Bonaldi Lorenza, Pretto Andrea, Pirri Carmelo, Uccheddu Francesca, Fontanella Chiara Giulia, Stecco Carla
Department of Civil, Environmental and Architectural Engineering, University of Padova, Via F. Marzolo 9, 35131 Padova, Italy.
Department of Industrial Engineering, University of Padova, Via Venezia 1, 35121 Padova, Italy.
Bioengineering (Basel). 2023 Jan 19;10(2):137. doi: 10.3390/bioengineering10020137.
By leveraging the recent development of artificial intelligence algorithms, several medical sectors have benefited from using automatic segmentation tools from bioimaging to segment anatomical structures. Segmentation of the musculoskeletal system is key for studying alterations in anatomical tissue and supporting medical interventions. The clinical use of such tools requires an understanding of the proper method for interpreting data and evaluating their performance. The current systematic review aims to present the common bottlenecks for musculoskeletal structures analysis (e.g., small sample size, data inhomogeneity) and the related strategies utilized by different authors. A search was performed using the PUBMED database with the following keywords: deep learning, musculoskeletal system, segmentation. A total of 140 articles published up until February 2022 were obtained and analyzed according to the PRISMA framework in terms of anatomical structures, bioimaging techniques, pre/post-processing operations, training/validation/testing subset creation, network architecture, loss functions, performance indicators and so on. Several common trends emerged from this survey; however, the different methods need to be compared and discussed based on each specific case study (anatomical region, medical imaging acquisition setting, study population, etc.). These findings can be used to guide clinicians (as end users) to better understand the potential benefits and limitations of these tools.
通过利用人工智能算法的最新发展，几个医学领域已从使用生物成像自动分割工具来分割解剖结构中受益。肌肉骨骼系统的分割是研究解剖组织变化和支持医学干预的关键。此类工具的临床应用需要了解解释数据和评估其性能的正确方法。当前的系统评价旨在呈现肌肉骨骼结构分析的常见瓶颈（如样本量小、数据不均匀性）以及不同作者采用的相关策略。使用PUBMED数据库进行了搜索，关键词如下：深度学习、肌肉骨骼系统、分割。共获取了截至2022年2月发表的140篇文章，并根据PRISMA框架从解剖结构、生物成像技术、预处理/后处理操作、训练/验证/测试子集创建、网络架构、损失函数、性能指标等方面进行了分析。本次调查出现了几个共同趋势；然而，需要根据每个具体案例研究（解剖区域、医学成像采集设置、研究人群等）对不同方法进行比较和讨论。这些发现可用于指导临床医生（作为最终用户）更好地理解这些工具的潜在益处和局限性。
