Siniscalchi Antonio, Inghingolo Vincenzo, Lochner Piergiorgio, Malferrari Giovanni
Department of Neurology and Stroke Unit, Azienda Ospedaliera di Cosenza, Italy.
Neurology Unit and Stroke Unit, Department of Medicine, Casa Sollievo della Sofferenza, Italy.
Curr Med Imaging. 2025;21:e15734056331493. doi: 10.2174/0115734056331493241217075436.
Transcranial Doppler is an instrumental ultrasound method capable of providing data on various brain pathologies, in particular, the study of cerebral hemodynamics in stroke, quickly, economically, and with repeatability of the data themselves. However, literature reviews from clinical studies and clinical trials reported that it is an operator-dependent method, and the data can be influenced by external factors, such as noise, which may require greater standardization of the parameters. Artificial intelligence can be utilized on transcranial Doppler to increase the accuracy and precision of the data collected while decreasing operator dependencies. In a time-dependent pathology, such as stroke, characterized by hemodynamic evolution, the use of artificial intelligence in transcranial Doppler ultrasound could represent beneficial support for better diagnosis and treatment in time-dependent pathologies, such as stroke.
经颅多普勒是一种仪器超声方法,能够提供有关各种脑部病变的数据,特别是在中风中研究脑血流动力学,具有快速、经济且数据本身可重复性的特点。然而,临床研究和临床试验的文献综述报告称,它是一种依赖操作者的方法,数据可能会受到外部因素的影响,如噪音,这可能需要对参数进行更大程度的标准化。人工智能可用于经颅多普勒,以提高所收集数据的准确性和精确性,同时减少对操作者的依赖。在诸如中风这种以血流动力学演变为特征的时间依赖性病变中,在经颅多普勒超声中使用人工智能可为诸如中风这类时间依赖性病变的更好诊断和治疗提供有益支持。