Journal of Medical Signals & Sensors最新文献

{"title":"Transfer Learning with Pretrained Convolutional Neural Network for Automated Gleason Grading of Prostate Cancer Tissue Microarrays.","authors":"Parisa Gifani, Ahmad Shalbaf","doi":"10.4103/jmss.jmss_42_22","DOIUrl":"10.4103/jmss.jmss_42_22","url":null,"abstract":"<p><strong>Background: </strong>The Gleason grading system has been the most effective prediction for prostate cancer patients. This grading system provides this possibility to assess prostate cancer's aggressiveness and then constitutes an important factor for stratification and therapeutic decisions. However, determining Gleason grade requires highly-trained pathologists and is time-consuming and tedious, and suffers from inter-pathologist variability. To remedy these limitations, this paper introduces an automatic methodology based on transfer learning with pretrained convolutional neural networks (CNNs) for automatic Gleason grading of prostate cancer tissue microarray (TMA).</p><p><strong>Methods: </strong>Fifteen pretrained (CNNs): Efficient Nets (B0-B5), NasNetLarge, NasNetMobile, InceptionV3, ResNet-50, SeResnet 50, Xception, DenseNet121, ResNext50, and inception_resnet_v2 were fine-tuned on a dataset of prostate carcinoma TMA images. Six pathologists separately identified benign and cancerous areas for each prostate TMA image by allocating benign, 3, 4, or 5 Gleason grade for 244 patients. The dataset was labeled by these pathologists and majority vote was applied on pixel-wise annotations to obtain a unified label.</p><p><strong>Results: </strong>Results showed the NasnetLarge architecture is the best model among them in the classification of prostate TMA images of 244 patients with accuracy of 0.93 and area under the curve of 0.98.</p><p><strong>Conclusion: </strong>Our study can act as a highly trained pathologist to categorize the prostate cancer stages with more objective and reproducible results.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"14 ","pages":"4"},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10950311/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140176925","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":"Super-resolution of Retinal Optical Coherence Tomography Images Using Statistical Modeling.","authors":"Sahar Jorjandi, Zahra Amini, Hossein Rabbani","doi":"10.4103/jmss.jmss_58_22","DOIUrl":"10.4103/jmss.jmss_58_22","url":null,"abstract":"<p><strong>Background: </strong>Optical coherence tomography (OCT) imaging has emerged as a promising diagnostic tool, especially in ophthalmology. However, speckle noise and downsampling significantly degrade the quality of OCT images and hinder the development of OCT-assisted diagnostics. In this article, we address the super-resolution (SR) problem of retinal OCT images using a statistical modeling point of view.</p><p><strong>Methods: </strong>In the first step, we utilized Weibull mixture model (WMM) as a comprehensive model to establish the specific features of the intensity distribution of retinal OCT data, such as asymmetry and heavy tailed. To fit the WMM to the low-resolution OCT images, expectation-maximization algorithm is used to estimate the parameters of the model. Then, to reduce the existing noise in the data, a combination of Gaussian transform and spatially constraint Gaussian mixture model is applied. Now, to super-resolve OCT images, the expected patch log-likelihood is used which is a patch-based algorithm with multivariate GMM prior assumption. It restores the high-resolution (HR) images with maximum a posteriori (MAP) estimator.</p><p><strong>Results: </strong>The proposed method is compared with some well-known super-resolution algorithms visually and numerically. In terms of the mean-to-standard deviation ratio (MSR) and the equivalent number of looks, our method makes a great superiority compared to the other competitors.</p><p><strong>Conclusion: </strong>The proposed method is simple and does not require any special preprocessing or measurements. The results illustrate that our method not only significantly suppresses the noise but also successfully reconstructs the image, leading to improved visual quality.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"14 ","pages":"2"},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10950312/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140176923","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":"Tensor Ring Based Image Enhancement.","authors":"Farnaz Sedighin","doi":"10.4103/jmss.jmss_32_23","DOIUrl":"10.4103/jmss.jmss_32_23","url":null,"abstract":"<p><strong>Background: </strong>Image enhancement, including image de-noising, super-resolution, registration, reconstruction, in-painting, and so on, is an important issue in different research areas. Different methods which have been exploited for image analysis were mostly based on matrix or low order analysis. However, recent researches show the superior power of tensor-based methods for image enhancement.</p><p><strong>Method: </strong>In this article, a new method for image super-resolution using Tensor Ring decomposition has been proposed. The proposed image super-resolution technique has been derived for the super-resolution of low resolution and noisy images. The new approach is based on a modification and extension of previous tensor-based approaches used for super-resolution of datasets. In this method, a weighted combination of the original and the resulting image of the previous stage has been computed and used to provide a new input to the algorithm.</p><p><strong>Result: </strong>This enables the method to do the super-resolution and de-noising simultaneously.</p><p><strong>Conclusion: </strong>Simulation results show the effectiveness of the proposed approach, especially in highly noisy situations.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"14 ","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10950313/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140176924","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":"Erratum: Investigation of Electrical Signals in the Brain of People with Autism Using Effective Connectivity Network.","authors":"","doi":"10.4103/jmss.jmss_12_25","DOIUrl":"https://doi.org/10.4103/jmss.jmss_12_25","url":null,"abstract":"<p><p>[This corrects the article 24 in vol. 14, PMID: 39234588.].</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"15 ","pages":"4"},"PeriodicalIF":1.3,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11870323/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143543754","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: Macular Pathology Detection Competition.","authors":"Farnaz Sedighin, Maryam Monemian, Zahra Zojaji, Ahmadreza Montazerolghaem, Mohammad Amin Asadinia, Seyed Mojtaba Mirghaderi, Seyed Amin Naji Esfahani, Mohammad Kazemi, Reza Mokhtari, Maryam Mohammadi, Mohadese Ramezani, Mahnoosh Tajmirriahi, Hossein Rabbani","doi":"10.4103/jmss.jmss_47_24","DOIUrl":"https://doi.org/10.4103/jmss.jmss_47_24","url":null,"abstract":"<p><strong>Background: </strong>Computer-aided diagnosis (CAD) methods have become of great interest for diagnosing macular diseases over the past few decades. Artificial intelligence (AI)-based CADs offer several benefits, including speed, objectivity, and thoroughness. They are utilized as an assistance system in various ways, such as highlighting relevant disease indicators to doctors, providing diagnosis suggestions, and presenting similar past cases for comparison.</p><p><strong>Methods: </strong>Much specifically, retinal AI-CADs have been developed to assist ophthalmologists in analyzing optical coherence tomography (OCT) images and making retinal diagnostics simpler and more accurate than before. Retinal AI-CAD technology could provide a new insight for the health care of humans who do not have access to a specialist doctor. AI-based classification methods are critical tools in developing improved retinal AI-CAD technology. The Isfahan AI-2023 challenge has organized a competition to provide objective formal evaluations of alternative tools in this area. In this study, we describe the challenge and those methods that had the most successful algorithms.</p><p><strong>Results: </strong>A dataset of OCT images, acquired from normal subjects, patients with diabetic macular edema, and patients with other macular disorders, was provided in a documented format. The dataset, including the labeled training set and unlabeled test set, was made accessible to the participants. The aim of this challenge was to maximize the performance measures for the test labels. Researchers tested their algorithms and competed for the best classification results.</p><p><strong>Conclusions: </strong>The competition is organized to evaluate the current AI-based classification methods in macular pathology detection. We received several submissions to our posted datasets that indicate the growing interest in AI-CAD technology. The results demonstrated that deep learning-based methods can learn essential features of pathologic images, but much care has to be taken in choosing and adapting appropriate models for imbalanced small datasets.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"15 ","pages":"3"},"PeriodicalIF":1.3,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11870325/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143542876","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 Intelligent 2023 Competitions.","authors":"Samaneh Ghasemi, Zahra Baharlouei, Hossein Rabbani","doi":"10.4103/jmss.jmss_78_24","DOIUrl":"https://doi.org/10.4103/jmss.jmss_78_24","url":null,"abstract":"","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"15 ","pages":"1"},"PeriodicalIF":1.3,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11870326/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143543138","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: Drug Demand Forecasting.","authors":"Meysam Jahani, Zahra Zojaji, AhmadReza Montazerolghaem, Maziar Palhang, Reza Ramezani, Ahmadreza Golkarnoor, Alireza Akhavan Safaei, Hossein Bahak, Pegah Saboori, Behnam Soufi Halaj, Ahmad R Naghsh-Nilchi, Fatemeh Mohamadpoor, Saeid Jafarizadeh","doi":"10.4103/jmss.jmss_59_24","DOIUrl":"https://doi.org/10.4103/jmss.jmss_59_24","url":null,"abstract":"<p><strong>Background: </strong>The pharmaceutical industry has seen increased drug production by different manufacturers. Failure to recognize future needs has caused improper production and distribution of drugs throughout the supply chain of this industry. Forecasting demand is one of the basic requirements to overcome these challenges. Forecasting the demand helps the drug to be well estimated and produced at a certain time.</p><p><strong>Methods: </strong>Artificial intelligence (AI) technologies are suitable methods for forecasting demand. The more accurate this forecast is the better it will be to decide on the management of drug production and distribution. Isfahan AI competitions-2023 have organized a challenge to provide models for accurately predicting drug demand. In this article, we introduce this challenge and describe the proposed approaches that led to the most successful results.</p><p><strong>Results: </strong>A dataset of drug sales was collected in 12 pharmacies of Hamadan University of Medical Sciences. This dataset contains 8 features, including sales amount and date of purchase. Competitors compete based on this dataset to accurately forecast the volume of demand. The purpose of this challenge is to provide a model with a minimum error rate while addressing some qualitative scientific metrics.</p><p><strong>Conclusions: </strong>In this competition, methods based on AI were investigated. The results showed that machine learning methods are particularly useful in drug demand forecasting. Furthermore, changing the dimensions of the data features by adding the geographic features helps increase the accuracy of models.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"15 ","pages":"2"},"PeriodicalIF":1.3,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11870324/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143543771","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 Evaluation of Scatter Correction in 128-slice Fan-Beam Computed Tomography Scan using Geant4 Application for Tomographic Emission Monte Carlo Simulation.","authors":"Iman Azinkhah,&nbsp;Mahdi Sadeghi,&nbsp;Peyman Sheikhzadeh,&nbsp;Malakeh Malekzadeh","doi":"10.4103/jmss.jmss_71_22","DOIUrl":"10.4103/jmss.jmss_71_22","url":null,"abstract":"<p><strong>Background: </strong>Simulation of tomographic imaging systems with fan-beam geometry, estimation of scattered beam profile using Monte Carlo techniques, and scatter correction using estimated data have always been new challenges in the field of medical imaging. The most important aspect is to ensure the results of the simulation and the accuracy of the scatter correction. This study aims to simulate 128-slice computed tomography (CT) scan using the Geant4 Application for Tomographic Emission (GATE) program, to assess the validity of this simulation and estimate the scatter profile. Finally, a quantitative comparison of the results is made from scatter correction.</p><p><strong>Methods: </strong>In this study, 128-slice CT scan devices with fan-beam geometry along with two phantoms were simulated by GATE program. Two validation methods were performed to validate the simulation results. The data obtained from scatter estimation of the simulation was used in a projection-based scatter correction technique, and the post-correction results were analyzed using four quantities, such as: pixel intensity, CT number inaccuracy, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR).</p><p><strong>Results: </strong>Both validation methods have confirmed the appropriate accuracy of the simulation. In the quantitative analysis of the results before and after the scatter correction, it should be said that the pixel intensity patterns were close to each other, and the accuracy of the CT scan number reached <10%. Moreover, CNR and SNR have increased by more than 30%-65% respectively in all studied areas.</p><p><strong>Conclusion: </strong>The comparison of the results before and after scatter correction shows an improvement in CNR and SNR while a reduction in cupping artifact according to pixel intensity pattern and enhanced CT number accuracy.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"13 4","pages":"280-289"},"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/ff/30/JMSS-13-280.PMC10559296.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41104405","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":"In-Field Recording of Six Biaxial Angles and Plantar Pressures in Weightlifting through a Wearable System.","authors":"Miguel Fernando Cárdenas-Rodríguez,&nbsp;Cristhian Geovanny Paute-Tigre,&nbsp;Freddy Leonardo Bueno-Palomeque","doi":"10.4103/jmss.jmss_61_22","DOIUrl":"10.4103/jmss.jmss_61_22","url":null,"abstract":"<p><strong>Background: </strong>Monitoring and evaluation of the techniques used in weightlifting are based on the subjective observation of the coach, which can ignore important aspects of short duration. This study aimed to implement an embedded system to register the angular variation of the hip, knee, and ankle joints, and plantar pressure during training.</p><p><strong>Methods: </strong>Four professional and four amateur athletes performed five snatch lifts. To evaluate the angular measurement, the tests were simultaneously videotaped and the results were contrasted.</p><p><strong>Results: </strong>The angular data presented a correlation coefficient of 0.92 and a delay of 495 ± 200 ms. The characterization of the sensors was implemented in a microcontroller with a mean absolute percentage error of 18.8% in the measurements. When comparing the average results between the elite and amateur groups, the amateur group performed a delayed descent in the first three phases of the lift and an accelerated descent in the fourth phase. A not uniform plantar pressure was registered in the same group, causing a reduction in the final speed of recovery with the barbell.</p><p><strong>Conclusions: </strong>The proposed system has been developed for biaxial angular registration of hip, knee, ankle, and plantar pressure during weightlifting snatch. The option to contrast between signals presented by the system met the requirements requested by the coaching staff and is seen as a promising quantitative analysis tool to support the coach and the athlete.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"13 4","pages":"290-299"},"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/aa/2f/JMSS-13-290.PMC10559295.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41173383","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":"Optimization of the Manufacturing Process and Mechanical Evaluation of a Functionally Graded Biodegradable Composite Screw for Orthopedic Applications.","authors":"Anosheh Zargar Kharazi,&nbsp;Emad Hosseini,&nbsp;Amir Shafaat,&nbsp;Mohammad Hosein Fathi","doi":"10.4103/jmss.jmss_5_23","DOIUrl":"10.4103/jmss.jmss_5_23","url":null,"abstract":"<p><strong>Background: </strong>Metal screws are commonly used for fracture fixations. However, the high modulus of elasticity relative to bones and releasing metallic ions by the metal screw needed a second surgery to remove the implant after the healing period. Furthermore, the removal of metal screws following the healing of the bone is a serious problem that can lead to refracture due to the presence of holes in the screw. Bioresorbable screws can overcome most of the problems associated with metallic screws which motivated research on manufacturing nonmetallic screws.</p><p><strong>Methods: </strong>In this study, three-layer poly L-lactic acid/bioactive glass composite screws were manufactured according to functionally graded material theory, by the forging process. All of the physical and chemical parameters in the manufacturing stages from making composite layers to the forging process were optimized to obtain suitable mechanical properties and durability off the screw in load-bearing positions.</p><p><strong>Results: </strong>The tri-layer composite screw with unidirectional, ±20° angled, and random fibers orientation from core to shell shows a flexural load of 661.5 ± 20.3 (N) with a decrease about 31% after 4-week degradation. Furthermore, its pull-out force was 1.8 ± 0.1 (N) which is considerably more than the degradable polymeric screws. Moreover, the integrity of the composite screws was maintained during the degradation process.</p><p><strong>Conclusions: </strong>By optimizing the manufacturing process and composition of the composite and crystallinity, mechanical properties (flexural, torsion, and pull-out) were improved and making it a perfect candidate for load-bearing applications in orthopedic implants. Improving the fiber/matrix interface through the use of a coupling agent was also considered to preserve the initial mechanical properties. The manufactured screw is sufficiently robust enough to replace metals for orthopedic load-bearing applications.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"13 4","pages":"300-306"},"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/27/a1/JMSS-13-300.PMC10559302.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41111955","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}