Journal of Medical Signals & Sensors最新文献

{"title":"An Emotion Recognition Embedded System using a Lightweight Deep Learning Model.","authors":"Mehdi Bazargani,&nbsp;Amir Tahmasebi,&nbsp;Mohammadreza Yazdchi,&nbsp;Zahra Baharlouei","doi":"10.4103/jmss.jmss_59_22","DOIUrl":"10.4103/jmss.jmss_59_22","url":null,"abstract":"<p><strong>Background: </strong>Diagnosing emotional states would improve human-computer interaction (HCI) systems to be more effective in practice. Correlations between Electroencephalography (EEG) signals and emotions have been shown in various research; therefore, EEG signal-based methods are the most accurate and informative.</p><p><strong>Methods: </strong>In this study, three Convolutional Neural Network (CNN) models, EEGNet, ShallowConvNet and DeepConvNet, which are appropriate for processing EEG signals, are applied to diagnose emotions. We use baseline removal preprocessing to improve classification accuracy. Each network is assessed in two setting ways: subject-dependent and subject-independent. We improve the selected CNN model to be lightweight and implementable on a Raspberry Pi processor. The emotional states are recognized for every three-second epoch of received signals on the embedded system, which can be applied in real-time usage in practice.</p><p><strong>Results: </strong>Average classification accuracies of 99.10% in the valence and 99.20% in the arousal for subject-dependent and 90.76% in the valence and 90.94% in the arousal for subject independent were achieved on the well-known DEAP dataset.</p><p><strong>Conclusion: </strong>Comparison of the results with the related works shows that a highly accurate and implementable model has been achieved for practice.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"13 4","pages":"272-279"},"PeriodicalIF":0.0,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/70/b9/JMSS-13-272.PMC10559299.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41161330","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":"Evaluating the Gray Level Co-Occurrence Matrix-Based Texture Features of Magnetic Resonance Images for Glioblastoma Multiform Patients' Treatment Response Assessment.","authors":"Sanaz Alibabaei,&nbsp;Masoumeh Rahmani,&nbsp;Marziyeh Tahmasbi,&nbsp;Mohammad Javad Tahmasebi Birgani,&nbsp;Sasan Razmjoo","doi":"10.4103/jmss.jmss_50_22","DOIUrl":"10.4103/jmss.jmss_50_22","url":null,"abstract":"<p><strong>Background: </strong>Medical images of cancer patients are usually evaluated qualitatively by clinical specialists which makes the accuracy of the diagnosis subjective and related to the skills of clinicians. Quantitative methods based on the textural feature analysis may be useful to facilitate such evaluations. This study aimed to analyze the gray level co-occurrence matrix (GLCM)-based texture features extracted from T1-axial magnetic resonance (MR) images of glioblastoma multiform (GBM) patients to determine the distinctive features specific to treatment response or disease progression.</p><p><strong>Methods: </strong>20 GLCM-based texture features, in addition to mean, standard deviation, entropy, RMS, kurtosis, and skewness were extracted from step I MR images (obtained 72 h after surgery) and step II MR images (obtained three months later). Responded and not responded patients to treatment were classified manually based on the radiological evaluation of step II images. Extracted texture features from Step I and Step II images were analyzed to determine the distinctive features for each group of responsive or progressive diseases. MATLAB 2020 was applied to feature extraction. SPSS version 26 was used for the statistical analysis. <i>P</i> value < 0.05 was considered statistically significant.</p><p><strong>Results: </strong>Despite no statistically significant differences between Step I texture features for two considered groups, almost all step II extracted GLCM-based texture features in addition to entropy M and skewness were significantly different between responsive and progressive disease groups.</p><p><strong>Conclusions: </strong>GLCM-based texture features extracted from MR images of GBM patients can be used with automatic algorithms for the expeditious prediction or interpretation of response to the treatment quantitatively besides qualitative evaluations.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"13 4","pages":"261-271"},"PeriodicalIF":0.0,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/5a/07/JMSS-13-261.PMC10559301.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41104404","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":"Application of Functional Near-Infrared Spectroscopy in Apraxia Studies in Alzheimer's Disease: A Proof of Concept Experiment.","authors":"Kiarash Azimzadeh,&nbsp;Majid Barekatain,&nbsp;Farinaz Tabibian","doi":"10.4103/jmss.jmss_40_22","DOIUrl":"10.4103/jmss.jmss_40_22","url":null,"abstract":"obtained. A continuous‐wave OxyMon fNIRS system (Artinis Medical Systems, Netherlands) with 28 active channels and a 10 Hz sampling rate was used. Measured wavelengths were 762 and 845 nm. Based on previous findings, the middle and superior parts of the temporal lobe, inferior and superior parts of the parietal lobe, and superior, middle, and inferior parts of the frontal lobe were selected as regions of interest.[9‐11] Location of optodes was determined using fNIRS Optodes’ Location Decider[12] and the most similar template was selected [Figure 2]. Raw data were processed using Homer3 in MATLAB 2021a (MathWorks, Natick, MA, USA).[13] After the conversion of light intensity signals to an optical density (OD), a bandpass filter of 0.01–0.1 Hz was applied and targeted principle component analysis was performed.[14] Changes in OD were then converted to concentration changes using modified Beer–Lambert Law.[15] Concentration changes within a period of‐2s before stimulus onset to 60s after stimulus onset (2s for baseline, 35s for five stimuli, and 23s for return to baseline) were averaged to obtain the hemodynamic response functions (HRF) during the task. Next, the HRF from channels within one region of interest (ROI) was averaged. Figures 3 and 4 demonstrate the HRF during the task. Overall, this experiment suggests that fNIRS can be used to study apraxia, especially in elderly patients with neurodegenerative diseases.","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"13 4","pages":"319-322"},"PeriodicalIF":0.0,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/6d/29/JMSS-13-319.PMC10559297.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41162029","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":"Loss-Modified Transformer-Based U-Net for Accurate Segmentation of Fluids in Optical Coherence Tomography Images of Retinal Diseases.","authors":"Reza Darooei,&nbsp;Milad Nazari,&nbsp;Rahle Kafieh,&nbsp;Hossein Rabbani","doi":"10.4103/jmss.jmss_52_22","DOIUrl":"10.4103/jmss.jmss_52_22","url":null,"abstract":"<p><strong>Background: </strong>Optical coherence tomography (OCT) imaging significantly contributes to ophthalmology in the diagnosis of retinal disorders such as age-related macular degeneration and diabetic macular edema. Both diseases involve the abnormal accumulation of fluids, location, and volume, which is vitally informative in detecting the severity of the diseases. Automated and accurate fluid segmentation in OCT images could potentially improve the current clinical diagnosis. This becomes more important by considering the limitations of manual fluid segmentation as a time-consuming and subjective to error method.</p><p><strong>Methods: </strong>Deep learning techniques have been applied to various image processing tasks, and their performance has already been explored in the segmentation of fluids in OCTs. This article suggests a novel automated deep learning method utilizing the U-Net structure as the basis. The modifications consist of the application of transformers in the encoder path of the U-Net with the purpose of more concentrated feature extraction. Furthermore, a custom loss function is empirically tailored to efficiently incorporate proper loss functions to deal with the imbalance and noisy images. A weighted combination of Dice loss, focal Tversky loss, and weighted binary cross-entropy is employed.</p><p><strong>Results: </strong>Different metrics are calculated. The results show high accuracy (Dice coefficient of 95.52) and robustness of the proposed method in comparison to different methods after adding extra noise to the images (Dice coefficient of 92.79).</p><p><strong>Conclusions: </strong>The segmentation of fluid regions in retinal OCT images is critical because it assists clinicians in diagnosing macular edema and executing therapeutic operations more quickly. This study suggests a deep learning framework and novel loss function for automated fluid segmentation of retinal OCT images with excellent accuracy and rapid convergence result.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"13 4","pages":"253-260"},"PeriodicalIF":0.0,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/2b/d1/JMSS-13-253.PMC10559298.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41153169","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":"Design and Implementation of a Smart Wireless Controlled Visual Acuity Measurement System.","authors":"Mohammad Hossein Vafaie,&nbsp;Ebrahim Ahmadi Beni","doi":"10.4103/jmss.jmss_38_22","DOIUrl":"10.4103/jmss.jmss_38_22","url":null,"abstract":"<p><p>In this article, a smart visual acuity measurement (VAM) system is designed and implemented. Hardware of the proposed VAM system consists of two parts: a wireless remote controller, and a high-resolution LCD controlled through a Raspberry-Pi mini-computer. In the remote controller, a 3.5\" graphical LCD with a touch screen is used as a human-machine interface. When a point is pressed on the touch screen, the unique identifier (ID) code of that point as well as its page number is transmitted to the Raspberry-Pi. In the Raspberry-Pi, data are received and processed by a smart application coded in visual studio software. Then, the commanded tasks are executed by the Raspberry-Pi's operating system. Numerous charts, characters, and pictures are stored in the proposed VAM system to provide various VAM options while the size of the optotypes is adjusted automatically based on the distance of the patient from the LCD. The performance of the proposed VAM system is examined practically under the supervision of an expert optometrist where the results indicate that visual acuity, astigmatism, and color blindness of patients can be examined precisely through the proposed VAM system in an easier and more comfortable manner.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"13 4","pages":"307-318"},"PeriodicalIF":0.0,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/2e/69/JMSS-13-307.PMC10559300.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41152725","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":"Pediatric effective dose assessment for routine computed tomography examinations in Tehran, Iran.","authors":"Atefeh Tahmasebzadeh,&nbsp;Asghar Maziyar,&nbsp;Reza Reiazi,&nbsp;Mojtaba Soltani Kermanshahi,&nbsp;Seyyed Hossein Mousavie Anijdan,&nbsp;Reza Paydar","doi":"10.4103/jmss.jmss_115_21","DOIUrl":"https://doi.org/10.4103/jmss.jmss_115_21","url":null,"abstract":"<p><strong>Background: </strong>The purpose of this study is to evaluate the effective dose (ED) for computed tomography (CT) examination in different age groups and medical exposure in pediatric imaging centers in Tehran, Iran.</p><p><strong>Methods: </strong>Imaging data were collected from 532 pediatric patients from four age groups subjected to three prevalent procedures. National Cancer Institute CT (NCICT) software was used to calculate the ED value.</p><p><strong>Results: </strong>The mean ED values were 1.60, 4.16, and 10.56 mSv for patients' procedures of head, chest, and abdomen-pelvis, respectively. This study showed a significant difference of ED value among five pediatric medical imaging centers (<i>P</i> < 0.05). In head, chest, and abdomen-pelvis exams, a reduction in ED was evident with decreasing patients' age.</p><p><strong>Conclusion: </strong>As there were significant differences among ED values in five pediatric medical imaging centers, optimizing this value is necessary to decrease this variation. For head CT in infants and also abdomen-pelvis, further reduction in radiation exposure is required.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"12 3","pages":"227-232"},"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/db/61/JMSS-12-227.PMC9480506.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40369391","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":"Detection of ADHD From EOG Signals Using Approximate Entropy and Petrosain's Fractal Dimension.","authors":"Nasrin Sho'ouri","doi":"10.4103/jmss.jmss_119_21","DOIUrl":"https://doi.org/10.4103/jmss.jmss_119_21","url":null,"abstract":"<p><strong>Background: </strong>Previous research has shown that eye movements are different in patients with attention deficit hyperactivity disorder (ADHD) and healthy people. As a result, electrooculogram (EOG) signals may also differ between the two groups. Therefore, the aim of this study was to investigate the recorded EOG signals of 30 ADHD children and 30 healthy children (control group) while performing an attention-related task.</p><p><strong>Methods: </strong>Two features of approximate entropy (ApEn) and Petrosian's fractal dimension (Pet's FD) of EOG signals were calculated for the two groups. Then, the two groups were classified using the vector derived from two features and two support vector machine (SVM) and neural gas (NG) classifiers.</p><p><strong>Results: </strong>Statistical analysis showed that the values of both features were significantly lower in the ADHD group compared to the control group. Moreover, the SVM classifier (accuracy: 84.6% ± 4.4%, sensitivity: 85.2% ± 4.9%, specificity: 78.8% ± 6.5%) was more successful in separating the two groups than the NG (78.1% ± 1.1%, sensitivity: 80.1% ± 6.2%, specificity: 72.2% ± 9.2%).</p><p><strong>Conclusion: </strong>The decrease in ApEn and Pet's FD values in the EOG signals of the ADHD group showed that their eye movements were slower than the control group and this difference was due to their attention deficit. The results of this study can be used to design an EOG biofeedback training course to reduce the symptoms of ADHD patients.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"12 3","pages":"254-262"},"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/b6/74/JMSS-12-254.PMC9480511.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40368974","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":"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}