Kotronias Rafail A, Fielding Kirsty, Greenhalgh Charlotte, Lee Regent, Alkhalil Mohammad, Marin Federico, Emfietzoglou Maria, Banning Adrian P, Vallance Claire, Channon Keith M, De Maria Giovanni Luigi
Oxford Heart Centre, National Institute for Health and Care Research (NIHR) Biomedical Research Centre, Oxford University Hospitals, Oxford, United Kingdom.
Department of Cardiovascular Medicine, University of Oxford, Oxford, United Kingdom.
Front Cardiovasc Med. 2022 Sep 20;9:930015. doi: 10.3389/fcvm.2022.930015. eCollection 2022.
We set out to further develop reflectance spectroscopy for the characterisation and quantification of coronary thrombi. Additionally, we explore the potential of our approach for use as a risk stratification tool by exploring the relation of reflectance spectra to indices of coronary microvascular injury.
We performed hyperspectral imaging of coronary thrombi aspirated from 306 patients presenting with ST-segment elevation acute coronary syndrome (STEACS). Spatially resolved reflected light spectra were analysed using unsupervised machine learning approaches. Invasive [index of coronary microvascular resistance (IMR)] and non-invasive [microvascular obstruction (MVO) at cardiac magnetic resonance imaging] indices of coronary microvascular injury were measured in a sub-cohort of 36 patients. The derived spectral signatures of coronary thrombi were correlated with both invasive and non-invasive indices of coronary microvascular injury. Successful machine-learning-based classification of the various thrombus image components, including differentiation between blood and thrombus, was achieved when classifying the pixel spectra into 11 groups. Fitting of the spectra to basis spectra recorded for separated blood components confirmed excellent correlation with visually inspected thrombi. In the 36 patients who underwent successful thrombectomy, spectral signatures were found to correlate well with the index of microcirculatory resistance and microvascular obstruction; : 0.80, < 0.0001, = 21 and : 0.64, = 0.02, = 17, respectively.
Machine learning assisted reflectance spectral analysis can provide a measure of thrombus composition and evaluate coronary microvascular injury in patients with STEACS. Future work will further validate its deployment as a point-of-care diagnostic and risk stratification tool for STEACS care.
我们着手进一步开发反射光谱技术，用于冠状动脉血栓的特征描述和定量分析。此外，我们通过探究反射光谱与冠状动脉微血管损伤指标之间的关系，探索将我们的方法用作风险分层工具的潜力。
我们对306例ST段抬高型急性冠状动脉综合征（STEACS）患者吸出的冠状动脉血栓进行了高光谱成像。使用无监督机器学习方法分析空间分辨反射光谱。在36例患者的亚组中测量了冠状动脉微血管损伤的有创指标[冠状动脉微血管阻力指数（IMR）]和无创指标[心脏磁共振成像时的微血管阻塞（MVO）]。冠状动脉血栓的衍生光谱特征与冠状动脉微血管损伤的有创和无创指标均相关。当将像素光谱分类为11组时，成功实现了基于机器学习的各种血栓图像成分的分类，包括血液与血栓的区分。将光谱与分离的血液成分记录的基础光谱进行拟合，证实与目视检查的血栓具有极好的相关性。在36例成功进行血栓切除术的患者中，发现光谱特征与微循环阻力指数和微血管阻塞密切相关；分别为：r = 0.80，P < 0.0001，n = 21和r = 0.64，P = 0.02，n = 17。
机器学习辅助的反射光谱分析可以提供血栓成分的测量方法，并评估STEACS患者的冠状动脉微血管损伤。未来的工作将进一步验证其作为STEACS护理的即时诊断和风险分层工具的应用。
