Journal of Medical Signals & Sensors最新文献

{"title":"Enhanced Joint Heart and Respiratory Rates Extraction from Functional Near-infrared Spectroscopy Signals Using Cumulative Curve Fitting Approximation.","authors":"Navid Adib, Seyed Kamaledin Setarehdan, Shirin Ashtari Tondashti, Mahdis Yaghoubi","doi":"10.4103/jmss.jmss_48_24","DOIUrl":"10.4103/jmss.jmss_48_24","url":null,"abstract":"<p><strong>Background: </strong>Functional near-infrared spectroscopy (fNIRS) is a valuable neuroimaging tool that captures cerebral hemodynamic during various brain tasks. However, fNIRS data usually suffer physiological artifacts. As a matter of fact, these physiological artifacts are rich in valuable physiological information.</p><p><strong>Methods: </strong>Leveraging this, our study presents a novel algorithm for extracting heart and respiratory rates (RRs) from fNIRS signals using a nonstationary, nonlinear filtering approach called cumulative curve fitting approximation. To enhance the accuracy of heart peak localization, a novel real-time method based on polynomial fitting was implemented, addressing the limitations of the 10 Hz temporal resolution in fNIRS. Simultaneous recordings of fNIRS, electrocardiogram (ECG), and respiration using a chest band strain gauge sensor were obtained from 15 subjects during a respiration task. Two-thirds of the subjects' data were used for the training procedure, employing a 5-fold cross-validation approach, while the remaining subjects were completely unseen and reserved for final testing.</p><p><strong>Results: </strong>The results demonstrated a strong correlation (<i>r</i> > 0.92, Bland-Altman Ratio <6%) between heart rate variability derived from fNIRS and ECG signals. Moreover, the low mean absolute error (0.18 s) in estimating the respiration period emphasizes the feasibility of the proposed method for RR estimation from fNIRS data. In addition, paired <i>t</i>-tests showed no significant difference between respiration rates estimated from the fNIRS-based measurements and those from the respiration sensor for each subject (<i>P</i> > 0.05).</p><p><strong>Conclusion: </strong>This study highlights fNIRS as a powerful tool for noninvasive extraction of heart and RRs alongside brain signals. The findings pave the way for developing lightweight, cost-effective wearable devices that can simultaneously monitor hemodynamic, heart, and respiratory activity, enhancing comfort and portability for health monitoring applications.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"15 ","pages":"15"},"PeriodicalIF":1.3,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12105807/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144152272","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":"Nonrigid Multimodal Registration Based on Fuzzy Inference System for Retinal Image Registration.","authors":"Monire Sheikh Hosseini, Hossein Rabbani","doi":"10.4103/jmss.jmss_42_24","DOIUrl":"10.4103/jmss.jmss_42_24","url":null,"abstract":"<p><strong>Background: </strong>Retinal imaging employs various modalities, each providing distinct perspectives on ocular structures. However, the integration of information from these modalities, which often have differing resolutions, requires effective image registration techniques. Existing retinal image registration methods typically rely on rigid or affine transformations, which may not adequately address the complexities of multimodal retinal images.</p><p><strong>Method: </strong>This study introduces a nonrigid fuzzy image registration approach designed to align optical coherence tomography (OCT) images with fundus images. The method employs a fuzzy inference system (FIS) that uses vessel locations as key features for registration. The FIS applies specific rules to map points from the source image to the reference image, facilitating accurate alignment.</p><p><strong>Results: </strong>The proposed method achieved a mean absolute registration error of 44.57 ± 39.38 µm in the superior-inferior orientation and 11.46 ± 10.06 µm in the nasal-temporal orientation. These results underscore the method's precision in aligning multimodal retinal images.</p><p><strong>Conclusion: </strong>The nonrigid fuzzy image registration approach demonstrates robust and versatile performance in integrating multimodal retinal imaging data. Despite its straightforward implementation, the method effectively addresses the challenges of multimodal retinal image registration, providing a reliable tool for advanced ocular imaging analysis.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"15 ","pages":"13"},"PeriodicalIF":1.3,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12105805/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144152274","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 Simulation of an Ultrahigh-resolution Spectral-domain Optical Coherence Tomography.","authors":"Mohammad Hossein Vafaie, Maryam Ansarian, Hossein Rabbani","doi":"10.4103/jmss.jmss_36_24","DOIUrl":"https://doi.org/10.4103/jmss.jmss_36_24","url":null,"abstract":"<p><strong>Background: </strong>Optical coherence tomography (OCT) is a biomedical imaging technique used to achieve high-resolution images from human tissues in a noninvasive manner.</p><p><strong>Methods: </strong>In this article, a practical approach is proposed for designing ultrahigh-resolution spectral-domain OCT (UHR SD-OCT) devices. At first, block diagram of a typical SD-OCT is introduced in detail. At second, internal components of each arm are introduced where the key parameters of each component are highlighted. At third, the effects of these key parameters on the overall performance of the UHR SD-OCT are investigated in a comprehensive manner. At fourth, the most important requirements of a UHR SD-OCT are explained, where suitable optical equipment is selected for each arm based on these requirements. At fifth, optical accessories as well as the electrical devices required for managing and control of the performance of a UHR SD-OCT are introduced in brief.</p><p><strong>Results: </strong>Performance of the proposed device is assessed through various simulations, and finally, the implementation cost and implementation challenges are investigated in detail.</p><p><strong>Conclusions: </strong>Simulation results indicate that the proposed UHR SD-OCT has acceptable axial resolution and imaging depth; hence, it is a good candidate for use in retinal applications that require UHR imaging.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"15 ","pages":"12"},"PeriodicalIF":1.3,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12063968/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144019120","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":"Multi-classification Deep Learning Approach for Diagnosing Stroke Type and Severity Using Multimodal Magnetic Resonance Images.","authors":"Sahar Felehgari, Payam Sariaslani, Sepideh Shamsizadeh, Saba Felehgari, Anahita Rajabi, Hiwa Mohammadi","doi":"10.4103/jmss.jmss_37_24","DOIUrl":"https://doi.org/10.4103/jmss.jmss_37_24","url":null,"abstract":"<p><strong>Background: </strong>Clinical decisions for stroke treatments, such as thrombolytic drugs for ischemic strokes or anticoagulants for hemorrhagic strokes, rely on accurate diagnosis and severity assessment. Our study uses diffusion-weighted magnetic resonance imaging and Convolutional Neural Networks (CNNs) to differentiate healthy and stroke samples, classify stroke types, and predict severity, aiding in decision-making for stroke management.</p><p><strong>Methods: </strong>We evaluated 143 patients: 85 with ischemic stroke and 58 with hemorrhagic stroke. For stroke diagnosis, we compared multimodal (apparent diffusion coefficient and diffusion-weighted imaging [DWI]) and single-modal (using separate images) preprocessing techniques. Our study introduced two models, Added CNN Layer-ResNet-50 (ACL-ResNet-50) and Added CNN Layer-MobileNetV1 (ACL-MobileNetV1), based on transfer learning (MobileNetV1 and ResNet-50), enhancing performance through reinforced layers. We compared our proposed models with a scenario in which only the final layer was replaced in ResNet-50 and MobileNetV1. Furthermore, we predicted National Institutes of Health Stroke Scale (NIHSS) scores in three ranges based on DWI images to gauge stroke severity. Evaluation criteria for the models included accuracy, sensitivity, specificity, and area under the curve (AUC).</p><p><strong>Results: </strong>In stroke classification (normal, ischemic, and hemorrhagic), ACL-MobileNetV1 outperformed other models, achieving 98% accuracy, 99% sensitivity, 98% specificity, and 99% AUC. For assessing ischemic stroke severity using NIHSS ranges, ACL-ResNet-50 showed the optimal performance with an accuracy of 0.92, sensitivity of 0.84, specificity of 0.92, and AUC of 0.95.</p><p><strong>Conclusion: </strong>Our study's proposed method effectively classified stroke type and severity based on multimodal MR images, potentially as a practical decision support tool for stroke treatments.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"15 ","pages":"10"},"PeriodicalIF":1.3,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12063969/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143989798","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":"Two Machine-learning Hybrid Models for Predicting Type 2 Diabetes Mellitus.","authors":"Rahman Farnoosh, Karlo Abnoosian, Rasha Abbas Isewid","doi":"10.4103/jmss.jmss_29_24","DOIUrl":"https://doi.org/10.4103/jmss.jmss_29_24","url":null,"abstract":"<p><strong>Background: </strong>The global increase in diabetes prevalence necessitates advanced diagnostic methods. Machine learning has shown promise in disease diagnosis, including diabetes.</p><p><strong>Materials and methods: </strong>We used a dataset collected from the Medical City Hospital laboratory and the Specialized Center for Endocrinology and Diabetes at Al-Kindy Teaching Hospital in Iraq. This dataset includes 1000 physical examination samples from both male and female patients. The samples are categorized into three classes: diabetic (Y), nondiabetic (N), and predicted diabetic (P). The dataset contains twelve attributes and includes outlier data. Outliers in medical studies can result from unusual disease attributes. Therefore, consulting with a specialist physician to identify and handle these outliers using statistical methods is necessary. The main contribution of this study is the proposal of two hybrid models for diabetes diagnosis in two scenarios: (1) Scenario 1 (presence of outlier data): Hybrid Model 1 combines the K-medoids clustering algorithm with a Gaussian naive Bayes (GNB) classifier based on kernel density estimation (KDE) to handle outliers and (2) Scenario 2 (after removing outlier data): Hybrid Model 2 combines the K-means clustering algorithm with a GNB classifier based on KDE with suitable bandwidth. We performed principal component analysis to minimize dimensionality and evaluated the models using fivefold cross-validation.</p><p><strong>Results: </strong>All experiments were conducted in identical settings. Our proposed hybrid models demonstrated superior performance in two scenarios, handling and rejecting outliers, compared to other machine-learning models in this study, including support vector machines (with radial-based, polynomial, linear, and sigmoid kernel functions), decision trees (J48), and GNB classifiers for diabetes prediction. The average accuracy for Scenario 1 with Hybrid Model 1 was 0.9743, and for Scenario 2 with Hybrid Model 2, it was 0.9867. We also evaluated precision, sensitivity, and F1-score as performance metrics.</p><p><strong>Conclusion: </strong>This study presents two hybrid models for diabetes diagnosis, demonstrating high accuracy in distinguishing between diabetic and nondiabetic patients and effectively handling outliers. The findings highlight the potential of machine-learning techniques for improving the early diagnosis and treatment of diabetes.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"15 ","pages":"11"},"PeriodicalIF":1.3,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12063970/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143989751","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":"Graphs Constructed from Instantaneous Amplitude and Phase of Electroencephalogram Successfully Differentiate Motor Imagery Tasks.","authors":"Maliheh Miri, Vahid Abootalebi, Hamid Saeedi-Sourck, Dimitri Van De Ville, Hamid Behjat","doi":"10.4103/jmss.jmss_63_24","DOIUrl":"10.4103/jmss.jmss_63_24","url":null,"abstract":"<p><strong>Background: </strong>Accurate classification of electroencephalogram (EEG) signals is challenging given the nonlinear and nonstationary nature of the data as well as subject-dependent variations. Graph signal processing (GSP) has shown promising results in the analysis of brain imaging data.</p><p><strong>Methods: </strong>In this article, a GSP-based approach is presented that exploits instantaneous amplitude and phase coupling between EEG time series to decode motor imagery (MI) tasks. A graph spectral representation of the Hilbert-transformed EEG signals is obtained, in which simultaneous diagonalization of covariance matrices provides the basis of a subspace that differentiates two classes of right hand and right foot MI tasks. To determine the most discriminative subspace, an exploratory analysis was conducted in the spectral domain of the graphs by ranking the graph frequency components using a feature selection method. The selected features are fed into a binary support vector machine that predicts the label of the test trials.</p><p><strong>Results: </strong>The performance of the proposed approach was evaluated on brain-computer interface competition III (IVa) dataset.</p><p><strong>Conclusions: </strong>Experimental results reflect that brain functional connectivity graphs derived using the instantaneous amplitude and phase of the EEG signals show comparable performance with the best results reported on these data in the literature, indicating the efficiency of the proposed method compared to the state-of-the-art methods.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"15 ","pages":"7"},"PeriodicalIF":1.3,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11970835/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143796329","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":"Rifampin-loaded Mesoporous Silica Nanoparticles Improved Physical and Mechanical Properties and Biological Response of Acrylic Bone Cement.","authors":"Mohammad Reza Shafiei, Nader Nezafati, Saeed Karbasi, Anousheh Zargar Kharazi","doi":"10.4103/jmss.jmss_52_24","DOIUrl":"10.4103/jmss.jmss_52_24","url":null,"abstract":"<p><strong>Background: </strong>Acrylic bone cement, which is used to fix implants in the knee and hip, is prone to contamination with various types of infections. Adding small amounts of different antibiotics to the cement can help prevent and treat infections. Rifampin antibiotic has been added to bone cement to create an appropriate antimicrobial response in the treatment of resistant coagulase-negative staphylococci (CoNS) biofilms, but there are some challenges such as reducing mechanical properties and prolonging the setting time of the cement. Loading the antibiotic in the nanoparticle could eliminate these challenges.</p><p><strong>Methods: </strong>In this study, rifampin-loaded mesoporous silica nanoparticles (MSNs) were added to bone cement, and the polymerization components, mechanical properties, drug release, antibacterial activity, and cellular response were investigated and compared with commercial pure cement and the cement containing free rifampin.</p><p><strong>Results: </strong>Loading rifampin into MSN improved compressive strength by 57.52%. Cement containing rifampin loaded into MSN showed remarkable success in antibacterial activity. The growth inhibition zone created by it in the culture medium of <i>Staphylococcus aureus</i> and CoNS was 15.44% and 11.8% greater, respectively, than in the cement containing free rifampin. In other words, according to the results of spectrophotometric analysis of cement samples over 5 weeks, MSNs caused a 33.2 ± 0.21-fold increase in rifampin washout from the cement. Cellular examination of the cement containing rifampin loaded into MSN compared to commercial pure cement showed an acceptable level of cell viability.</p><p><strong>Conclusion: </strong>Rifampin loading in MSN limited the reduction of cement strength. It also improved the drug release pattern and prevented antibiotic resistance.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"15 ","pages":"9"},"PeriodicalIF":1.3,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11970834/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143796348","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 Severe Knee Arthritis Based on Two Inertial Measurement Unit Sensors as a Dynamic Coordinate System Using Classical Machine Learning.","authors":"Erfan Azizi, Mohammadsadegh Darbankhalesi, Amirhossein Zare, Zahra Sadat Rezaeian, Saeed Kermani","doi":"10.4103/jmss.jmss_18_24","DOIUrl":"10.4103/jmss.jmss_18_24","url":null,"abstract":"<p><strong>Background: </strong>Aging of societies in recent and upcoming years has made musculoskeletal disorders a significant challenge for healthcare system. Knee osteoarthritis (KOA) is a progressive musculoskeletal disorder that is typically diagnosed using radiographs. Considering the drawbacks of X-ray imaging, such as exposure to ionizing radiation, the need for a noninvasive, low-cost alternative method for diagnosing KOA is essential. The purpose of this study was to evaluate the ability of a wearable device to differentiate between healthy individuals and those with severe osteoarthritis (grade 4).</p><p><strong>Methods: </strong>The wearable device consisted of two inertial measurement unit (IMU) sensors, one on the lower leg and one on the thigh. One of the sensors is used as a dynamic coordinate system to improve the accuracy of the measurements. In this study, to discriminate between 1433 labeled IMU signals collected from 15 healthy individuals and 15 people with severe KOA aged over 45, new features were extracted and defined in dynamic coordinates. These features were employed in four different classifiers: (1) naive Bayes, (2) K-nearest neighbors (KNNs), (3) support vector machine, and (4) random forest. Each classifier was evaluated using the 10-fold cross-validation method (<i>K</i> = 10). The data were applied to these models, and based on their outputs, four performance metrics - accuracy, precision, sensitivity, and specificity - were calculated to assess the classification of these two groups using the mentioned software.</p><p><strong>Results: </strong>The evaluation of the selected classifiers involved calculating the four specified metrics and their average and variance values. The highest accuracy was achieved by KNN, with an accuracy of 93.71 ± 1.1 and a precision of 93 ± 1.31.</p><p><strong>Conclusion: </strong>The novel features based on the dynamic coordinate system, along with the success of the proposed KNN model, demonstrate the effectiveness of the proposed algorithm in diagnosing between signals received from healthy individuals and patients. The proposed algorithm outperforms existing methods in similar articles in sensitivity showing an improvement of 4% and at least. The main objective of this study is to investigate the feasibility of using a wearable device as an auxiliary tool in the diagnosis of arthritis. The reported results in this study are related to two groups of individuals with severe arthritis (grade 4), and there is a possibility of weaker results with the current method.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"15 ","pages":"8"},"PeriodicalIF":1.3,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11970831/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143796343","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":"Isfahan Artificial Intelligence Event 2023: Lesion Segmentation and Localization in Magnetic Resonance Images of Patients with Multiple Sclerosis.","authors":"Fariba Davanian, Iman Adibi, Mahnoosh Tajmirriahi, Maryam Monemian, Zahra Zojaji, Ahmadreza Montazerolghaem, Mohammad Amin Asadinia, Seyed Mojtaba Mirghaderi, Seyed Amin Naji Esfahani, Mohammad Kazemi, Mohammad Reza Iravani, Kian Shahriari, Nesa Sharifi, Sadaf Moharreri, Farnaz Sedighin, Hossein Rabbani","doi":"10.4103/jmss.jmss_55_24","DOIUrl":"10.4103/jmss.jmss_55_24","url":null,"abstract":"<p><strong>Background: </strong>Multiple sclerosis (MS) is one of the most common reasons of neurological disabilities in young adults. The disease occurs when the immune system attacks the central nervous system and destroys the myelin of nervous cells. This results in appearing several lesions in the magnetic resonance (MR) images of patients. Accurate determination of the amount and the place of lesions can help physicians to determine the severity and progress of the disease.</p><p><strong>Method: </strong>Due to the importance of this issue, this challenge has been dedicated to the segmentation and localization of lesions in MR images of patients with MS. The goal was to segment and localize the lesions in the flair MR images of patients as close as possible to the ground truth masks.</p><p><strong>Results: </strong>Several teams sent us their results for the segmentation and localization of lesions in MR images. Most of the teams preferred to use deep learning methods. The methods varied from a simple U-net structure to more complicated networks.</p><p><strong>Conclusion: </strong>The results show that deep learning methods can be useful for segmentation and localization of lesions in MR images. In this study, we briefly described the dataset and the methods of teams attending the competition.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"15 ","pages":"5"},"PeriodicalIF":1.8,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11970832/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143796300","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":"Isfahan Artificial Intelligence Event 2023: Reflux Detection Competition.","authors":"Azra Rasouli Kenari, Ahmadreza Montazerolghaem, Zahra Zojaji, Mehdi Ghatee, Behnam Yousefimehr, Amin Rahmani, Mahdi Kalani, Farnoush Kiyanpour, Mohamad Kiani-Abari, Mohammad Yasin Fakhar, Safiyeh Rezaei, Mojtaba Tahernia, Mohammad Hossein Vafaie, Hamidreza Besharatnezhad, Vahid Rahimi Bafrani, Mohamad Taghi Tofighi, Peyman Adibi Sedeh, Maryam Soheilipour, Hossein Rabbani","doi":"10.4103/jmss.jmss_46_24","DOIUrl":"10.4103/jmss.jmss_46_24","url":null,"abstract":"<p><strong>Background: </strong>Gastroesophageal reflux disease (GERD) is a prevalent digestive disorder that impacts millions of individuals globally. Multichannel intraluminal impedance-pH (MII-pH) monitoring represents a novel technique and currently stands as the gold standard for diagnosing GERD. Accurately characterizing reflux events from MII data are crucial for GERD diagnosis. Despite the initial introduction of clinical literature toward software advancements several years ago, the reliable extraction of reflux events from MII data continues to pose a significant challenge. Achieving success necessitates the seamless collaboration of two key components: a reflux definition criteria protocol established by gastrointestinal experts and a comprehensive analysis of MII data for reflux detection.</p><p><strong>Method: </strong>In an endeavor to address this challenge, our team assembled a dataset comprising 201 MII episodes. We meticulously crafted precise reflux episode definition criteria, establishing the gold standard and labels for MII data.</p><p><strong>Result: </strong>A variety of signal-analyzing methods should be explored. The first Isfahan Artificial Intelligence Competition in 2023 featured formal assessments of alternative methodologies across six distinct domains, including MII data evaluations.</p><p><strong>Discussion: </strong>This article outlines the datasets provided to participants and offers an overview of the competition results.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"15 ","pages":"6"},"PeriodicalIF":1.3,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11970833/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143796337","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}