Displaying publications 41 - 60 of 84 in total

  1. Al-Qaysi ZT, Zaidan BB, Zaidan AA, Suzani MS
    Comput Methods Programs Biomed, 2018 Oct;164:221-237.
    PMID: 29958722 DOI: 10.1016/j.cmpb.2018.06.012
    CONTEXT: Intelligent wheelchair technology has recently been utilised to address several mobility problems. Techniques based on brain-computer interface (BCI) are currently used to develop electric wheelchairs. Using human brain control in wheelchairs for people with disability has elicited widespread attention due to its flexibility.

    OBJECTIVE: This study aims to determine the background of recent studies on wheelchair control based on BCI for disability and map the literature survey into a coherent taxonomy. The study intends to identify the most important aspects in this emerging field as an impetus for using BCI for disability in electric-powered wheelchair (EPW) control, which remains a challenge. The study also attempts to provide recommendations for solving other existing limitations and challenges.

    METHODS: We systematically searched all articles about EPW control based on BCI for disability in three popular databases: ScienceDirect, IEEE and Web of Science. These databases contain numerous articles that considerably influenced this field and cover most of the relevant theoretical and technical issues.

    RESULTS: We selected 100 articles on the basis of our inclusion and exclusion criteria. A large set of articles (55) discussed on developing real-time wheelchair control systems based on BCI for disability signals. Another set of articles (25) focused on analysing BCI for disability signals for wheelchair control. The third set of articles (14) considered the simulation of wheelchair control based on BCI for disability signals. Four articles designed a framework for wheelchair control based on BCI for disability signals. Finally, one article reviewed concerns regarding wheelchair control based on BCI for disability signals.

    DISCUSSION: Since 2007, researchers have pursued the possibility of using BCI for disability in EPW control through different approaches. Regardless of type, articles have focused on addressing limitations that impede the full efficiency of BCI for disability and recommended solutions for these limitations.

    CONCLUSIONS: Studies on wheelchair control based on BCI for disability considerably influence society due to the large number of people with disability. Therefore, we aim to provide researchers and developers with a clear understanding of this platform and highlight the challenges and gaps in the current and future studies.

  2. Sidibé D, Sankar S, Lemaître G, Rastgoo M, Massich J, Cheung CY, et al.
    Comput Methods Programs Biomed, 2017 Feb;139:109-117.
    PMID: 28187882 DOI: 10.1016/j.cmpb.2016.11.001
    This paper proposes a method for automatic classification of spectral domain OCT data for the identification of patients with retinal diseases such as Diabetic Macular Edema (DME). We address this issue as an anomaly detection problem and propose a method that not only allows the classification of the OCT volume, but also allows the identification of the individual diseased B-scans inside the volume. Our approach is based on modeling the appearance of normal OCT images with a Gaussian Mixture Model (GMM) and detecting abnormal OCT images as outliers. The classification of an OCT volume is based on the number of detected outliers. Experimental results with two different datasets show that the proposed method achieves a sensitivity and a specificity of 80% and 93% on the first dataset, and 100% and 80% on the second one. Moreover, the experiments show that the proposed method achieves better classification performance than other recently published works.
  3. Hagiwara Y, Koh JEW, Tan JH, Bhandary SV, Laude A, Ciaccio EJ, et al.
    Comput Methods Programs Biomed, 2018 Oct;165:1-12.
    PMID: 30337064 DOI: 10.1016/j.cmpb.2018.07.012
    BACKGROUND AND OBJECTIVES: Glaucoma is an eye condition which leads to permanent blindness when the disease progresses to an advanced stage. It occurs due to inappropriate intraocular pressure within the eye, resulting in damage to the optic nerve. Glaucoma does not exhibit any symptoms in its nascent stage and thus, it is important to diagnose early to prevent blindness. Fundus photography is widely used by ophthalmologists to assist in diagnosis of glaucoma and is cost-effective.

    METHODS: The morphological features of the disc that is characteristic of glaucoma are clearly seen in the fundus images. However, manual inspection of the acquired fundus images may be prone to inter-observer variation. Therefore, a computer-aided detection (CAD) system is proposed to make an accurate, reliable and fast diagnosis of glaucoma based on the optic nerve features of fundus imaging. In this paper, we reviewed existing techniques to automatically diagnose glaucoma.

    RESULTS: The use of CAD is very effective in the diagnosis of glaucoma and can assist the clinicians to alleviate their workload significantly. We have also discussed the advantages of employing state-of-art techniques, including deep learning (DL), when developing the automated system. The DL methods are effective in glaucoma diagnosis.

    CONCLUSIONS: Novel DL algorithms with big data availability are required to develop a reliable CAD system. Such techniques can be employed to diagnose other eye diseases accurately.

  4. Alade IO, Bagudu A, Oyehan TA, Rahman MAA, Saleh TA, Olatunji SO
    Comput Methods Programs Biomed, 2018 Sep;163:135-142.
    PMID: 30119848 DOI: 10.1016/j.cmpb.2018.05.029
    BACKGROUND AND OBJECTIVES: The refractive index of hemoglobin plays important role in hematology due to its strong correlation with the pathophysiology of different diseases. Measurement of the real part of the refractive index remains a challenge due to strong absorption of the hemoglobin especially at relevant high physiological concentrations. So far, only a few studies on direct measurement of refractive index have been reported and there are no firm agreements on the reported values of refractive index of hemoglobin due to measurement artifacts. In addition, it is time consuming, laborious and expensive to perform several experiments to obtain the refractive index of hemoglobin. In this work, we proposed a very rapid and accurate computational intelligent approach using Genetic Algorithm/Support Vector Regression models to estimate the real part of the refractive index for oxygenated and deoxygenated hemoglobin samples.

    METHODS: These models utilized experimental data of wavelengths and hemoglobin concentrations in building highly accurate Genetic Algorithm/Support Vector Regression model (GA-SVR).

    RESULTS: The developed methodology showed high accuracy as indicated by the low root mean square error values of 4.65 × 10-4 and 4.62 × 10-4 for oxygenated and deoxygenated hemoglobin, respectively. In addition, the models exhibited 99.85 and 99.84% correlation coefficients (r) for the oxygenated and deoxygenated hemoglobin, thus, validating the strong agreement between the predicted and the experimental results CONCLUSIONS: Due to the accuracy and relative simplicity of the proposed models, we envisage that these models would serve as important references for future studies on optical properties of blood.

  5. Ibrahim RW, Hasan AM, Jalab HA
    Comput Methods Programs Biomed, 2018 Sep;163:21-28.
    PMID: 30119853 DOI: 10.1016/j.cmpb.2018.05.031
    BACKGROUND AND OBJECTIVES: The MRI brain tumors segmentation is challenging due to variations in terms of size, shape, location and features' intensity of the tumor. Active contour has been applied in MRI scan image segmentation due to its ability to produce regions with boundaries. The main difficulty that encounters the active contour segmentation is the boundary tracking which is controlled by minimization of energy function for segmentation. Hence, this study proposes a novel fractional Wright function (FWF) as a minimization of energy technique to improve the performance of active contour without edge method.

    METHOD: In this study, we implement FWF as an energy minimization function to replace the standard gradient-descent method as minimization function in Chan-Vese segmentation technique. The proposed FWF is used to find the boundaries of an object by controlling the inside and outside values of the contour. In this study, the objective evaluation is used to distinguish the differences between the processed segmented images and ground truth using a set of statistical parameters; true positive, true negative, false positive, and false negative.

    RESULTS: The FWF as a minimization of energy was successfully implemented on BRATS 2013 image dataset. The achieved overall average sensitivity score of the brain tumors segmentation was 94.8 ± 4.7%.

    CONCLUSIONS: The results demonstrate that the proposed FWF method minimized the energy function more than the gradient-decent method that was used in the original three-dimensional active contour without edge (3DACWE) method.

  6. Acharya UR, Raghavendra U, Koh JEW, Meiburger KM, Ciaccio EJ, Hagiwara Y, et al.
    Comput Methods Programs Biomed, 2018 Nov;166:91-98.
    PMID: 30415722 DOI: 10.1016/j.cmpb.2018.10.006
    BACKGROUND AND OBJECTIVE: Liver fibrosis is a type of chronic liver injury that is characterized by an excessive deposition of extracellular matrix protein. Early detection of liver fibrosis may prevent further growth toward liver cirrhosis and hepatocellular carcinoma. In the past, the only method to assess liver fibrosis was through biopsy, but this examination is invasive, expensive, prone to sampling errors, and may cause complications such as bleeding. Ultrasound-based elastography is a promising tool to measure tissue elasticity in real time; however, this technology requires an upgrade of the ultrasound system and software. In this study, a novel computer-aided diagnosis tool is proposed to automatically detect and classify the various stages of liver fibrosis based upon conventional B-mode ultrasound images.

    METHODS: The proposed method uses a 2D contourlet transform and a set of texture features that are efficiently extracted from the transformed image. Then, the combination of a kernel discriminant analysis (KDA)-based feature reduction technique and analysis of variance (ANOVA)-based feature ranking technique was used, and the images were then classified into various stages of liver fibrosis.

    RESULTS: Our 2D contourlet transform and texture feature analysis approach achieved a 91.46% accuracy using only four features input to the probabilistic neural network classifier, to classify the five stages of liver fibrosis. It also achieved a 92.16% sensitivity and 88.92% specificity for the same model. The evaluation was done on a database of 762 ultrasound images belonging to five different stages of liver fibrosis.

    CONCLUSIONS: The findings suggest that the proposed method can be useful to automatically detect and classify liver fibrosis, which would greatly assist clinicians in making an accurate diagnosis.

  7. Mohammed KI, Zaidan AA, Zaidan BB, Albahri OS, Albahri AS, Alsalem MA, et al.
    Comput Methods Programs Biomed, 2020 Mar;185:105151.
    PMID: 31710981 DOI: 10.1016/j.cmpb.2019.105151
    CONTEXT: Telemedicine has been increasingly used in healthcare to provide services to patients remotely. However, prioritising patients with multiple chronic diseases (MCDs) in telemedicine environment is challenging because it includes decision-making (DM) with regard to the emergency degree of each chronic disease for every patient.

    OBJECTIVE: This paper proposes a novel technique for reorganisation of opinion order to interval levels (TROOIL) to prioritise the patients with MCDs in real-time remote health-monitoring system.

    METHODS: The proposed TROOIL technique comprises six steps for prioritisation of patients with MCDs: (1) conversion of actual data into intervals; (2) rule generation; (3) rule ordering; (4) expert rule validation; (5) data reorganisation; and (6) criteria weighting and ranking alternatives within each rule. The secondary dataset of 500 patients from the most relevant study in a remote prioritisation area was adopted. The dataset contains three diseases, namely, chronic heart disease, high blood pressure (BP) and low BP.

    RESULTS: The proposed TROOIL is an effective technique for prioritising patients with MCDs. In the objective validation, remarkable differences were recognised among the groups' scores, indicating identical ranking results. In the evaluation of issues within all scenarios, the proposed framework has an advantage of 22.95% over the benchmark framework.

    DISCUSSION: Patients with the most severe MCD were treated first on the basis of their highest priority levels. The treatment for patients with less severe cases was delayed more than that for other patients.

    CONCLUSIONS: The proposed TROOIL technique can deal with multiple DM problems in prioritisation of patients with MCDs.

  8. Arunachalam GR, Chiew YS, Tan CP, Ralib AM, Nor MBM
    Comput Methods Programs Biomed, 2020 Jan;183:105103.
    PMID: 31606559 DOI: 10.1016/j.cmpb.2019.105103
    BACKGROUND AND OBJECTIVE: Mechanical ventilation therapy of respiratory failure patients can be guided by monitoring patient-specific respiratory mechanics. However, the patient's spontaneous breathing effort during controlled ventilation changes airway pressure waveform and thus affects the model-based identification of patient-specific respiratory mechanics parameters. This study develops a model to estimate respiratory mechanics in the presence of patient effort.

    METHODS: Gaussian effort model (GEM) is a derivative of the single-compartment model with basis function. GEM model uses a linear combination of basis functions to model the nonlinear pressure waveform of spontaneous breathing patients. The GEM model estimates respiratory mechanics such as Elastance and Resistance along with the magnitudes of basis functions, which accounts for patient inspiratory effort.

    RESULTS AND DISCUSSION: The GEM model was tested using both simulated data and a retrospective observational clinical trial patient data. GEM model fitting to the original airway pressure waveform is better than any existing models when reverse triggering asynchrony is present. The fitting error of GEM model was less than 10% for both simulated data and clinical trial patient data.

    CONCLUSION: GEM can capture the respiratory mechanics in the presence of patient effect in volume control ventilation mode and also can be used to assess patient-ventilator interaction. This model determines basis functions magnitudes, which can be used to simulate any waveform of patient effort pressure for future studies. The estimation of parameter identification GEM model can further be improved by constraining the parameters within a physiologically plausible range during least-square nonlinear regression.

  9. Acharya UR, Faust O, Ciaccio EJ, Koh JEW, Oh SL, Tan RS, et al.
    Comput Methods Programs Biomed, 2019 Jul;175:163-178.
    PMID: 31104705 DOI: 10.1016/j.cmpb.2019.04.018
    BACKGROUND AND OBJECTIVE: Complex fractionated atrial electrograms (CFAE) may contain information concerning the electrophysiological substrate of atrial fibrillation (AF); therefore they are of interest to guide catheter ablation treatment of AF. Electrogram signals are shaped by activation events, which are dynamical in nature. This makes it difficult to establish those signal properties that can provide insight into the ablation site location. Nonlinear measures may improve information. To test this hypothesis, we used nonlinear measures to analyze CFAE.

    METHODS: CFAE from several atrial sites, recorded for a duration of 16 s, were acquired from 10 patients with persistent and 9 patients with paroxysmal AF. These signals were appraised using non-overlapping windows of 1-, 2- and 4-s durations. The resulting data sets were analyzed with Recurrence Plots (RP) and Recurrence Quantification Analysis (RQA). The data was also quantified via entropy measures.

    RESULTS: RQA exhibited unique plots for persistent versus paroxysmal AF. Similar patterns were observed to be repeated throughout the RPs. Trends were consistent for signal segments of 1 and 2 s as well as 4 s in duration. This was suggestive that the underlying signal generation process is also repetitive, and that repetitiveness can be detected even in 1-s sequences. The results also showed that most entropy metrics exhibited higher measurement values (closer to equilibrium) for persistent AF data. It was also found that Determinism (DET), Trapping Time (TT), and Modified Multiscale Entropy (MMSE), extracted from signals that were acquired from locations at the posterior atrial free wall, are highly discriminative of persistent versus paroxysmal AF data.

    CONCLUSIONS: Short data sequences are sufficient to provide information to discern persistent versus paroxysmal AF data with a significant difference, and can be useful to detect repeating patterns of atrial activation.

  10. Omam S, Babini MH, Sim S, Tee R, Nathan V, Namazi H
    Comput Methods Programs Biomed, 2020 Feb;184:105293.
    PMID: 31887618 DOI: 10.1016/j.cmpb.2019.105293
    BACKGROUND AND OBJECTIVE: Human body is covered with skin in different parts. In fact, skin reacts to different changes around human. For instance, when the surrounding temperature changes, human skin will react differently. It is known that the activity of skin is regulated by human brain. In this research, for the first time we investigate the relation between the activities of human skin and brain by mathematical analysis of Galvanic Skin Response (GSR) and Electroencephalography (EEG) signals.

    METHOD: For this purpose, we employ fractal theory and analyze the variations of fractal dimension of GSR and EEG signals when subjects are exposed to different olfactory stimuli in the form of pleasant odors.

    RESULTS: Based on the obtained results, the complexity of GSR signal changes with the complexity of EEG signal in case of different stimuli, where by increasing the molecular complexity of olfactory stimuli, the complexity of EEG and GSR signals increases. The results of statistical analysis showed the significant effect of stimulation on variations of complexity of GSR signal. In addition, based on effect size analysis, fourth odor with greatest molecular complexity had the greatest effect on variations of complexity of EEG and GSR signals.

    CONCLUSION: Therefore, it can be said that human skin reaction changes with the variations in the activity of human brain. The result of analysis in this research can be further used to make a model between the activities of human skin and brain that will enable us to predict skin reaction to different stimuli.

  11. Kho ASK, Foo JJ, Ooi ET, Ooi EH
    Comput Methods Programs Biomed, 2020 Feb;184:105289.
    PMID: 31891903 DOI: 10.1016/j.cmpb.2019.105289
    BACKGROUND AND OBJECTIVE: The majority of the studies on radiofrequency ablation (RFA) have focused on enlarging the size of the coagulation zone. An aspect that is crucial but often overlooked is the shape of the coagulation zone. The shape is crucial because the majority of tumours are irregularly-shaped. In this paper, the ability to manipulate the shape of the coagulation zone following saline-infused RFA by altering the location of saline infusion is explored.

    METHODS: A 3D model of the liver tissue was developed. Saline infusion was described using the dual porosity model, while RFA was described using the electrostatic and bioheat transfer equations. Three infusion locations were investigated, namely at the proximal end, the middle and the distal end of the electrode. Investigations were carried out numerically using the finite element method.

    RESULTS: Results indicated that greater thermal coagulation was found in the region of tissue occupied by the saline bolus. Infusion at the middle of the electrode led to the largest coagulation volume followed by infusion at the proximal and distal ends. It was also found that the ability to delay roll-off, as commonly associated with saline-infused RFA, was true only for the case when infusion is carried out at the middle. When infused at the proximal and distal ends, the occurrence of roll-off was advanced. This may be due to the rapid and more intense heating experienced by the tissue when infusion is carried out at the electrode ends where Joule heating is dominant.

    CONCLUSION: Altering the location of saline infusion can influence the shape of the coagulation zone following saline-infused RFA. The ability to 'shift' the coagulation zone to a desired location opens up great opportunities for the development of more precise saline-infused RFA treatment that targets specific regions within the tissue.

  12. Faust O, Razaghi H, Barika R, Ciaccio EJ, Acharya UR
    Comput Methods Programs Biomed, 2019 Jul;176:81-91.
    PMID: 31200914 DOI: 10.1016/j.cmpb.2019.04.032
    BACKGROUND AND OBJECTIVE: Sleep is an important part of our life. That importance is highlighted by the multitude of health problems which result from sleep disorders. Detecting these sleep disorders requires an accurate interpretation of physiological signals. Prerequisite for this interpretation is an understanding of the way in which sleep stage changes manifest themselves in the signal waveform. With that understanding it is possible to build automated sleep stage scoring systems. Apart from their practical relevance for automating sleep disorder diagnosis, these systems provide a good indication of the amount of sleep stage related information communicated by a specific physiological signal.

    METHODS: This article provides a comprehensive review of automated sleep stage scoring systems, which were created since the year 2000. The systems were developed for Electrocardiogram (ECG), Electroencephalogram (EEG), Electrooculogram (EOG), and a combination of signals.

    RESULTS: Our review shows that all of these signals contain information for sleep stage scoring.

    CONCLUSIONS: The result is important, because it allows us to shift our research focus away from information extraction methods to systemic improvements, such as patient comfort, redundancy, safety and cost.

  13. Yildirim O, Baloglu UB, Tan RS, Ciaccio EJ, Acharya UR
    Comput Methods Programs Biomed, 2019 Jul;176:121-133.
    PMID: 31200900 DOI: 10.1016/j.cmpb.2019.05.004
    BACKGROUND AND OBJECTIVE: For diagnosis of arrhythmic heart problems, electrocardiogram (ECG) signals should be recorded and monitored. The long-term signal records obtained are analyzed by expert cardiologists. Devices such as the Holter monitor have limited hardware capabilities. For improved diagnostic capacity, it would be helpful to detect arrhythmic signals automatically. In this study, a novel approach is presented as a candidate solution for these issues.

    METHODS: A convolutional auto-encoder (CAE) based nonlinear compression structure is implemented to reduce the signal size of arrhythmic beats. Long-short term memory (LSTM) classifiers are employed to automatically recognize arrhythmias using ECG features, which are deeply coded with the CAE network.

    RESULTS: Based upon the coded ECG signals, both storage requirement and classification time were considerably reduced. In experimental studies conducted with the MIT-BIH arrhythmia database, ECG signals were compressed by an average 0.70% percentage root mean square difference (PRD) rate, and an accuracy of over 99.0% was observed.

    CONCLUSIONS: One of the significant contributions of this study is that the proposed approach can significantly reduce time duration when using LSTM networks for data analysis. Thus, a novel and effective approach was proposed for both ECG signal compression, and their high-performance automatic recognition, with very low computational cost.

  14. Cheong JKK, Yap S, Ooi ET, Ooi EH
    Comput Methods Programs Biomed, 2019 Jul;176:17-32.
    PMID: 31200904 DOI: 10.1016/j.cmpb.2019.04.028
    BACKGROUND AND OBJECTIVES: Recently, there have been calls for RFA to be implemented in the bipolar mode for cancer treatment due to the benefits it offers over the monopolar mode. These include the ability to prevent skin burns at the grounding pad and to avoid tumour track seeding. The usage of bipolar RFA in clinical practice remains uncommon however, as not many research studies have been carried out on bipolar RFA. As such, there is still uncertainty in understanding the effects of the different RF probe configurations on the treatment outcome of RFA. This paper demonstrates that the electrode lengths have a strong influence on the mechanics of bipolar RFA. The information obtained here may lead to further optimization of the system for subsequent uses in the hospitals.

    METHODS: A 2D model in the axisymmetric coordinates was developed to simulate the electro-thermophysiological responses of the tissue during a single probe bipolar RFA. Two different probe configurations were considered, namely the configuration where the active electrode is longer than the ground and the configuration where the ground electrode is longer than the active. The mathematical model was first verified with an existing experimental study found in the literature.

    RESULTS: Results from the simulations showed that heating is confined only to the region around the shorter electrode, regardless of whether the shorter electrode is the active or the ground. Consequently, thermal coagulation also occurs in the region surrounding the shorter electrode. This opened up the possibility for a better customized treatment through the development of RF probes with adjustable electrode lengths.

    CONCLUSIONS: The electrode length was found to play a significant role on the outcome of single probe bipolar RFA. In particular, the length of the shorter electrode becomes the limiting factor that influences the mechanics of single probe bipolar RFA. Results from this study can be used to further develop and optimize bipolar RFA as an effective and reliable cancer treatment technique.

  15. Albahri OS, Al-Obaidi JR, Zaidan AA, Albahri AS, Zaidan BB, Salih MM, et al.
    Comput Methods Programs Biomed, 2020 Nov;196:105617.
    PMID: 32593060 DOI: 10.1016/j.cmpb.2020.105617
    CONTEXT: People who have recently recovered from the threat of deteriorating coronavirus disease-2019 (COVID-19) have antibodies to the coronavirus circulating in their blood. Thus, the transfusion of these antibodies to deteriorating patients could theoretically help boost their immune system. Biologically, two challenges need to be surmounted to allow convalescent plasma (CP) transfusion to rescue the most severe COVID-19 patients. First, convalescent subjects must meet donor selection plasma criteria and comply with national health requirements and known standard routine procedures. Second, multi-criteria decision-making (MCDM) problems should be considered in the selection of the most suitable CP and the prioritisation of patients with COVID-19.

    OBJECTIVE: This paper presents a rescue framework for the transfusion of the best CP to the most critical patients with COVID-19 on the basis of biological requirements by using machine learning and novel MCDM methods.

    METHOD: The proposed framework is illustrated on the basis of two distinct and consecutive phases (i.e. testing and development). In testing, ABO compatibility is assessed after classifying donors into the four blood types, namely, A, B, AB and O, to indicate the suitability and safety of plasma for administration in order to refine the CP tested list repository. The development phase includes patient and donor sides. In the patient side, prioritisation is performed using a contracted patient decision matrix constructed between 'serological/protein biomarkers and the ratio of the partial pressure of oxygen in arterial blood to fractional inspired oxygen criteria' and 'patient list based on novel MCDM method known as subjective and objective decision by opinion score method'. Then, the patients with the most urgent need are classified into the four blood types and matched with a tested CP list from the test phase in the donor side. Thereafter, the prioritisation of CP tested list is performed using the contracted CP decision matrix.

    RESULT: An intelligence-integrated concept is proposed to identify the most appropriate CP for corresponding prioritised patients with COVID-19 to help doctors hasten treatments.

    DISCUSSION: The proposed framework implies the benefits of providing effective care and prevention of the extremely rapidly spreading COVID-19 from affecting patients and the medical sector.

  16. Kho ASK, Ooi EH, Foo JJ, Ooi ET
    Comput Methods Programs Biomed, 2021 Nov;211:106436.
    PMID: 34601185 DOI: 10.1016/j.cmpb.2021.106436
    BACKGROUND AND OBJECTIVE: Saline infusion is applied together with radiofrequency ablation (RFA) to enlarge the ablation zone. However, one of the issues with saline-infused RFA is backflow, which spreads saline along the insertion track. This raises the concern of not only thermally ablating the tissue within the backflow region, but also the loss of saline from the targeted tissue, which may affect the treatment efficacy.

    METHODS: In the present study, 2D axisymmetric models were developed to investigate how saline backflow influence saline-infused RFA and whether the aforementioned concerns are warranted. Saline-infused RFA was described using the dual porosity-Joule heating model. The hydrodynamics of backflow was described using Poiseuille law by assuming the flow to be similar to that in a thin annulus. Backflow lengths of 3, 4.5, 6 and 9 cm were considered.

    RESULTS: Results showed that there is no concern of thermally ablating the tissue in the backflow region. This is due to the Joule heating being inversely proportional to distance from the electrode to the fourth power. Results also indicated that larger backflow lengths led to larger growth of thermal damage along the backflow region and greater decrease in coagulation volume. Hence, backflow needs to be controlled to ensure an effective treatment of saline-infused RFA.

    CONCLUSIONS: There is no risk of ablating tissues around the needle insertion track due to backflow. Instead, the risk of underablation as a result of the loss of saline due to backflow was found to be of greater concern.

  17. Abbasian Ardakani A, Bureau NJ, Ciaccio EJ, Acharya UR
    Comput Methods Programs Biomed, 2022 Mar;215:106609.
    PMID: 34990929 DOI: 10.1016/j.cmpb.2021.106609
    Radiomics is a newcomer field that has opened new windows for precision medicine. It is related to extraction of a large number of quantitative features from medical images, which may be difficult to detect visually. Underlying tumor biology can change physical properties of tissues, which affect patterns of image pixels and radiomics features. The main advantage of radiomics is that it can characterize the whole tumor non-invasively, even after a single sampling from an image. Therefore, it can be linked to a "digital biopsy". Physicians need to know about radiomics features to determine how their values correlate with the appearance of lesions and diseases. Indeed, physicians need practical references to conceive of basics and concepts of each radiomics feature without knowing their sophisticated mathematical formulas. In this review, commonly used radiomics features are illustrated with practical examples to help physicians in their routine diagnostic procedures.
  18. Ang CYS, Chiew YS, Vu LH, Cove ME
    Comput Methods Programs Biomed, 2022 Mar;215:106601.
    PMID: 34973606 DOI: 10.1016/j.cmpb.2021.106601
    BACKGROUND: Spontaneous breathing (SB) effort during mechanical ventilation (MV) is an important metric of respiratory drive. However, SB effort varies due to a variety of factors, including evolving pathology and sedation levels. Therefore, assessment of SB efforts needs to be continuous and non-invasive. This is important to prevent both over- and under-assistance with MV. In this study, a machine learning model, Convolutional Autoencoder (CAE) is developed to quantify the magnitude of SB effort using only bedside MV airway pressure and flow waveform.

    METHOD: The CAE model was trained using 12,170,655 simulated SB flow and normal flow data (NB). The paired SB and NB flow data were simulated using a Gaussian Effort Model (GEM) with 5 basis functions. When the CAE model is given a SB flow input, it is capable of predicting a corresponding NB flow for the SB flow input. The magnitude of SB effort (SBEMag) is then quantified as the difference between the SB and NB flows. The CAE model was used to evaluate the SBEMag of 9 pressure control/ support datasets. Results were validated using a mean squared error (MSE) fitting between clinical and training SB flows.

    RESULTS: The CAE model was able to produce NB flows from the clinical SB flows with the median SBEMag of the 9 datasets being 25.39% [IQR: 21.87-25.57%]. The absolute error in SBEMag using MSE validation yields a median of 4.77% [IQR: 3.77-8.56%] amongst the cohort. This shows the ability of the GEM to capture the intrinsic details present in SB flow waveforms. Analysis also shows both intra-patient and inter-patient variability in SBEMag.

    CONCLUSION: A Convolutional Autoencoder model was developed with simulated SB and NB flow data and is capable of quantifying the magnitude of patient spontaneous breathing effort. This provides potential application for real-time monitoring of patient respiratory drive for better management of patient-ventilator interaction.

  19. Corda JV, Shenoy BS, Ahmad KA, Lewis L, K P, Khader SMA, et al.
    Comput Methods Programs Biomed, 2022 Feb;214:106538.
    PMID: 34848078 DOI: 10.1016/j.cmpb.2021.106538
    BACKGROUND AND OBJECTIVE: Neonates are preferential nasal breathers up to 3 months of age. The nasal anatomy in neonates and infants is at developing stages whereas the adult nasal cavities are fully grown which implies that the study of airflow dynamics in the neonates and infants are significant. In the present study, the nasal airways of the neonate, infant and adult are anatomically compared and their airflow patterns are investigated.

    METHODS: Computational Fluid Dynamics (CFD) approach is used to simulate the airflow in a neonate, an infant and an adult in sedentary breathing conditions. The healthy CT scans are segmented using MIMICS 21.0 (Materialise, Ann arbor, MI). The patient-specific 3D airway models are analyzed for low Reynolds number flow using ANSYS FLUENT 2020 R2. The applicability of the Grid Convergence Index (GCI) for polyhedral mesh adopted in this work is also verified.

    RESULTS: This study shows that the inferior meatus of neonates accounted for only 15% of the total airflow. This was in contrast to the infants and adults who experienced 49 and 31% of airflow at the inferior meatus region. Superior meatus experienced 25% of total flow which is more than normal for the neonate. The highest velocity of 1.8, 2.6 and 3.7 m/s was observed at the nasal valve region for neonates, infants and adults, respectively. The anterior portion of the nasal cavity experienced maximum wall shear stress with average values of 0.48, 0.25 and 0.58 Pa for the neonates, infants and adults.

    CONCLUSIONS: The neonates have an underdeveloped nasal cavity which significantly affects their airway distribution. The absence of inferior meatus in the neonates has limited the flow through the inferior regions and resulted in uneven flow distribution.

  20. Lee JWW, Chiew YS, Wang X, Tan CP, Mat Nor MB, Cove ME, et al.
    Comput Methods Programs Biomed, 2022 Feb;214:106577.
    PMID: 34936946 DOI: 10.1016/j.cmpb.2021.106577
    BACKGROUND AND OBJECTIVE: Mechanical ventilation is the primary form of care provided to respiratory failure patients. Limited guidelines and conflicting results from major clinical trials means selection of mechanical ventilation settings relies heavily on clinician experience and intuition. Determining optimal mechanical ventilation settings is therefore difficult, where non-optimal mechanical ventilation can be deleterious. To overcome these difficulties, this research proposes a model-based method to manage the wide range of possible mechanical ventilation settings, while also considering patient-specific conditions and responses.

    METHODS: This study shows the design and development of the "VENT" protocol, which integrates the single compartment linear lung model with clinical recommendations from landmark studies, to aid clinical decision-making in selecting mechanical ventilation settings. Using retrospective breath data from a cohort of 24 patients, 3,566 and 2,447 clinically implemented VC and PC settings were extracted respectively. Using this data, a VENT protocol application case study and clinical comparison is performed, and the prediction accuracy of the VENT protocol is validated against actual measured outcomes of pressure and volume.

    RESULTS: The study shows the VENT protocols' potential use in narrowing an overwhelming number of possible mechanical ventilation setting combinations by up to 99.9%. The comparison with retrospective clinical data showed that only 33% and 45% of clinician settings were approved by the VENT protocol. The unapproved settings were mainly due to exceeding clinical recommended settings. When utilising the single compartment model in the VENT protocol for forecasting peak pressures and tidal volumes, median [IQR] prediction error values of 0.75 [0.31 - 1.83] cmH2O and 0.55 [0.19 - 1.20] mL/kg were obtained.

    CONCLUSIONS: Comparing the proposed protocol with retrospective clinically implemented settings shows the protocol can prevent harmful mechanical ventilation setting combinations for which clinicians would be otherwise unaware. The VENT protocol warrants a more detailed clinical study to validate its potential usefulness in a clinical setting.

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