利用结构性磁共振成像生物标志物预防运动员跟腱损伤:机器学习方法。

IF 4.1 2区 医学 Q1 SPORT SCIENCES Sports Medicine - Open Pub Date : 2024-11-05 DOI:10.1186/s40798-024-00786-6
Norbert Kapinski, Karol Jaskulski, Justyna Witkowska, Adam Kozlowski, Pawel Adamczyk, Bartosz Wysoczanski, Agnieszka Zdrodowska, Adam Niemaszyk, Beata Ciszkowska-Lyson, Michal Starczewski
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引用次数: 0

摘要

背景:人工智能的最新进展证明了其在骨科领域的有效性,尤其是在医学图像分析等任务中。本研究比较了人类肌肉骨骼放射科医生和人工智能对跟腱磁共振图像进行的新颖、详细、简短和经济高效的检查,以发现他们在推理方法上的潜在差异。目的是确定跟腱结构化评估与其功能之间的关系,从而有助于预防损伤。我们对 72 名运动员进行了检查,以研究跟腱结构与跟腱功能属性之间的联系,跟腱结构是通过精确的 T2* 加权梯度回波序列和极短的回波时间在磁共振图像中显示出来的。我们使用先进的人工智能技术对获取的数据进行了分析,并由放射科医生进行了审查。此外,我们还进行了统计评估,以探索与四个有意义组的功能研究之间的关系:动态力量、运动范围、肌肉扭矩和稳定度:结果显示,功能指标与结构改变之间存在明显的线性或非线性关系(放射学评估的斯皮尔曼相关系数最大值为 0.3 至 0.36,人工智能评估的斯皮尔曼相关系数最大值为 0.33 至 0.49,而放射学评估的归一化互信息最大值为 0.52 至 0.57,人工智能评估的归一化互信息最大值为 0.42 至 0.6)。此外,当使用基于人工智能的磁共振评估作为输入时,关联始终被证明更为稳健,或者说显著关联的数量超过了放射学评估。最终,仅利用结构参数作为输入,我们就能解释特定功能组内高达59%的变异:这项分析表明,结构参数会影响与跟腱相关的四个关键功能方面。研究设计:研究设计:横断面研究。
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Towards Achilles Tendon Injury Prevention in Athletes with Structural MRI Biomarkers: A Machine Learning Approach.

Background: Recent advancements in artificial intelligence have proven their effectiveness in orthopaedic settings, especially in tasks like medical image analysis. This study compares human musculoskeletal radiologists to artificial intelligence in a novel, detailed, short, and cost-effective examination of Achilles tendon magnetic resonance images to uncover potential disparities in their reasoning approaches. Aiming to identify relationships between the structured assessment of the Achilles tendon and its function that could support injury prevention. We examined 72 athletes to investigate the link between Achilles tendon structure, as visualised in magnetic resonance images using a precise T2*-weighted gradient echo sequence with very short echo times, and its functional attributes. The acquired data were analysed using advanced artificial intelligence techniques and reviewed by radiologists. Additionally, we conducted statistical assessments to explore relationships with functional studies in four meaningful groups: dynamic strength, range of motion, muscle torque and stabilography.

Results: The results show notable linear or non-linear relationships between functional indicators and structural alterations (maximal obtained Spearman correlation coefficients ranged from 0.3 to 0.36 for radiological assessment and from 0.33 to 0.49 for artificial intelligence assessment, while maximal normalised mutual information ranged from 0.52 to 0.57 for radiological assessment and from 0.42 to 0.6 for artificial intelligence assessment). Moreover, when artificial intelligence-based magnetic resonance assessment was utilised as an input, the associations consistently proved more robust, or the count of significant relationships surpassed that derived from radiological assessment. Ultimately, utilising only structural parameters as inputs enabled us to explain up to 59% of the variance within specific functional groups.

Conclusions: This analysis revealed that structural parameters influence four key functional aspects related to the Achilles tendon. Furthermore, we found that relying solely on subjective radiologist opinions limited our ability to reason effectively, in contrast to the structured artificial intelligence assessment.

Study design: Cross-sectional studies.

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来源期刊
Sports Medicine - Open
Sports Medicine - Open SPORT SCIENCES-
CiteScore
7.00
自引率
4.30%
发文量
142
审稿时长
13 weeks
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