[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122700-en":3,"doc-seo-122700-105":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":20,"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},122700,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","On the in vivo recognition of kidney stones using machine learning - Pilot study","Determining kidney stone type enables urologists to select personalized treatment and reduce recurrence of renal lithiasis. This pilot study proposes automated in-vivo, endoscope-acquired image classification for four frequent urinary calculus types. It benchmarks six shallow machine learning methods against three deep-learning architectures, detailing dataset construction and classifier design. Inception v3 achieves the best weighted precision, recall, and F1-score (0.97, 0.98, 0.97), while a carefully chosen colour space and texture features allow XGBoost to closely approach performance (weighted F1 around 0.96).","arXiv :2201 .08865v2 [ ee ss .IV] 24 Aug 2023  \nOn the in vivo recognition of kidney stones using machine learning  \nFrancisco Lopez-Tiroa,b , Vincent Estradec , Jacques Hubertd,e , Daniel Flores-Araizaa , Miguel Gonzalez-Mendozaa , Gilberto Ochoa-Ruiza and Christian Daulb  \na Tecnologico de Monterrey, Escuela de Ingenieria y Ciencias, Av. Eugenio Garza Sada Sur 2501 Sur, Tecnológico, 64849 Monterrey, N.L, Mexico b Centre de Recherche en Automatique de Nancy (UMR 7030, CNRS and Université de Lorraine), 2 avenue de la Forêt de Haye, F-54516 Vandœuvre-Lès-Nancy, France  \ncCHRUPellegrin, place Amélie Raba Léon, F-33000 Bordeaux, France  \ndIADI-UL-INSERM (U1254), 5 rue du Morvan, Vandœuvre-lès-Nancy, France  \neCHRU Nancy, Service d’urologie de Brabois, rue du Morvan, F-54511 Vandœuvre-Lès-Nancy, France  \nAB STRACT  \nDetermining the kidney stones type allows urologists to prescribe a treatment to avoid recurrence of renal lithiasis. An automated in-vivo image-based classification method would be an important step towards an immediate identification of the kidney stone type required as a first phase of the diagnosis. In the literature it was shown on ex-vivo data (i.e., in very controlled scene and image acquisition conditions) that an automated kidney stone classification is indeed feasible. This pilot study compares the kidney stone recognition performances of six shallow machine learning methods and three deep-learning architectures which were tested with in-vivo images of the four most frequent urinary calculi types acquired with an endoscope during standard ureteroscopies. This contribution details the database construction and the design of the tested kidney stones classifiers. Even if the best results were obtained by the Inception v3 architecture (weighted precision, recall and F1-score of 0.97, 0.98 and 0.97, respectively), it is also shown that choosing an appropriate colour space and texture features allows a shallow machine learning method to approach closely the performances of the most promising deep-learning methods (the XGBoost classifier led to weighted precision, recall and F1-score values of 0 .96) .  \n1. Introduction  \nUrinary lithiasis refers to the formation of crystalline accretions (kidney stones) from minerals dissolved in urine Cloutier et al. (2015) . Kidney stones form themselves in the kidneys and migrate through the urinary tract (ureters, bladder, etc.). While small kidney stones evacuate naturally and imperceptibly, larger accretions (beyond a few millimeters) often cause severe pain (e.g., due to an obstructed ureter) and must be removed during an ureteroscopy (endoscopy of the upper urinary tract) . Numerous developed countries Kasidas et al. (2004); Hall (2009) exhibit a high urinary lithiasis incidence since about 10% of their population is affected at least once by a kidney stone episode. The formation of kidney stones is favoured by various risk factors. Apart from reasons related to genetic inheritance, diet (eating too many fruits, vitamin C, vitamin B6, or animal proteins increases the risk of forming kidney stones), chronic diseases (e.g., diabetes) or an inappropriate lifestyle (e.g., a sedentary lifestyle that leads to a high body mass index) are some of the risk factors for urinary lithiasis. Thereis a direct relationship between these risk factors and the biochemical composition of the kidney stones Silva et al.(2010); Daudon and Jungers (2012). In developed countries, the stone recurrence rate approaches a very high value of 40% Scales Jr et al. (2012); Viljoen et al. (2019) .  \n∗Corresponding author  \nEmail: [gilberto.ochoa@tec.mx](gilberto.ochoa@tec.mx) (G. Ochoa-Ruiz), christian.daul@univ[lorraine.fr](lorraine.fr) (C. Daul*)  \nORCID(s):  \nTherefore, identifying the kidney stone types is crucial to avoid relapses Kartha et al. (2013); Friedlander et al.(2015) through personalized treatments (diet adaptation, surgery, etc.) is considered of utmost importance by many practitioners Estra","cbCaioIufKYmD4hH","https://ap.wps.com/l/cbCaioIufKYmD4hH","pdf",7238232,1,19,"English","en",105,"# Introduction\n## Context and recent trends in ureteroscopy\n## In-vivo stone recognition approach\n## Machine learning classifiers and evaluation","[{\"question\":\"Why is recognizing kidney stone type important in clinical practice?\",\"answer\":\"Accurate stone typing supports personalized treatment choices and helps avoid relapses of renal lithiasis.\"},{\"question\":\"What data and imaging setting are used for the recognition study?\",\"answer\":\"The study uses in-vivo images of four frequent urinary calculus types acquired with an endoscope during standard ureteroscopies.\"},{\"question\":\"How do shallow machine learning methods compare with deep-learning architectures?\",\"answer\":\"The best deep model is Inception v3, while an appropriately selected colour space and texture features allow a shallow model such as XGBoost to approach deep-learning performance closely.\"}]","On the in vivo recognition of kidney stones using machine learning - Pilot study | PDF",1785812330,48,{"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},"on-the-in-vivo-recognition-of-kidney-stones-using-machine-learning-pilot-study","",{"@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/on-the-in-vivo-recognition-of-kidney-stones-using-machine-learning-pilot-study/122700/",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":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is recognizing kidney stone type important in clinical practice?","Question",{"text":75,"@type":76},"Accurate stone typing supports personalized treatment choices and helps avoid relapses of renal lithiasis.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and imaging setting are used for the recognition study?",{"text":80,"@type":76},"The study uses in-vivo images of four frequent urinary calculus types acquired with an endoscope during standard ureteroscopies.",{"name":82,"@type":73,"acceptedAnswer":83},"How do shallow machine learning methods compare with deep-learning architectures?",{"text":84,"@type":76},"The best deep model is Inception v3, while an appropriately selected colour space and texture features allow a shallow model such as XGBoost to approach deep-learning performance closely.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},"General","general"]