Journal of Medical Signals & Sensors最新文献

{"title":"Predicting Efficacy of 5-Fluorouracil Therapy via a Mathematical Model with Fuzzy Uncertain Parameters.","authors":"Sajad Shafiekhani,&nbsp;Amir Homayoun Jafari,&nbsp;Leila Jafarzadeh,&nbsp;Vahid Sadeghi,&nbsp;Nematollah Gheibi","doi":"10.4103/jmss.jmss_92_21","DOIUrl":"https://doi.org/10.4103/jmss.jmss_92_21","url":null,"abstract":"<p><strong>Background: </strong>Due to imprecise/missing data used for parameterization of ordinary differential equations (ODEs), model parameters are uncertain. Uncertainty of parameters has hindered the application of ODEs that require accurate parameters.</p><p><strong>Methods: </strong>We extended an available ODE model of tumor-immune system interactions via fuzzy logic to illustrate the fuzzification procedure of an ODE model. The fuzzy ODE (FODE) model assigns a fuzzy number to the parameters, to capture parametric uncertainty. We used the FODE model to predict tumor and immune cell dynamics and to assess the efficacy of 5-fluorouracil (5-FU) chemotherapy.</p><p><strong>Result: </strong>FODE model investigates how parametric uncertainty affects the uncertainty band of cell dynamics in the presence and absence of 5-FU treatment. <i>In silico</i> experiments revealed that the frequent 5-FU injection created a beneficial tumor microenvironment that exerted detrimental effects on tumor cells by enhancing the infiltration of CD8+ T cells, and natural killer cells, and decreasing that of myeloid-derived suppressor cells. The global sensitivity analysis was proved model robustness against random perturbation to parameters.</p><p><strong>Conclusion: </strong>ODE models with fuzzy uncertain kinetic parameters cope with insufficient/imprecise experimental data in the field of mathematical oncology and can predict cell dynamics uncertainty band.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"12 3","pages":"202-218"},"PeriodicalIF":0.0,"publicationDate":"2022-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/64/0b/JMSS-12-202.PMC9480509.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40368423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pragmatic Approaches to Reducing Radiation Dose in Brain Computed Tomography Scan using Scan Parameter Modification.","authors":"Mohammad Reza Choopani,&nbsp;Iraj Abedi,&nbsp;Fatemeh Dalvand","doi":"10.4103/jmss.JMSS_83_20","DOIUrl":"https://doi.org/10.4103/jmss.JMSS_83_20","url":null,"abstract":"<p><strong>Background: </strong>High radiation dose of patients has become a concern in the computed tomography (CT) examinations. The aim of this study is to guide the radiology technician in modifying or optimizing the underlying parameters of the CT scan to reduce the patient radiation dose and produce an acceptable image quality for diagnosis.</p><p><strong>Methods: </strong>The body mass measurement device phantom was repeatedly scanned by changing the scan parameters. To analyze the image quality, software-based and observer-based evaluations were employed. To study the effect of scan parameters such as slice thickness and reconstruction filter on image quality and radiation dose, the structural equation modeling was used.</p><p><strong>Results: </strong>By changing the reconstruction filter from standard to soft and slice thickness from 2.5 mm to 5 mm, low-contrast resolution did not change significantly. In addition, by increasing the slice thickness and changing the reconstruction filter, the spatial resolution at different radiation conditions did not significantly differ from the standard irradiation conditions (<i>P</i> > 0.05).</p><p><strong>Conclusion: </strong>In this study, it was shown that in the brain CT scan imaging, the radiation dose was reduced by 30%-50% by increasing the slice thickness or changing the reconstruction filter. It is necessary to adjust the CT scan protocols according to clinical requirements or the special conditions of some patients while maintaining acceptable image quality.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"12 3","pages":"219-226"},"PeriodicalIF":0.0,"publicationDate":"2022-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/d9/42/JMSS-12-219.PMC9480513.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40368427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brain Tumor Segmentation using Hierarchical Combination of Fuzzy Logic and Cellular Automata.","authors":"Roqaie Kalantari,&nbsp;Roqaie Moqadam,&nbsp;Nazila Loghmani,&nbsp;Armin Allahverdy,&nbsp;Mohammad Bagher Shiran,&nbsp;Arash Zare-Sadeghi","doi":"10.4103/jmss.jmss_128_21","DOIUrl":"https://doi.org/10.4103/jmss.jmss_128_21","url":null,"abstract":"<p><strong>Background: </strong>Magnetic resonance (MR) image is one of the most important diagnostic tools for brain tumor detection. Segmentation of glioma tumor region in brain MR images is challenging in medical image processing problems. Precise and reliable segmentation algorithms can be significantly helpful in the diagnosis and treatment planning.</p><p><strong>Methods: </strong>In this article, a novel brain tumor segmentation method is introduced as a postsegmentation module, which uses the primary segmentation method's output as input and makes the segmentation performance values better. This approach is a combination of fuzzy logic and cellular automata (CA).</p><p><strong>Results: </strong>The BraTS online dataset has been used for implementing the proposed method. In the first step, the intensity of each pixel is fed to a fuzzy system to label each pixel, and at the second step, the label of each pixel is fed to a fuzzy CA to make the performance of segmentation better. This step repeated while the performance saturated. The accuracy of the first step was 85.8%, but the accuracy of segmentation after using fuzzy CA was obtained to 99.8%.</p><p><strong>Conclusion: </strong>The practical results have shown that our proposed method could improve the brain tumor segmentation in MR images significantly in comparison with other approaches.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"12 3","pages":"263-268"},"PeriodicalIF":0.0,"publicationDate":"2022-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/bb/f1/JMSS-12-263.PMC9480508.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40368424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cardiovascular System Modeling Using Windkessel Segmentation Model Based on Photoplethysmography Measurements of Fingers and Toes.","authors":"Ervin Masita Dewi,&nbsp;Sugondo Hadiyoso,&nbsp;Tati Latifah Erawati Rajab Mengko,&nbsp;Hasballah Zakaria,&nbsp;Kastam Astami","doi":"10.4103/jmss.jmss_101_21","DOIUrl":"https://doi.org/10.4103/jmss.jmss_101_21","url":null,"abstract":"<p><strong>Background: </strong>Photoplethysmography (PPG) contains information about the health condition of the heart and blood vessels. Cardiovascular system modeling using PPG signal measurements can represent, analyze, and predict the cardiovascular system.</p><p><strong>Methods: </strong>This study aims to make a cardiovascular system model using a Windkessel model by dividing the blood vessels into seven segments. This process involves the PPG signal of the fingertips and toes for further analysis to obtain the condition of the elasticity of the blood vessels as the main parameter. The method is to find the Resistance, Inductance, and Capacitance (RLC) value of each segment of the body through the equivalent equation between the electronic unit and the cardiovascular unit. The modeling made is focused on PPG parameters in the form of stiffness index, the time delay (△t), and augmentation index.</p><p><strong>Results: </strong>The results of the model simulation using PSpice were then compared with the results of measuring the PPG signal to analyze changes in the behavior of the PPG signal taken from ten healthy people with an average age of 46 years, compared to ten cardiac patients with an average age of 48 years. It is found that decreasing 20% of capacitance value and the arterial stiffness parameter will close to cardiac patients' data. Compared with the measurement results, the correlation of the PPG signal in the simulation model is more than 0.9.</p><p><strong>Conclusions: </strong>The proposed model is expected to be used in the early detection of arterial stiffness. It can also be used to study the dynamics of the cardiovascular system, including changes in blood flow velocity and blood pressure.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"12 3","pages":"192-201"},"PeriodicalIF":0.0,"publicationDate":"2022-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/9b/5f/JMSS-12-192.PMC9480512.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40368426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence Approaches on X-ray-oriented Images Process for Early Detection of COVID-19.","authors":"Sorayya Rezayi,&nbsp;Marjan Ghazisaeedi,&nbsp;Sharareh Rostam Niakan Kalhori,&nbsp;Soheila Saeedi","doi":"10.4103/jmss.jmss_111_21","DOIUrl":"https://doi.org/10.4103/jmss.jmss_111_21","url":null,"abstract":"<p><strong>Background: </strong>COVID-19 is a global public health problem that is crucially important to be diagnosed in the early stages. This study aimed to investigate the use of artificial intelligence (AI) to process X-ray-oriented images to diagnose COVID-19 disease.</p><p><strong>Methods: </strong>A systematic search was conducted in Medline (through PubMed), Scopus, ISI Web of Science, Cochrane Library, and IEEE Xplore Digital Library to identify relevant studies published until 21 September 2020.</p><p><strong>Results: </strong>We identified 208 papers after duplicate removal and filtered them into 60 citations based on inclusion and exclusion criteria. Direct results sufficiently indicated a noticeable increase in the number of published papers in July-2020. The most widely used datasets were, respectively, GitHub repository, hospital-oriented datasets, and Kaggle repository. The Keras library, Tensorflow, and Python had been also widely employed in articles. X-ray images were applied more in the selected articles. The most considerable value of accuracy, sensitivity, specificity, and Area under the ROC Curve was reported for ResNet18 in reviewed techniques; all the mentioned indicators for this mentioned network were equal to one (100%).</p><p><strong>Conclusion: </strong>This review revealed that the application of AI can accelerate the process of diagnosing COVID-19, and these methods are effective for the identification of COVID-19 cases exploiting Chest X-ray images.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"12 3","pages":"233-253"},"PeriodicalIF":0.0,"publicationDate":"2022-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/fc/b8/JMSS-12-233.PMC9480507.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40368972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Hybrid Approach to Multimodal Biometric Recognition Based on Feature-level Fusion of Face, Two Irises, and Both Thumbprints.","authors":"Mohammad H Safavipour,&nbsp;Mohammad A Doostari,&nbsp;Hamed Sadjedi","doi":"10.4103/jmss.jmss_103_21","DOIUrl":"https://doi.org/10.4103/jmss.jmss_103_21","url":null,"abstract":"<p><strong>Background: </strong>The most significant motivations for designing multi-biometric systems are high-accuracy recognition, high-security assurances as well as overcoming the limitations like non-universality, noisy sensor data, and large intra-user variations. Therefore, choosing data for fusion is of high significance for the design of a multimodal biometric system. The feature vectors contain richer information than the scores, decisions and even raw data, thereby making feature-level fusion more effective than other levels.</p><p><strong>Method: </strong>In the proposed method, kernel is used for fusion in feature space. First, the face features are extracted using kernel-based methods, the features of both right and left irises are extracted using Hough Transform and Daugman algorithm methods, and the features of both thumb prints are extracted using the Gabor filter bank. Second, after normalization operations, we use kernel methods to map the feature vectors to a kernel Hilbert space where non-linear relations are shown as linear for the purpose of compatibility of feature spaces. Then, dimensionality reduction algorithms are used to the fusion of the feature vectors extracted from fingerprints, irises and the face. since the proposed system uses face, both right 7and left irises and right and left thumbprints, it is hybrid multi-biometric system. We c8arried out the tests on seven databases.</p><p><strong>Results: </strong>Our results show that the hybrid multimodal template, while being secure against spoof attacks and making the system robust, can use the dimensionality of only 15 features to increase the accuracy of a hybrid multimodal biometric system to 100%, which shows a significant improvement compared with uni-biometric and other multimodal systems.</p><p><strong>Conclusion: </strong>The proposed method can be used to search large databases. Consequently, a large database of a secure multimodal template could be correctly differentiated based on the corresponding class of a test sample without any consistency error.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"12 3","pages":"177-191"},"PeriodicalIF":0.0,"publicationDate":"2022-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/5a/fb/JMSS-12-177.PMC9480510.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40368973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"¹⁸F-Fludeoxyglucose Absorbed Dose Estimation in Fetus during Early Pregnancy.","authors":"Nemat Ahmadi,&nbsp;Alireza Karimian,&nbsp;Mehdi Nasri Nasrabadi","doi":"10.4103/jmss.JMSS_70_20","DOIUrl":"https://doi.org/10.4103/jmss.JMSS_70_20","url":null,"abstract":"<p><p>The purpose of this study is to assess a rare case of fetal radiation absorbed dose here through ¹⁸F-Fludeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) in early pregnancy (5-week-old fetus). The fetal absorbed dose due to the radiation emitted from the mother's body, the fetus self-dose, and the dose received from CT were computed. The 35-year-old patient, weighing 85 kg, was injected with 370 MBq of ¹⁸F-FDG. Imaging started at 1 h with CT acquisition followed by PET imaging. The photon and positron self-dose was calculated by applying the Monte Carlo (MC) GATE (GEANT 4 Application for Tomographic Emission) code. The volume of absorbed dose from the mother's body organs and the absorbed dose from the CT were added to the self-dose to obtain the final dose. The volume of self-dose obtained through MC simulation for the fetus was 3.3 × 10^-2 mGy/MBq, of which 2.97 × 10^-2 mGy/MBq was associated with positrons and 0.33 × 10^-2 mGy/MBq was associated with photons. Biologically, the absorbed dose from CT, 7.3 mGy, had to be added to the total dose. The absorbed dose by the fetus during early pregnancy was higher than the standard value of 2.2 × 10^-2 mGy/MBq (MIRD DER) because, during the examinations, the mother's bladder was full. This issue was a concern during updating standards.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"12 2","pages":"171-175"},"PeriodicalIF":0.0,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/f8/37/JMSS-12-171.PMC9215831.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40401615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative Analysis of Inter- and Intrahemispheric Coherence on Epileptic Electroencephalography Signal.","authors":"Inung Wijayanto,&nbsp;Rudy Hartanto,&nbsp;Hanung Adi Nugroho","doi":"10.4103/jmss.JMSS_63_20","DOIUrl":"https://doi.org/10.4103/jmss.JMSS_63_20","url":null,"abstract":"<p><p>When an epileptic seizure occurs, the neuron's activity of the brain is dynamically changed, which affects the connectivity between brain regions. The connectivity of each brain region can be quantified by electroencephalography (EEG) coherence, which measures the statistical correlation between electrodes spatially separated on the scalp. Previous studies conducted a coherence analysis of all EEG electrodes covering all parts of the brain. However, in an epileptic condition, seizures occur in a specific region of the brain then spreading to other areas. Therefore, this study applies an energy-based channel selection process to determine the coherence analysis in the most active brain regions during the seizure. This paper presents a quantitative analysis of inter- and intrahemispheric coherence in epileptic EEG signals and the correlation with the channel activity to glean insights about brain area connectivity changes during epileptic seizures. The EEG signals are obtained from ten patients' data from the CHB-MIT dataset. Pair-wise electrode spectral coherence is calculated in the full band and five sub-bands of EEG signals. The channel activity level is determined by calculating the energy of each channel in all patients. The EEG coherence observation in the preictal (<i>Coh_pre</i> ) and ictal (<i>Coh_ictal</i> ) conditions showed a significant decrease of <i>Coh_ictal</i> in the most active channel, especially in the lower EEG sub-bands. This finding indicates that there is a strong correlation between the decrease of mean spectral coherence and channel activity. The decrease of coherence in epileptic conditions (<i>Coh_ictal</i> <<i>Coh_pre</i> ) indicates low neuronal connectivity. There are some exceptions in some channel pairs, but a constant pattern is found in the high activity channel. This shows a strong correlation between the decrease of coherence and the channel activity. The finding in this study demonstrates that the neuronal connectivity of epileptic EEG signals is suitable to be analyzed in the more active brain regions.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"12 2","pages":"145-154"},"PeriodicalIF":0.0,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/da/54/JMSS-12-145.PMC9215829.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40401607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electrodermal Activity for Measuring Cognitive and Emotional Stress Level.","authors":"Osmalina Nur Rahma,&nbsp;Alfian Pramudita Putra,&nbsp;Akif Rahmatillah,&nbsp;Yang Sa'ada Kamila Ariyansah Putri,&nbsp;Nuzula Dwi Fajriaty,&nbsp;Khusnul Ain,&nbsp;Rifai Chai","doi":"10.4103/jmss.JMSS_78_20","DOIUrl":"https://doi.org/10.4103/jmss.JMSS_78_20","url":null,"abstract":"<p><p>Stress can lead to harmful conditions in the body, such as anxiety disorders and depression. One of the promising noninvasive methods, which has been widely used in detecting stress and emotion, is electrodermal activity (EDA). EDA has a tonic and phasic component called skin conductance level and skin conductance response (SCR). However, the components of the EDA cannot be directly extracted and need to be deconvolved to obtain it. The EDA signals were collected from 18 healthy subjects that underwent three sessions - Stroop test with increasing stress levels. The EDA signals were then deconvoluted by using continuous deconvolution analysis (CDA) and convex optimization approach to electrodermal activity (cvxEDA). Four features from the result of the deconvolution process were collected, namely sample average, standard deviation, first absolute difference, and normalized first absolute difference. Those features were used as the input of the classification process using the extreme learning machine (ELM). The output of classification was the stress level; mild, moderate, and severe. The visual of the phasic component using cvxEDA is more precise or smoother than the CDA's result. However, both methods could separate SCR from the original skin conductivity raw and indicate the small peaks from the SCR. The classification process results showed that both CDA and cvxEDA methods with 50 hidden layers in ELM had a high accuracy in classifying the stress level, which was 95.56% and 94.45%, respectively. This study developed a stress level classification method using ELM and the statistical features of SCR. The result showed that EDA could classify the stress level with over 94% accuracy. This system could help people monitor their mental health during overworking, leading to anxiety and depression because of untreated stress.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"12 2","pages":"155-162"},"PeriodicalIF":0.0,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/e4/aa/JMSS-12-155.PMC9215837.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40401609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigation the Efficacy of Fuzzy Logic Implementation at Image-Guided Radiotherapy.","authors":"Ahmad Esmaili Torshabi","doi":"10.4103/jmss.JMSS_76_20","DOIUrl":"https://doi.org/10.4103/jmss.JMSS_76_20","url":null,"abstract":"<p><p>At image-guided radiotherapy, technique, different imaging, and monitoring systems are utilized for (i) organs border detection and tumor delineation during the treatment planning process and (ii) patient setup and tumor localization at pretreatment step and (iii) real-time tumor motion tracking for dynamic thorax tumors during the treatment. In this study, the effect of fuzzy logic is quantitatively investigated at different steps of image-guided radiotherapy. Fuzzy logic-based models and algorithms have been implemented at three steps, and the obtained results are compared with commonly available strategies. Required data are (i) real patients treated with Synchrony Cyberknife system at Georgetown University Hospital for real-time tumor motion prediction, (ii) computed tomography images taken from real patients for geometrical setup, and also (iii) tomography images of an anthropomorphic phantom for tumor delineation process. In real-time tumor tracking, the targeting error averages of the fuzzy correlation model in comparison with the Cyberknife modeler are 4.57 mm and 8.97 mm, respectively, for a given patient that shows remarkable error reduction. In the case of patient geometrical setup, the fuzzy logic-based algorithm has better influence in comparing with the artificial neural network, while the setup error averages is reduced from 1.47 to 0.4432 mm using the fuzzy logic-based method, for a given patient.Finally, the obtained results show that the fuzzy logic based image processing algorithm exhibits much better performance for edge detection compared to four conventional operators. This study is an effort to show that fuzzy logic based algorithms are also highly applicable at image-guided radiotherapy as one of the important treatment modalities for tumor delineation, patient setup error reduction, and intrafractional motion error compensation due to their inherent properties.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"12 2","pages":"163-170"},"PeriodicalIF":0.0,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/18/ff/JMSS-12-163.PMC9215832.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40401730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}