The open medical informatics journal最新文献

{"title":"Intelligent Signal and Image Processing in eHealth","authors":"O. Salvetti, S. Colantonio","doi":"10.2174/1874431101004010103","DOIUrl":"https://doi.org/10.2174/1874431101004010103","url":null,"abstract":"Highly technological intelligent solutions based on the appropriate and careful interpretation of medical data, acquired by diagnostic investigations are more and more assuming a key importance in the improvement of health care quality and management. \u0000 \u0000The considerable advances in diagnostic technologies and enhancement of the different modalities have made possible to obtain high-resolution images and signals which are able to provide highly precise information regarding body structure and function, which allow clinicians making more accurate and efficient diagnoses, often in a non-invasive way. As a result, in the last decades, the development of computerised methods for diagnostic data processing and management has attracted a lot of interest and effort within medical imaging and diagnostic radiology, becoming in some cases a practical clinical approach. The basic concept of these methods is to provide a second opinion or a second reader that can aid clinicians in improving the accuracy and consistency of the diagnostic, prognostic and follow-up processes. Actually, the clinical interpretation of diagnostic data and their findings largely depends on the reader’s subjective point of view, knowledge and experience. The presence of noise or the vast amount of data, generated by some devices, can make the detection of potential diseases a burdensome task and may cause oversight errors. Hence, computer-aided methods, able to make this interpretation reproducible and consistent, are fundamental for reducing subjectivity while increasing accuracy. \u0000 \u0000Moreover, the amount and complexity of data and information to be analyzed and managed strongly demand for the development of computerised decision aiding systems able to cope with the increasing bulk of clinical data by providing an integrated approach to analysis, foster adherence to guidelines, prevent omissions and disseminate up-to-date specialist knowledge. \u0000 \u0000In this respect, the aim of this Special Issue is to gather new research and application trends in eHealth including intelligent signal and image processing, advanced systems for medical ontologies, medical knowledge discovery, representation and management, efficient clinical decision support systems, multilevel modeling of pathologies, therapy simulation and virtualization of the human physiology; all methods that are becoming an essential component in supporting clinicians’ decision making during their clinical routine workflow. \u0000 \u0000The issues related to the development of specialized platforms and tools to speed up the process of biomedical data analysis are faced by Skounakis et al. in the first paper. The authors present Doctor Eye, a novel, open access interactive platform which is devoted to 3D medical image analysis, simulation and visualization. Currently focused on oncological application, the platform allows clinicians managing a large number of 3D tomographic datasets by providing them methods for efficiently annotating multiple","PeriodicalId":88331,"journal":{"name":"The open medical informatics journal","volume":"11 1","pages":"103 - 104"},"PeriodicalIF":0.0,"publicationDate":"2010-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87740513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Identification of Insulin Saturation Effects During the Dynamic Insulin Sensitivity Test~!2010-03-19~!2010-05-13~!2010-07-22~!","authors":"P. Docherty","doi":"10.2174/1874431101004030141","DOIUrl":"https://doi.org/10.2174/1874431101004030141","url":null,"abstract":"","PeriodicalId":88331,"journal":{"name":"The open medical informatics journal","volume":"1 1","pages":"141-148"},"PeriodicalIF":0.0,"publicationDate":"2010-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77118011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantification of Epicardial Fat by Cardiac CT Imaging~!2009-12-05~!2010-03-01~!2010-07-22~!","authors":"G. Coppini","doi":"10.2174/1874431101004030126","DOIUrl":"https://doi.org/10.2174/1874431101004030126","url":null,"abstract":"","PeriodicalId":88331,"journal":{"name":"The open medical informatics journal","volume":"101 1","pages":"126-135"},"PeriodicalIF":0.0,"publicationDate":"2010-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79384877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DoctorEye: A Clinically Driven Multifunctional Platform, for Accurate Processing of Tumors in Medical Images~!2009-12-08~!2010-03-04~!2010-07-21~!","authors":"Emmanouil Skounakis","doi":"10.2174/1874431101004030105","DOIUrl":"https://doi.org/10.2174/1874431101004030105","url":null,"abstract":"","PeriodicalId":88331,"journal":{"name":"The open medical informatics journal","volume":"26 1","pages":"105-115"},"PeriodicalIF":0.0,"publicationDate":"2010-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78236555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discovering Differences in Acoustic Emission Between Healthy and Osteoarthritic Knees Using a Four-Phase Model of Sit-Stand-Sit Movements~!2009-12-08~!2010-03-04~!2010-07-21~!","authors":"L. Shark","doi":"10.2174/1874431101004030116","DOIUrl":"https://doi.org/10.2174/1874431101004030116","url":null,"abstract":"","PeriodicalId":88331,"journal":{"name":"The open medical informatics journal","volume":"21 1","pages":"116-125"},"PeriodicalIF":0.0,"publicationDate":"2010-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78765511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spirometry Longitudinal Data Analysis Software (SPIROLA) for Analysis of Spirometry Data in Workplace Prevention or COPD Treatment.","authors":"Eva Hnizdo, Tieliang Yan, Artak Hakobyan, Paul Enright, Lu-Ann Beeckman-Wagner, John Hankinson, James Fleming, Edward Lee Petsonk","doi":"10.2174/1874431101004010094","DOIUrl":"10.2174/1874431101004010094","url":null,"abstract":"<p><p>Chronic obstructive pulmonary disease (COPD) is one of the leading causes of morbidity and mortality. Periodic spirometry is often recommended for individuals with potential occupational exposure to respiratory hazards and in medical treatment of respiratory disease, to prevent COPD or improve treatment outcome. To achieve the full potential of spirometry monitoring in preserving lung function, it is important to maintain acceptable precision of the longitudinal measurements, apply interpretive strategies that identify individuals with abnormal test results or excessive loss of lung function in a timely manner, and use the results for intervention on respiratory disease prevention or treatment modification. We describe novel, easy-to-use visual and analytical software, Spirometry Longitudinal Data Analysis software (SPIROLA), designed to assist healthcare providers in the above aspects of spirometry monitoring. Software application in ongoing workplace spirometry-based medical monitoring programs helped to identify increased spirometry data variability due to deteriorating test quality and subsequent improvement following interventions, and helped to enhance identification of individuals with excessive decline in lung function.</p>","PeriodicalId":88331,"journal":{"name":"The open medical informatics journal","volume":" ","pages":"94-102"},"PeriodicalIF":0.0,"publicationDate":"2010-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/9f/24/TOMINFOJ-4-94.PMC2936036.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40062602","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":"Segmentation of fluorescence microscopy cell images using unsupervised mining.","authors":"Xian Du,&nbsp;Sumeet Dua","doi":"10.2174/1874431101004020041","DOIUrl":"https://doi.org/10.2174/1874431101004020041","url":null,"abstract":"<p><p>The accurate measurement of cell and nuclei contours are critical for the sensitive and specific detection of changes in normal cells in several medical informatics disciplines. Within microscopy, this task is facilitated using fluorescence cell stains, and segmentation is often the first step in such approaches. Due to the complex nature of cell issues and problems inherent to microscopy, unsupervised mining approaches of clustering can be incorporated in the segmentation of cells. In this study, we have developed and evaluated the performance of multiple unsupervised data mining techniques in cell image segmentation. We adapt four distinctive, yet complementary, methods for unsupervised learning, including those based on k-means clustering, EM, Otsu's threshold, and GMAC. Validation measures are defined, and the performance of the techniques is evaluated both quantitatively and qualitatively using synthetic and recently published real data. Experimental results demonstrate that k-means, Otsu's threshold, and GMAC perform similarly, and have more precise segmentation results than EM. We report that EM has higher recall values and lower precision results from under-segmentation due to its Gaussian model assumption. We also demonstrate that these methods need spatial information to segment complex real cell images with a high degree of efficacy, as expected in many medical informatics applications.</p>","PeriodicalId":88331,"journal":{"name":"The open medical informatics journal","volume":"4 ","pages":"41-9"},"PeriodicalIF":0.0,"publicationDate":"2010-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/d3/c2/TOMINFOJ-4-41.PMC2930152.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29501006","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":"Finger motion classification by forearm skin surface vibration signals.","authors":"Wenwei Yu,&nbsp;Toshiharu Kishi,&nbsp;U Rajendra Acharya,&nbsp;Yuse Horiuchi,&nbsp;Jose Gonzalez","doi":"10.2174/1874431101004020031","DOIUrl":"https://doi.org/10.2174/1874431101004020031","url":null,"abstract":"<p><p>The development of prosthetic hand systems with both decoration and motion functionality for hand amputees has attracted wide research interests. Motion-related myoelectric potentials measured from the surface of upper part of forearms were mostly employed to construct the interface between amputees and prosthesis.However, finger motions, which play a major role in dexterous hand activities, could not be recognized from surface EMG (Electromyogram) signals.The basic idea of this study is to use motion-related surface vibration, to detect independent finger motions without using EMG signals. In this research, accelerometers were used in a finger tapping experiment to collect the finger motion related mechanical vibration patterns. Since the basic properties of the signals are unknown, a norm based, a correlation coefficient based, and a power spectrum based method were applied to the signals for feature extraction. The extracted features were then fed to back-propagation neural networks to classify for different finger motions.The results showed that, the finger motion identification is possible by using the neural networks to recognize vibration patterns.</p>","PeriodicalId":88331,"journal":{"name":"The open medical informatics journal","volume":"4 ","pages":"31-40"},"PeriodicalIF":0.0,"publicationDate":"2010-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2174/1874431101004020031","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29176044","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":"Classification of upper limb motions from around-shoulder muscle activities: hand biofeedback.","authors":"Jose González,&nbsp;Yuse Horiuchi,&nbsp;Wenwei Yu","doi":"10.2174/1874431101004020074","DOIUrl":"https://doi.org/10.2174/1874431101004020074","url":null,"abstract":"<p><p>Mining information from EMG signals to detect complex motion intention has attracted growing research attention, especially for upper-limb prosthetic hand applications. In most of the studies, recordings of forearm muscle activities were used as the signal sources, from which the intention of wrist and hand motions were detected using pattern recognition technology. However, most daily-life upper limb activities need coordination of the shoulder-arm-hand complex, therefore, relying only on the local information to recognize the body coordinated motion has many disadvantages because natural continuous arm-hand motions can't be realized. Also, achieving a dynamical coupling between the user and the prosthesis will not be possible. This study objective was to investigate whether it is possible to associate the around-shoulder muscles' Electromyogram (EMG) activities with the different hand grips and arm directions movements. Experiments were conducted to record the EMG of different arm and hand motions and the data were analyzed to decide the contribution of each sensor, in order to distinguish the arm-hand motions as a function of the reaching time. Results showed that it is possible to differentiate hand grips and arm position while doing a reaching and grasping task. Also, these results are of great importance as one step to achieve a close loop dynamical coupling between the user and the prosthesis.</p>","PeriodicalId":88331,"journal":{"name":"The open medical informatics journal","volume":"4 ","pages":"74-80"},"PeriodicalIF":0.0,"publicationDate":"2010-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/4b/77/TOMINFOJ-4-74.PMC2918869.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29197758","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":"Joint Metabonomic and Instrumental Analysis for the Classification of Migraine Patients with 677-MTHFR Mutations.","authors":"Pierangela Giustetto,&nbsp;William Liboni,&nbsp;Ornella Mana,&nbsp;Gianni Allais,&nbsp;Chiara Benedetto,&nbsp;Filippo Molinari","doi":"10.2174/1874431101004020023","DOIUrl":"https://doi.org/10.2174/1874431101004020023","url":null,"abstract":"<p><p>Migraine is a neurological disorder that correlates with an increased risk of cerebrovascular lesions. Genetic mutations of the MTHFR gene are correlated to migraine and to the increased risk of artery pathologies. Also, migraine patients show altered hematochemical parameters, linked to an impaired platelet aggregation mechanism. Hence, the vascular assessment of migraineurs is of primary importance.Transcranial Doppler sonography (TCD) is used to measure cerebral blood flow velocity (CBFV) and vasomotor reactivity (by an index measured during breath-holding - BHI). Aim of this study was the metabolic profiling of migraine subjects with T/T677-MTHFR and C/T677-MTHFR mutations and its correlation with CBFV and BHI.Metabonomic multidimensional techniques were used to describe and cluster subjects. Fifty women suffering from migraine (age: 18-64; 21 with aura) underwent TCD examination, hematochemical blood analysis, Born test, and genetic tests for MTHFR mutation. Fourteen (7 with aura) had T/T677, 18 (8 with aura) had C/T677, and 18 (6 with aura) had no mutation. The total number of variables was 24.Unsupervised and supervised techniques_showed the correlation between CBFV and BHI with mutation. Discriminant analysis allowed for classifying the subjects with 95.9% sensitivity and 89.0% specificity. Aura was not correlated to mutation or variations of instrumental data.Our study showed that metabonomics could be effectively applied in clinical problems to show the overall correlation structure of complex systems in pathology. Specifically, our results confirmed the importance of TCD in the metabolic profiling and follow-up of migraine patients.</p>","PeriodicalId":88331,"journal":{"name":"The open medical informatics journal","volume":"4 ","pages":"23-30"},"PeriodicalIF":0.0,"publicationDate":"2010-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/44/58/TOMINFOJ-4-23.PMC2916204.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29176043","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}