创伤性脑损伤神经影像学中的机器学习:当前形势、研究空白和未来方向

Kevin Pierre, Jordan Turetsky, Abheek G Raviprasad, Seyedeh Mehrsa Sadat Razavi, Michael Mathelier, Anjali Patel, Brandon Lucke-Wold
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引用次数: 0

摘要

在这篇叙述性综述中,我们探讨了机器学习(ML)在创伤性脑损伤(TBI)的诊断、预后和临床管理中不断发展的作用。创伤性脑损伤的发病率越来越高,需要采用先进的技术进行及时准确的诊断,而机器学习为应对这一挑战提供了前景广阔的工具。目前的研究主要集中在整合临床数据、患者人口统计数据、实验室结果和成像结果,但在充分利用图像特征的潜力方面仍存在差距。虽然在硬膜下血肿分割和预后预测等领域取得了进展,但将这些技术转化为临床实践仍处于起步阶段。与数据隐私、临床医生信任度和各种医疗系统的互操作性有关的挑战进一步加剧了这一问题。尽管存在这些障碍,但经 FDA 批准用于创伤性脑损伤的人工智能应用及其随后取得的可喜成果凸显了人工智能在彻底改变创伤性脑损伤护理方面的潜力。本综述最后强调了弥合理论研究与实际临床应用之间差距的重要性,以及解决将 ML 融入医疗保健所涉及的伦理和隐私问题的必要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning in Neuroimaging of Traumatic Brain Injury: Current Landscape, Research Gaps, and Future Directions
In this narrative review, we explore the evolving role of machine learning (ML) in the diagnosis, prognosis, and clinical management of traumatic brain injury (TBI). The increasing prevalence of TBI necessitates advanced techniques for timely and accurate diagnosis, and ML offers promising tools to meet this challenge. Current research predominantly focuses on integrating clinical data, patient demographics, lab results, and imaging findings, but there remains a gap in fully harnessing the potential of image features. While advancements have been made in areas such as subdural hematoma segmentation and prognosis prediction, the translation of these techniques into clinical practice is still in its infancy. This is further compounded by challenges related to data privacy, clinician trust, and the interoperability of various health systems. Despite these hurdles, FDA-approved ML applications for TBI and their subsequent promising results underscore the potential of ML in revolutionizing TBI care. This review concludes by emphasizing the importance of bridging the gap between theoretical research and real-world clinical application and the necessity of addressing the ethical and privacy implications of integrating ML into healthcare.
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