[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122211-en":3,"doc-seo-122211-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122211,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","SURVEY OF MACHINE LEARNING APPLICATIONS IN MEDICAL IMAGING - ALGORITHMS AND TECHNOLOGIES","A literature survey reports how machine learning algorithms are used to process and interpret medical images such as X-ray, CT, MRI, and DEXA for diagnosing orthopedic disorders. It reviews how recent advances in compute capacity and open-source ML platforms and libraries (e.g., Anaconda, Scikit, TensorFlow, Torch) accelerated model development. The work also summarizes key ML technology categories, with attention to neural networks and typical systems employed in orthopedic imaging for clinical decision support.","SURVEY OF MACHINE LEARNING APPLICATIONS IN MEDICAL  \nIMAGING. ALGORITHMS AND TECHNOLOGIES  \nEng. PhD. Marius Eremia Vlaicu POPA, Complexul Energetic Oltenia, ROMANIA, [m.vlaicu@yahoo.com](m.vlaicu@yahoo.com)  \nProfessor PhD. eng. Mihai CRUCERU, Constantin Brancusi” University of Tg-Jiu,  \nROMANIA, [cruceru.mihai@gmail.com](cruceru.mihai@gmail.com)  \nAssoc.Prof. Eng. Ph.D., Bogdan DIACONU, Constantin Brancusi” University of Tg-Jiu,  \nROMANIA, [bdiaconu2004@gmail.com](bdiaconu2004@gmail.com)  \nAbstract: A literature survey was conducted attempting to report usage of Machine Learning (ML) algorithms in processing and interpretation of medical images (X-ray, CT scan, MRI, DEXA, etc.) for the purpose of diagnosing orthopedic disorders. ML algorithms and technologies developed exponentially over the last decades triggered by advances in processing capacity, open-source ML platforms, frameworks and libraries (Anaconda, Scikit, TensorFlow, OpenNN, Torch and so on).  \nKeywords: Artificial Intelligence; Machine Learning; Medical Imaging; Medical Diagnosis.  \n1. Introduction  \nMedical imaging has become a standard in clinical analysis of orthopedic disorders, either trauma, developmental or induced by factor such as infectious processes, neuromuscular, nutritional, or neoplastic processes. A large number of imaging techniques is required in orthopedic diagnosis, depending on the particular tissue that has to be visualized and the specific type of tissue abnormality suspicion. Some case-specific conditions could impose restrictions on the use of one technique or another: patients with cardiac stimulation devices prevents the usage of MRI; various allergies prevent imaging techniques that require contrast agents, etc.  \nPicture Archiving and Communication Systems (PACS) have been used since the 1990s for radiologic images storage, management and processing [224] . A complex system aiming at storing, transmitting, retrieving, printing, processing and displaying medical imaging information - Digital Imaging and Communications in Medicine was introduced in 1993 0. The necessity to archive/retrieve and search for relevant information in radiology reports resulted in definition of lexicons such as Metathesaurus 0, RadLex 0 and Medical Subjects Headings 0. Metathesaurus includes more than five million concept names and a million biomedical terms from more than one hundred controlled vocabulary systems 0. Such databases require automated agents to add/retrieve/search/manage information, which is where ML models can prove their utility. DL is a ML subset that demonstrated promising results in generating radiology reports from images 0, 0. DL applications in healthcare have been reviewed in 0.  \nML has been increasingly used in medical application, especially in diagnosis, with the role of a decision support tool designed to improve the performance of the healthcare provider. The first reports of AI applications in medicine dates back in the 1980s 0. Picciali et al 0 reviewed comprehensively the evolution of DL application in medicine reporting: (1) medical specialties such as oncology, cardiovascular medicine, orthopedics, neurology, pulmonology, etc., (2) bio signals, such as electrocardiography, electroencephalography, phonocardiography, photoplethysmography, electromyography, magnetic resonance  \nspectroscopy and monitoring signaling molecules and (3) DL models such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Autoencoders, Generative Adversarial Networks (GAN), Deep Belief Networks (DBN) .  \n2. Literature review: Results.  \nThe machine learning technologies identified in the selected papers were classified in the following groups:  \n1. Neural Networks  \n2. Support Vector Machine  \n3. Decision Tree/Random Forest/Gradient Boosting  \n4. Linear Regression/Multiple Least Squares Linear Regression  \n5. K Nearest Neighbor  \n6. Naïve Bayes Classifier  \n3. Reports of typical ML technologies employed in orthopedic imaging  \nT","cbCaiuLN0X0MhqMA","https://ap.wps.com/l/cbCaiuLN0X0MhqMA","pdf",917457,1,6,"English","en",105,"# Introduction\n## Orthopedic disorders and imaging constraints\n## PACS and radiology information retrieval\n# Literature review: Results\n## Classified ML technology groups\n# Reports of typical ML technologies employed in orthopedic imaging\n## Artificial Neural Networks","[{\"question\":\"What medical imaging modalities are covered when applying machine learning for orthopedic diagnosis?\",\"answer\":\"The survey focuses on images such as X-ray, CT scan, MRI, and DEXA. It frames these modalities around orthopedic disorder diagnosis needs.\"},{\"question\":\"Why do constraints sometimes limit which imaging technique can be used for a patient?\",\"answer\":\"The document explains that some conditions restrict technique choice, such as MRI being unavailable for patients with cardiac stimulation devices and imaging with contrast agents being prevented by allergies.\"},{\"question\":\"Which machine learning approaches are identified as common technology groups in the literature?\",\"answer\":\"The survey classifies technologies into groups including neural networks, support vector machines, decision tree/random forest/gradient boosting, linear regression variants, k-nearest neighbor, and naïve Bayes classifiers.\"}]","SURVEY OF MACHINE LEARNING APPLICATIONS IN MEDICAL IMAGING - ALGORITHMS AND TECHNOLOGIES | PDF",1785809383,15,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"survey-of-machine-learning-applications-in-medical-imaging-algorithms-and-technologies","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/survey-of-machine-learning-applications-in-medical-imaging-algorithms-and-technologies/122211/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What medical imaging modalities are covered when applying machine learning for orthopedic diagnosis?","Question",{"text":75,"@type":76},"The survey focuses on images such as X-ray, CT scan, MRI, and DEXA. It frames these modalities around orthopedic disorder diagnosis needs.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why do constraints sometimes limit which imaging technique can be used for a patient?",{"text":80,"@type":76},"The document explains that some conditions restrict technique choice, such as MRI being unavailable for patients with cardiac stimulation devices and imaging with contrast agents being prevented by allergies.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning approaches are identified as common technology groups in the literature?",{"text":84,"@type":76},"The survey classifies technologies into groups including neural networks, support vector machines, decision tree/random forest/gradient boosting, linear regression variants, k-nearest neighbor, and naïve Bayes classifiers.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]