scholarly journals Novel and accurate non-linear index for the automated detection of haemorrhagic brain stroke using CT images

2021 ◽  
Author(s):  
U. Raghavendra ◽  
The-Hanh Pham ◽  
Anjan Gudigar ◽  
V. Vidhya ◽  
B. Nageswara Rao ◽  
...  
Keyword(s):  
Blood Flow ◽  
Intracerebral Haemorrhage ◽  
Medical Condition ◽  
Early Stage ◽  
Probabilistic Neural Network ◽  
Ct Images ◽  
Statistical Validation ◽  
Soft Computing Techniques ◽  
Non Linear ◽  
Brain Stroke

AbstractBrain stroke is an emergency medical condition which occurs mainly due to insufficient blood flow to the brain. It results in permanent cellular-level damage. There are two main types of brain stroke, ischemic and hemorrhagic. Ischemic brain stroke is caused by a lack of blood flow, and the haemorrhagic form is due to internal bleeding. The affected part of brain will not function properly after this attack. Hence, early detection is important for more efficacious treatment. Computer-aided diagnosis is a type of non-invasive diagnostic tool which can help in detecting life-threatening disease in its early stage by utilizing image processing and soft computing techniques. In this paper, we have developed one such model to assess intracerebral haemorrhage by employing non-linear features combined with a probabilistic neural network classifier and computed tomography (CT) images. Our model achieved a maximum accuracy of 97.37% in discerning normal versus haemorrhagic subjects. An intracerebral haemorrhage index is also developed using only three significant features. The clinical and statistical validation of the model confirms its suitability in providing for improved treatment planning and in making strategic decisions.

Connection of discontinuous segments in early functional recovery from thoracic ossification of the posterior longitudinal ligament treated with posterior instrumented surgery

2020 ◽  
Vol 32 (2) ◽  
pp. 200-206
Author(s):  
Kei Ando ◽  
Kazuyoshi Kobayashi ◽  
Masaaki Machino ◽  
Kyotaro Ota ◽  
Satoshi Tanaka ◽  
...  
Keyword(s):  
Early Stage ◽  
Morphological Changes ◽  
Japanese Orthopaedic Association ◽  
Multislice Ct ◽  
Posterior Longitudinal Ligament ◽  
Ct Images ◽  
Rigid Fixation ◽  
Disc Space ◽  
Fusion Surgery ◽  
Estimated Blood Loss

OBJECTIVEThe objective of this study was to investigate the relationship between morphological changes in thoracic ossification of the posterior longitudinal ligament (T-OPLL) and postoperative neurological recovery after thoracic posterior fusion surgery. Changes of OPLL morphology and postoperative recovery in cases with T-OPLL have not been examined.METHODSIn this prospective study, the authors evaluated data from 44 patients (23 male and 21 female) who underwent posterior decompression and fusion surgery with instrumentation for the treatment of T-OPLL at our hospital. The patients’ mean age at surgery was 50.7 years (range 38–68 years). The minimum duration of follow-up was 2 years. The location of thoracic ossification of the ligamentum flavum (T-OLF), T-OLF at the OPLL level, OPLL morphology, fusion range, estimated blood loss, operative time, pre- and postoperative Japanese Orthopaedic Association (JOA) scores, and JOA recovery rate were investigated. Reconstructed sagittal multislice CT images were obtained before and at 3 and 6 months and 1 and 2 years after surgery. The basic fusion area was 3 vertebrae above and below the OPLL lesion. All parameters were compared between patients with and without continuity across the disc space at the OPLL at 3 and 6 months after surgery.RESULTSThe preoperative morphology of OPLL was discontinuous across the disc space between the rostral and caudal ossification regions on sagittal CT images in all but one of the patients. Postoperatively, these segments became continuous in 42 patients (97.7%; occurring by 6.6 months on average) without progression of OPLL thickness. Patients with continuity at 3 months had significantly lower rates of diabetes mellitus (p < 0.05) and motor palsy in the lower extremities (p < 0.01). The group with continuity also had significantly higher mean postoperative JOA scores at 3 (p < 0.01) and 6 (p < 0.05) months and mean JOA recovery rates at 3 and 6 months (both p < 0.01) after surgery.CONCLUSIONSPreoperatively, discontinuity of rostral and caudal ossified lesions was found on CT in all patients but one of this group of 44 patients who needed surgery for T-OPLL. Rigid fixation with instrumentation may have allowed these segments to connect at the OPLL. Such OPLL continuity at an early stage after surgery may accelerate spinal cord recovery.

scholarly journals Laser Doppler blood flowmeter as a useful instrument for the early detection of lower extremity peripheral arterial disease in hemodialysis patients: an observational study

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Takeo Ishii ◽  
Shizuka Takabe ◽  
Yuki Yanagawa ◽  
Yuko Ohshima ◽  
Yasuhiro Kagawa ◽  
...  
Keyword(s):  
Blood Flow ◽  
Peripheral Arterial Disease ◽  
Early Stage ◽  
Arterial Disease ◽  
Hemodialysis Patients ◽  
Laser Doppler ◽  
Doppler Flowmetry ◽  
Skin Perfusion ◽  
Dorsal Area ◽  
Peripheral Arterial

Abstract Background A simpler method for detecting atherosclerosis obliterans is required in the clinical setting. Laser Doppler flowmetry (LDF) is easy to perform and can accurately detect deterioration in skin perfusion. We performed LDF for hemodialysis patients to determine the correlations between blood flow in the lower limbs and peripheral arterial disease (PAD). Methods This retrospective study included 128 hemodialysis patients. Patients were categorized into the non-PAD group (n = 106) and PAD group (n = 22), 14 early stage PAD patients were included in the PAD group. We conducted LDF for the plantar area and dorsal area of the foot and examined skin perfusion pressure (SPP) during dialysis. Results SPP-Dorsal Area values were 82.1 ± 22.0 mmHg in the non-PAD, and 59.1 ± 20.3 mmHg in PAD group, respectively (p < 0.05). The LDF-Plantar blood flow (Qb) values were 32.7 ± 15.5 mL/min in non-PAD group and 21.5 ± 11.3 mL/min in PAD group (p < 0.001). A total of 21 non-PAD patients underwent LDF before and during dialysis. The LDF-Plantar-Qb values were 36.5 ± 17.6 mL/min before dialysis and 29.6 ± 17.7 mL/min after dialysis (p < 0.05). We adjusted SPP and LDF for PAD using logistic regression, SPP-Dorsal-Area and LDF-P were significantly correlated with PAD (p < 0.05). The receiver-operating characteristic curve analysis indicated cut-off values of 20.0 mL/min for LDF-Plantar-Qb during dialysis. Conclusion LDF is a simple technique for sensitive detection of early-stage PAD. This assessment will help physicians identify early-stage PAD, including Fontaine stage II in clinical practice, thereby allowing prompt treatment.

Modeling Cerebral Blood Flow and Ventilation Instability due to CO2

2021 ◽  
Author(s):  
Stanley M. Yamashiro ◽  
Takahide Kato
Keyword(s):  
Blood Flow ◽  
Cerebral Blood Flow ◽  
Current Model ◽  
Respiratory Control ◽  
Breath Hold ◽  
Chronic Obstructive ◽  
Brain Blood Flow ◽  
Linear Interaction ◽  
Non Linear ◽  
Flow Changes

A minimal model of cerebral blood flow and respiratory control was developed to describe hypocapnic and hypercapnic responses. Important non-linear properties such as cerebral blood flow changes with arterial partial pressure of carbon dioxide (PaCO2) and associated time dependent circulatory time delays were included. It was also necessary to vary cerebral metabolic rate as a function of PaCO2. The cerebral blood flow model was added to a previously developed respiratory control model to simulate central and peripheral controller dynamics for humans. Model validation was based on previously collected data. The variable time delay due to brain blood flow changes in hypercapnia was an important determinant of predicted instability due to non-linear interaction in addition to linear loop gain considerations. Peripheral chemoreceptor gains above a critical level, but within normal limits, was necessary to produce instability. Instability was observed in recovery from hypercapnia and hypocapnia. The 20 sec breath-hold test appears to be a simple test of brain blood flow mediated instability in hypercapnia. Brain blood flow was predicted to play an important role with non-linear properties. There is an important interaction predicted by the current model between central and peripheral control mechanisms related to instability in hypercapnia recovery. Post hyperventilation breathing pattern can also reveal instability tied to brain blood flow. Previous data collected in patients with chronic obstructive lung disease was closely fitted with the current model and instability predicted. Brain vascular volume was proposed as a potential cause of instability despite cerebral autoregulation promoting constant brain flow.

scholarly journals A non-linear fluid suspension model for blood flow

2019 ◽  
Vol 109 ◽  
pp. 32-39 ◽  
Author(s):  
Wei-Tao Wu ◽  
Nadine Aubry ◽  
James F. Antaki ◽  
Mehrdad Massoudi
Keyword(s):  
Blood Flow ◽  
Non Linear ◽  
Suspension Model

scholarly journals AUTOREGULATION OF BRAIN CIRCULATION IN PROGRESSING CARDIAC FAILURE AND ITS RELATION TO THE APPEARANCE OF SEIZURE READINESS

2016 ◽  
Vol 15 (2) ◽  
pp. 86-93
Author(s):  
M.L. Mamalyga ◽  
◽  
L.M. Mamalyga ◽  
Keyword(s):  
Heart Failure ◽  
Blood Flow ◽  
Cardiac Failure ◽  
Early Stage ◽  
Brain Circulation ◽  
Brain Disorders ◽  
Compression Tests ◽  
Cardiac Decompensation ◽  
Severe Stage ◽  
Basilar Arteries

On the early stage of cardiac decompensation, the blood flow in common carotid and basilar arteries does not change, however the seizure readiness (SR) of animals increases. The preserved reaction on hypercapnic and compression tests allows us to stipulate that the increased SR is not related to the circulatory brain disorders. Progressive aggravation of cardiac failure (CF) leads to the severe stage of decompensation accompanied by decreased blood flow in common carotid and basilar arteries, as well as increases SR. At the same time the metabolic cascade of autoregulation is areactive and myogenic is significantly decreased. Ineffective operation of heart in different stages of heart failure shows not the same effect or backup possibilities for cerebral hemodynamic autoregulation affecting the formation and aggravation of SR. The increased SR in cardiac failure is not always caused by brain ischemia.

scholarly journals Computed Tomography Image under Optimized Iterative Reconstruction Algorithm to Analyze the Characteristics of Blood Flow Field in Cerebral Aneurysm before and after Stent Implantation

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Kunyang Bao ◽  
Chao Liu ◽  
Jin Li ◽  
Xiang Liu ◽  
Wenzhang Luo ◽  
...  
Keyword(s):  
Blood Flow ◽  
Flow Field ◽  
Cerebral Aneurysm ◽  
Iterative Reconstruction ◽  
Stent Implantation ◽  
Cerebral Aneurysms ◽  
Reconstruction Algorithm ◽  
Ct Images ◽  
Iterative Reconstruction Algorithm ◽  
The Difference

In order to analyze the change characteristics of blood flow field in cerebral aneurysms before and after stent implantation, this study first constructed an optimized iterative reconstruction algorithm to reconstruct CT images of patients with cerebral aneurysms and used it to solve the problem of image sharpness. In addition, backprojection image reconstruction algorithm and Fourier transform analytic method were introduced. According to the CT images of cerebral arteries of patients, the lesions were presented in a three-dimensional and visual way through the reconstructed three-dimensional images, thus achieving the effects of simulation and simulation. The results showed that the sensitivity, specificity, and accuracy of the optimized iterative reconstruction algorithm were 90.78%, 83.27%, and 94.82%, which were significantly higher than those of the backprojection image reconstruction algorithm and Fourier transform analysis method, and the difference was statistically significant ( P < 0.05 ). Before operation, the blood flow velocity in the neck of aneurysm was 7.35 × 10−2 m/s, the exit velocity was 1.51 × 10−1 m/s, and the maximum velocity appeared in the upstream part of the exit. After passing through the aneurysm, the blood flow velocity began to decrease gradually, forming a vortex at the top of the tumor. After stent implantation, the neck and outlet velocities of cerebral aneurysm were 9.352 × 10−2 m/s and 1.897 × 10−2 m/s, respectively. The velocity of blood flow decreased after entering the aneurysm, and there was no vortex at the top of the aneurysm. Among the outlet velocities of arterial blood vessels, the velocity before stent implantation was significantly lower than that after stent implantation, and the difference was statistically significant ( P < 0.05 ). Compared with prestent, the shear force distribution on the wall of cerebral aneurysm showed a significant decrease, and the difference was statistically significant ( P < 0.05 ). To sum up, pelvic floor ultrasound based on hybrid iterative reconstruction algorithm has high accuracy in diagnosing the changes of blood flow field in cerebral aneurysms. The application of CT images in the diagnosis of cerebral aneurysms can objectively provide imaging data for clinical practice and has high application value.

scholarly journals Systematic Framework to Predict Early-Stage Liver Carcinoma Using Hybrid of Feature Selection Techniques and Regression Techniques

Complexity ◽  
2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Marium Mehmood ◽  
Nasser Alshammari ◽  
Saad Awadh Alanazi ◽  
Fahad Ahmad
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
Liver Diseases ◽  
Early Stage ◽  
Support Vector ◽  
Liver Carcinoma ◽  
Random Forest Regression ◽  
Soft Computing Techniques ◽  
Regression Algorithms ◽  
Regression Techniques

The liver is the human body’s mandatory organ, but detecting liver disease at an early stage is very difficult due to the hiddenness of symptoms. Liver diseases may cause loss of energy or weakness when some irregularities in the working of the liver get visible. Cancer is one of the most common diseases of the liver and also the most fatal of all. Uncontrolled growth of harmful cells is developed inside the liver. If diagnosed late, it may cause death. Treatment of liver diseases at an early stage is, therefore, an important issue as is designing a model to diagnose early disease. Firstly, an appropriate feature should be identified which plays a more significant part in the detection of liver cancer at an early stage. Therefore, it is essential to extract some essential features from thousands of unwanted features. So, these features will be mined using data mining and soft computing techniques. These techniques give optimized results that will be helpful in disease diagnosis at an early stage. In these techniques, we use feature selection methods to reduce the dataset’s feature, which include Filter, Wrapper, and Embedded methods. Different Regression algorithms are then applied to these methods individually to evaluate the result. Regression algorithms include Linear Regression, Ridge Regression, LASSO Regression, Support Vector Regression, Decision Tree Regression, Multilayer Perceptron Regression, and Random Forest Regression. Based on the accuracy and error rates generated by these Regression algorithms, we have evaluated our results. The result shows that Random Forest Regression with the Wrapper Method from all the deployed Regression techniques is the best and gives the highest R2-Score of 0.8923 and lowest MSE of 0.0618.

scholarly journals Reducing False-Positives in Lung Nodules Detection Using Balanced Datasets

2021 ◽  
Vol 9 ◽  
Author(s):  
Jinglun Liang ◽  
Guoliang Ye ◽  
Jianwen Guo ◽  
Qifan Huang ◽  
Shaohui Zhang
Keyword(s):  
Lung Cancer ◽  
Early Stage ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
False Positives ◽  
Classification Systems ◽  
Cad System ◽  
Nodule Detection ◽  
Diagnosis Of Lung Cancer ◽  
Lung Ct

Malignant pulmonary nodules are one of the main manifestations of lung cancer in early CT image screening. Since lung cancer may have no early obvious symptoms, it is important to develop a computer-aided detection (CAD) system to assist doctors to detect the malignant pulmonary nodules in the early stage of lung cancer CT diagnosis. Due to the recent successful applications of deep learning in image processing, more and more researchers have been trying to apply it to the diagnosis of pulmonary nodules. However, due to the ratio of nodules and non-nodules samples used in the training and testing datasets usually being different from the practical ratio of lung cancer, the CAD classification systems may easily produce higher false-positives while using this imbalanced dataset. This work introduces a filtering step to remove the irrelevant images from the dataset, and the results show that the false-positives can be reduced and the accuracy can be above 98%. There are two steps in nodule detection. Firstly, the images with pulmonary nodules are screened from the whole lung CT images of the patients. Secondly, the exact locations of pulmonary nodules will be detected using Faster R-CNN. Final results show that this method can effectively detect the pulmonary nodules in the CT images and hence potentially assist doctors in the early diagnosis of lung cancer.

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