[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123957-en":3,"doc-seo-123957-105":29,"detail-sidebar-cat-0-en-105":82},{"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":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},123957,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",7,"Healthcare","A Complementary Diagnostic Tool for Diabetic Peripheral Neuropathy Through Muscle Ultrasound and Machine Learning Algorithms","Diabetic peripheral neuropathy is a common, long-term complication affecting about half of patients with diabetes, and routine diagnosis based on nerve conduction study can overlook skeletal muscle involvement. A complementary diagnostic tool is proposed by combining patient data (BMI, age, diabetes duration, average blood glucose) with nerve conduction measures and muscle ultrasound features, processed via supervised machine learning. Healthy and diabetic patients were analyzed using gray-level ultrasound images of six skeletal muscles, achieving high classification accuracy, supporting precise clinician diagnosis and characterization of nerve and muscle side effects.","A complementary Diagnostic Tool for Diabetic Peripheral Neuropathy Through Muscle Ultrasound and Machine Learning  \nAlgorithms  \nKadhim Kamal1, Ali Hussein Al-Timemy2, Zahid M. Kadhim3, Kosai Raoof4  \nAuthors affiliations:  \n1) Department of Biomedical Engineering, Al-Nahrain University, Baghdad, Iraq.  \n[kazemhasan90@gmail.com](kazemhasan90@gmail.com)  \n2) Department of Biomedical Engineering, University of Baghdad, Baghdad, Iraq.  \nali.altimemy@kecbu.uobagh[dad.edu.iq](dad.edu.iq)  \n3) College of Medicine, University of Babylon, Babylon, Iraq.  \n[med.zahid@uobabylon.edu.iq](med.zahid@uobabylon.edu.iq)  \n3) LAUM, Le Mans University, Le Mans-France.  \n[kosai.raoof@univ-lemans.fr](kosai.raoof@univ-lemans.fr)  \n[Paper History:](Paper History:)  \nReceived: 25th Dec. 2023  \nRevised: 18th Jan. 2024  \nAccepted: 27th Mar. 2024  \nAbstract  \nDiabetic peripheral neuropathy represents one of the common longterms complications that effect about fifty percentage of diabetes patients. The habitual diagnosis tool based on nerve conduction study that examine the nerve damage and classify the patient status into normal and diabetic peripheral neuropathy with degree of severity without considering the effect on skeletal muscle and take on patient data. A complementary diagnostic tool proposed, in this study integrates the patient’s data including body mass index, age and duration of diabetic, average blood glucose levels, nerve conduction study that involves amplitude and latency of peroneal and tibial nerves and muscle ultrasound alongside the machine learning algorithms to facilitate the clinicians for a precise diagnosis. A group of healthy and diabetic patients utilized to gather the data with calculating the muscle thickness and statistical properties from the gray-level ultrasound images of six skeletal muscles. Support vector machine, naïve bayes, ensemble of bagged tree and artificial neural network supervised machine learning algorithms categorize each class with a high classification accuracy, 98.1% for tibialis anterior with naïve bayes algorithm. The outcomes ofthis study show a promising complementary diagnostic tool that will help the clinicians to perform an exact diagnosis and disclose the side effect on both nerves and muscles of diabetic patients.  \nKeywords: Diabetic Peripheral Neuropathy, Muscle Ultrasound, Machine Learning Algorithm.  \nأ داة التشخيص المتكاملة لمرض اعتلال الاعصاب المحيطي السكري من خلالتصوير الموجات الفوق صوتيه للعضلات وخوارزميات التعلم الآلي  \nكاظم كمال، علي حسين التميمي، زاهد محمد كاظم، قصي رؤوف  \nالخلاصة:  \nيمثل الاعتلال العصبي المحيطي من المضاعفات الشائعة التي تصيب مرضى السكرى وبنسبة تصل الى نصف  \nالمرضى في حين يركز التشخيص على دراسة التأثير على الاعصاب في تصنيف درجة الإصابة من دون الرجوع الىالعضلات التي تصاب بالعتلال أ يضا . هذا البحث يقترح عمل أداة تشخيص تكميلية تأخذ بعين الاعتبار بياناتالمريض ا لمتمثلة بمؤشركتلة الجسم والعمر ومدة الاصابة بمرض السكري ومتوسط تركيز السكر في الدم ودراسة التوصيلالعصبي ا لمتمثلة بالسعة ووقت الاستجابة لعصبين حسيين في الطرف السفلي عم التصوير بالموجات الفوق الصوتيةللعضلات الهيكلية الى جانب خوارزميات التعلم الآلي ل نشاء نظام تشخيص دقيق يساعد الطبيب . شملت هذهالدراسة مجموعتين من العينات ل شخاص أصحاء ومصابين بالعتلال العصبي المحيطي السكري من النوع الثاني حيثتم حساب سمك العضلات والصفات الإحصائية لصور التدرج الرمادي للموجات الفوق الصوتية المأخوذة من ستعضلات منكل عينة تشمل ثلاثة في الطرف العلوي وثلاثة في الطرف السفلي حيث تمت أ دخالكل البيانات اليأ ربعة من خوارزميات التعلم الآلي الموجهة لتصنيف العينات باستخدام الذكاء الاصطناعي وحسب المجموعة المأخوذةك صحاء او مصابين بالعتلال العصبي المحيطي السكري بطريقة دقيقة جدا وبأقل نسبة خطأ .  \n1. Introduction  \nDiabetic peripheral neuropathy (DPN) impacts around 50% of the adult patient with diabetes through their life with associated morbidity pain, foot ulcers, and some cases lower limb amputation [1] [2] . Furthermore, DPN is the prevalent and precocious complication which cause cumulative injury of nerve fibers resulting on inferior life quali","cbCailHAbeFziRSa","https://ap.wps.com/l/cbCailHAbeFziRSa","pdf",645391,1,"English","en",105,"# Abstract\n# Keywords\n# Introduction","[{\"question\":\"How is machine learning used to classify diabetic peripheral neuropathy in the study?\",\"answer\":\"Supervised algorithms—including support vector machine, naïve bayes, bagged-tree ensemble, and artificial neural network—categorize samples. The reported results include high classification accuracy, with 98.1% for tibialis anterior using naïve bayes.\"}]","A Complementary Diagnostic Tool for Diabetic Peripheral Neuropathy Through Muscle Ultrasound and Machine Learning Algorithms | PDF",1785819429,18,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":77,"head_meta":79,"extra_data":81,"updated_unix":27},"a-complementary-diagnostic-tool-for-diabetic-peripheral-neuropathy-through-muscle-ultrasound-and-machine-learning-algorithms","",{"@graph":35,"@context":76},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/healthcare/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/a-complementary-diagnostic-tool-for-diabetic-peripheral-neuropathy-through-muscle-ultrasound-and-machine-learning-algorithms/123957/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70],{"name":71,"@type":72,"acceptedAnswer":73},"How is machine learning used to classify diabetic peripheral neuropathy in the study?","Question",{"text":74,"@type":75},"Supervised algorithms—including support vector machine, naïve bayes, bagged-tree ensemble, and artificial neural network—categorize samples. The reported results include high classification accuracy, with 98.1% for tibialis anterior using naïve bayes.","Answer","https://schema.org",{"og:url":51,"og:type":78,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":80,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":83},[84,88,92,96,101,106,109,114,119,122,126],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":45,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":107,"slug":108},40,"healthcare",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},8,"Research & Report",30,"research-report",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":45,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general"]