Advance Thermography diagnostics: automatic algorithm to find out acute sinusitis

I. M. Dolgov, Yu. V. Karamyshev, I. S. Zheleznyak, A. A. Karamysheva, A. I. Makhnovsky, D. N. Khriupkovskiy
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Abstract

Development of medical imaging techniques (including medical thermography) provided an opportunity to mine data that can be explored for the development of clinical decision support systems. Aim. Find out quantitative thermography criteria for acute rhinosinusitis and implement these criteria into automated analysis protocols to create a diagnostic complex suitable for use by professionals of any medical speciality. Material, methods. After necessary adaptation, facial thermography by mean of thermography camera ТВС300-меd LLC “СТК СИЛАР” Russia, (384x288 pixel, 30 mK) was performed in 100 healthy volunteers and 305 patients with suspected acute rhinosinusitis (in 173 of them diagnosis was supported). Resulted thermograms were processed in “cloud service” TVision (LLC “Dignosys”, Russia). Point and regions of interest (ROI), namely, point in the middle of the line, connected inner edge of eyebrows (Тср) and ROI, covered the projections of maxillary sinuses were marked automatically. Difference between temperature in Тср point and mean temperature in each ROI (ΔТr/ΔТl), as well as mean temperature between ROI were calculated (ΔTпаз). Result. The best results ( sensitivity 82%, specificity 74%, accuracy 78%) were received when complex of ΔТr/ΔТl ≤0 and module |ΔTпаз| ≥0,5°C (both or at least one criterion) was applied. Based on these data, automatic algorithm of thermography acute rhinosinusitis diagnostics was created to use in primary medical care routine as clinical decision support systems.
先进的热成像诊断:自动算法发现急性鼻窦炎
医学成像技术(包括医学热成像)的发展为临床决策支持系统的开发提供了挖掘数据的机会。的目标。找出急性鼻窦炎的定量热成像标准,并将这些标准实施到自动分析协议中,以创建适合任何医学专业人员使用的诊断复合体。材料、方法。对100名健康志愿者和305例疑似急性鼻窦炎患者(其中173例诊断得到支持)进行面部热像仪ТВС300-меd LLC“СТК СИЛАР”俄罗斯(384x288像素,30 mK)的调整后进行面部热像仪。生成的热像图在“云服务”TVision (LLC“Dignosys”,Russia)中处理。感兴趣点和感兴趣区域(ROI),即线中间的点,连接眉毛内缘(Тср)和感兴趣区域,覆盖上颌窦的投影被自动标记。计算Тср点温度与各ROI平均温度之差(ΔТr/ΔТl)以及ROI间平均温度之差(ΔTпаз)。结果。当配合物ΔТr/ΔТl≤0,模组|ΔTпаз|≥0,5°C(两种或至少一种标准)时,获得最佳结果(灵敏度82%,特异性74%,准确性78%)。基于这些数据,建立了急性鼻窦炎热成像诊断的自动算法,作为临床决策支持系统用于初级医疗常规。
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