Liver volumetric and anatomic assessment in living donor liver transplantation: The role of modern imaging and artificial intelligence
作者机构：Department of Hepato-Biliary-Pancreatic Surgery and Liver TransplantationIrmandade Santa Casa de Misericórdia de Porto AlegrePorto Alegre 90020-090Brazil Postgraduation Program in Medicine:HepatologyFederal University of Health Sciences of Porto AlegrePorto Alegre 90050-170Brazil
出 版 物：《World Journal of Transplantation》 (世界移植杂志)
年 卷 期：2023年第13卷第6期
页 面：290-298页
学科分类：1002[医学-临床医学] 100201[医学-内科学(含：心血管病、血液病、呼吸系病、消化系病、内分泌与代谢病、肾病、风湿病、传染病)] 10[医学]
基 金：Supported by Part by The Coordenação de Aperfeiçoamento de Pessoal de Nível Superior–Brasil(CAPES)
主 题：Liver transplantation Living-donor Diagnostic imaging Artificial intelligence Machine learning Deep learning
摘 要：The shortage of deceased donor organs has prompted the development of alternative liver grafts for ***-donor liver transplantation(LDLT)has emerged as a viable option,expanding the donor pool and enabling timely transplantation with favorable graft function and improved long-term *** accurate evaluation of the donor liver’s volumetry(LV)and anatomical study is crucial to ensure adequate future liver remnant,graft volume and precise liver ***,ensuring donor safety and an appropriate graftto-recipient weight *** LV(MLV)using computed tomography has traditionally been considered the gold standard for assessing liver ***,the method has been limited by cost,subjectivity,and *** LV techniques employing advanced segmentation algorithms offer improved reproducibility,reduced variability,and enhanced efficiency compared to manual ***,the accuracy of automated LV requires further *** study provides a comprehensive review of traditional and emerging LV methods,including semi-automated image processing,automated LV techniques,and machine learning-based ***,the study discusses the respective strengths and weaknesses of each of the aforementioned *** use of artificial intelligence(AI)technologies,including machine learning and deep learning,is expected to become a routine part of surgical planning in the near *** implementation of AI is expected to enable faster and more accurate image study interpretations,improve workflow efficiency,and enhance the safety,speed,and cost-effectiveness of the *** preoperative assessment of the liver plays a crucial role in ensuring safe donor selection and improved outcomes in *** has inherent limitations that have led to the adoption of semi-automated and automated software ***,AI has tremendous potential for LV and segmentation;however,its widespread use is hindered
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