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General General

Hybrid support vector machine optimization model for inversion of tunnel transient electromagnetic method.

In Mathematical biosciences and engineering : MBE

The transient electromagnetic method (TEM) can effectively predict adverse geological conditions, and is widely used in underground engineering fields such as coal mining and tunneling. Accurate evaluation of adverse geological features is a crucial problem that requires urgent solutions. TEM inversion is an essential tool in solving such problems. However, the three-dimensional full-space detection of tunnels and its inversion are not sufficiently developed. Therefore, combining a least-squares support vector machine (LSSVM) with particle swarm optimization (PSO), this paper proposes a tunnel TEM inversion approach. Firstly, the PSO algorithm is adopted to optimize the LSSVM model, thus overcoming the randomness and uncertainty of model parameter selection. An orthogonal test method is adopted to optimize the initial parameter combination of the PSO algorithm, which further improves the accuracy of our PSO-LSSVM model. Numerical simulations are conducted to generate 125 sets of original data. The optimized PSO-LSSVM model is then used to predict certain values of the original data. Finally, the optimization model is compared with conventional machine learning methods, and the results show that the randomness of the initial parameters of the PSO algorithm has been reduced and the optimization effect has been improved. The optimized PSO algorithm further improves the stability and accuracy of the generalization ability of the model. Through a comparison of different machine learning methods and laboratory model tests, it is verified that the optimized PSO-LSSVM model proposed in this paper is an effective technique for tunnel TEM detection inversion.

Liang Xiao, Qi Tai Yue, Jin Zhi Yi, Qian Wang Ping


** hybrid support vector machine , inversion method , particle swarm optimization , transient electromagnetic method **

General General

An improved spotted hyena optimizer for PID parameters in an AVR system.

In Mathematical biosciences and engineering : MBE

In this paper, an improved spotted hyena optimizer (ISHO) with a nonlinear convergence factor is proposed for proportional integral derivative (PID) parameter optimization in an automatic voltage regulator (AVR). In the proposed ISHO, an opposition-based learning strategy is used to initialize the spotted hyena individual's position in the search space, which strengthens the diversity of individuals in the global searching process. A novel nonlinear update equation for the convergence factor is used to enhance the SHO's exploration and exploitation abilities. The experimental results show that the proposed ISHO algorithm performed better than other algorithms in terms of the solution precision and convergence rate.

Zhou Guo, Li Jie, Tang Zhong Hua, Luo Qi Fang, Zhou Yong Quan


** PID parameter optimization , metaheuristic , nonlinear convergence factor , opposition-based learning , spotted hyena optimizer **

Radiology Radiology

The Value of Quantitative Musculoskeletal Imaging.

In Seminars in musculoskeletal radiology

Musculoskeletal imaging is mainly based on the subjective and qualitative analysis of imaging examinations. However, integration of quantitative assessment of imaging data could increase the value of imaging in both research and clinical practice. Some imaging modalities, such as perfusion magnetic resonance imaging (MRI), diffusion MRI, or T2 mapping, are intrinsically quantitative. But conventional morphological imaging can also be analyzed through the quantification of various parameters. The quantitative data retrieved from imaging examinations can serve as biomarkers and be used to support diagnosis, determine patient prognosis, or monitor therapy.We focus on the value, or clinical utility, of quantitative imaging in the musculoskeletal field. There is currently a trend to move from volume- to value-based payments. This review contains definitions and examines the role that quantitative imaging may play in the implementation of value-based health care. The influence of artificial intelligence on the value of quantitative musculoskeletal imaging is also discussed.

Visser Jacob J, Goergen Stacy K, Klein Stefan, Noguerol Teodoro Martín, Pickhardt Perry J, Fayad Laura M, Omoumi Patrick


Radiology Radiology

Improving Quantitative Magnetic Resonance Imaging Using Deep Learning.

In Seminars in musculoskeletal radiology

Deep learning methods have shown promising results for accelerating quantitative musculoskeletal (MSK) magnetic resonance imaging (MRI) for T2 and T1ρ relaxometry. These methods have been shown to improve musculoskeletal tissue segmentation on parametric maps, allowing efficient and accurate T2 and T1ρ relaxometry analysis for monitoring and predicting MSK diseases. Deep learning methods have shown promising results for disease detection on quantitative MRI with diagnostic performance superior to conventional machine-learning methods for identifying knee osteoarthritis.

Liu Fang


General General

Patient-adaptable intracranial pressure morphology analysis using a probabilistic model-based approach.

In Physiological measurement ; h5-index 36.0

OBJECTIVE : We present a framework for analyzing the intracranial pressure (ICP) morphology. Analyzing ICP signals is challenging due to the non-linear and non-Gaussian characteristics of the signal dynamics, inevitable corruption with noise and artifacts, and variations in the ICP pulse morphology among individuals with different neurological conditions. Existing frameworks make unrealistic assumptions regarding ICP dynamics and are not tuned for individual patients.

APPROACH : We propose a dynamic Bayesian network (DBN) for automated detection of three major ICP pulsatile components. The proposed model captures the non-linear and non-Gaussian dynamics of the ICP morphology and further adapts to a patient as the individual's ICP measurements are received. To make the approach more robust, we leverage evidence reversal and present an inference algorithm to obtain the posterior distribution over the locations of pulsatile components.

RESULTS : We evaluate our approach on a dataset with over 700 hours of recordings from 66 neurological patients, where the pulsatile components have been annotated in prior studies. The algorithm obtains an accuracy of 96.56%, 92.39%, and 94.04% for detecting each pulsatile component on the test set, showing significant improvements over existing approaches.

SIGNIFICANCE : Continuous ICP monitoring is essential in guiding the treatment of neurological conditions such as traumatic brain injuries. An automated approach for ICP morphology analysis takes a step toward enhancing patient care with minimal supervision. Compared to previous methods, our framework offers several advantages. It learns the parameters that model each patient's ICP in an unsupervised manner, resulting in an accurate morphology analysis. The Bayesian model-based framework provides uncertainty estimates and reveals interesting facts about ICP dynamics. The framework can be readily applied to replace existing morphological analysis methods and support the application of ICP pulse morphological features to aid the monitoring of pathophysiological changes of relevance to the care of patients with acute brain injuries.

Rashidinejad Paria, Hu Xiao, Russell Stuart


Artificial Intelligence in Healthcare, Dynamic Bayesian Network, ICP, Model-Based Probabilistic Inference, Particle Filter, Patient Monitoring

General General

Deep Learning-Based Approach for the Diagnosis of Moyamoya Disease.

In Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association

OBJECTIVES : Moyamoya disease is a unique cerebrovascular disorder that is characterized by chronic bilateral stenosis of the internal carotid arteries and by the formation of an abnormal vascular network called moyamoya vessels. In this stury, the authors inspected whether differentiation between patients with moyamoya disease and those with atherosclerotic disease or normal controls might be possible by using deep machine learning technology.

MATERIALS AND METHODS : This study included 84 consecutive patients diagnosed with moyamoya disease at our hospital between April 2009 and July 2016. In each patient, two axial continuous slices of T2-weighed imaging at the level of the basal cistern, basal ganglia, and centrum semiovale were acquired. The image sets were processed by using code written in the programming language Python 3.7. Deep learning with fine tuning developed using VGG16 comprised several layers.

RESULTS : The accuracies of distinguishing between patients with moyamoya disease and those with atherosclerotic disease or controls in the basal cistern, basal ganglia, and centrum semiovale levels were 92.8, 84.8, and 87.8%, respectively.

CONCLUSION : The authors showed excellent results in terms of accuracy of differential diagnosis of moyamoya disease using AI with the conventional T2 weighted images. The authors suggest the possibility of diagnosing moyamoya disease using AI technique and demonstrate the area of interest on which AI focuses while processing magnetic resonance images.

Akiyama Yukinori, Mikami Takeshi, Mikuni Nobuhiro


Artificial intelligence, Deep learning, Diagnostic accuracy, Moyamoya disease