography

Image reconstruction of Magnetic induction tomography (MIT) is an ill-posed problem. The non-linear characteristics lead many difficulties to its solution. In this paper, a method based on a Generative Adversarial Network (GAN) is presented to tackle these barriers. Firstly, the principle of MIT is analyzed. Then the process for finding the global optimum of conductivity distribution is described as a training process, and the GAN model is proposed. Finally, the image was reconstructed by a part of the model (the generator). All datasets are obtained from an eight-channel MIT model by COMSOL Multiphysics software. The voltage measurement samples are used as input to the trained network, and its output is an estimate for image reconstruction of the internal conductivity distribution. The results based on the proposed model and the traditional algorithms were compared, which have shown that average root mean squared error of reconstruction results obtained by the proposed method is 0.090, and the average correlation coefficient with original images is 0.940, better than corresponding indicators of BPNN and Tikhonov regularization algorithms. Accordingly, the GAN algorithm was able to fit the non-linear relationship between input and output, and visual images also show that it solved the usual problems of artifact in traditional algorithm and hot pixels in L2 regularization, which is of great significance for other ill-posed or non-linear problems.

A convolutional neural network algorithm for breast tumor detection with magnetic detection electrical impedance tomography

Breast cancer is a malignant tumor disease for which early detection, diagnosis, and treatment are of paramount significance in prolonging the life of patients. Magnetic Detection Electrical Impedance Tomography (MDEIT) based on the Convolutional Neural Network (CNN), which aims to realize non-invasive, high resolution detection of breast tumors, is proposed. First, the MDEIT forward problem of the coronal and horizontal planes of the breast was simulated and solved using the Finite Element Method to obtain sample datasets of different lesions. Then, the CNN was built and trained to predict the conductivity distribution in different orientations of the breast model. Finally, noise and phantom experiments were performed in order to assess the anti-noise performance of the CNN algorithm and its feasibility of detecting breast tumors in practical applications. The simulation results showed that the reconstruction relative error with the CNN algorithm can be reduced to 10%, in comparison with the truncated singular value decomposition algorithm and back propagation algorithm. The CNN algorithm had better stability in the anti-noise performance test. When the noise of 60 dB was added, the shape of the breast tumor could still be restored by the CNN algorithm. The phantom experimental results showed that through the CNN based reconstruction algorithm, the reconstruction conductivity distribution image was legible and the position of the breast tumor could be determined. It is reasonable to conclude that the MDEIT reconstruction method proposed in this study has practical importance for the early and non-invasive detection of breast tumors.