Abstract：
OBJECTIVE: To evaluate a deep learning-based pipeline using a Dense-UNet architecture for the assessment of acute intracranial hemorrhage (ICH) on non-contrast computed tomography (NCCT) head scans after traumatic brain injury (TBI).
METHODS: This retrospective study was conducted using a prototype algorithm that evaluated 502 NCCT head scans with ICH in context of TBI. Four board-certified radiologists evaluated in consensus the CT scans to establish the standard of reference for hemorrhage presence and type of ICH. Consequently, all CT scans were independently analyzed by the algorithm and a board-certified radiologist to assess the presence and type of ICH. Additionally, the time to diagnosis was measured for both methods.
RESULTS: A total of 405/502 patients presented ICH classified in the following types: intraparenchymal (n = 172); intraventricular (n = 26); subarachnoid (n = 163); subdural (n = 178); and epidural (n = 15). The algorithm showed high diagnostic accuracy (91.24%) for the assessment of ICH with a sensitivity of 90.37% and specificity of 94.85%. To distinguish the different ICH types, the algorithm had a sensitivity of 93.47% and a specificity of 99.79%, with an accuracy of 98.54%. To detect midline shift, the algorithm had a sensitivity of 100%. In terms of processing time, the algorithm was significantly faster compared to the radiologist\'s time to first diagnosis (15.37 ± 1.85 vs 277 ± 14 s, p < 0.001).
CONCLUSIONS: A novel deep learning algorithm can provide high diagnostic accuracy for the identification and classification of ICH from unenhanced CT scans, combined with short processing times. This has the potential to assist and improve radiologists\' ICH assessment in NCCT scans, especially in emergency scenarios, when time efficiency is needed.
METHODS: This retrospective study was conducted using a prototype algorithm that evaluated 502 NCCT head scans with ICH in context of TBI. Four board-certified radiologists evaluated in consensus the CT scans to establish the standard of reference for hemorrhage presence and type of ICH. Consequently, all CT scans were independently analyzed by the algorithm and a board-certified radiologist to assess the presence and type of ICH. Additionally, the time to diagnosis was measured for both methods.
RESULTS: A total of 405/502 patients presented ICH classified in the following types: intraparenchymal (n = 172); intraventricular (n = 26); subarachnoid (n = 163); subdural (n = 178); and epidural (n = 15). The algorithm showed high diagnostic accuracy (91.24%) for the assessment of ICH with a sensitivity of 90.37% and specificity of 94.85%. To distinguish the different ICH types, the algorithm had a sensitivity of 93.47% and a specificity of 99.79%, with an accuracy of 98.54%. To detect midline shift, the algorithm had a sensitivity of 100%. In terms of processing time, the algorithm was significantly faster compared to the radiologist\'s time to first diagnosis (15.37 ± 1.85 vs 277 ± 14 s, p < 0.001).
CONCLUSIONS: A novel deep learning algorithm can provide high diagnostic accuracy for the identification and classification of ICH from unenhanced CT scans, combined with short processing times. This has the potential to assist and improve radiologists\' ICH assessment in NCCT scans, especially in emergency scenarios, when time efficiency is needed.
摘要：
目的：使用Dense-UNet架构评估基于深度学习的管道，以评估创伤性脑损伤（TBI）后的非对比计算机断层扫描（NCCT）头部扫描的急性颅内出血（ICH）。
方法：这项回顾性研究是使用原型算法进行的，该算法在TBI背景下评估了502例ICH的NCCT头部扫描。四名委员会认证的放射科医师一致评估了CT扫描，以建立出血存在和ICH类型的参考标准。因此,所有CT扫描由算法和董事会认证的放射科医师独立分析,以评估ICH的存在和类型.此外,对两种方法的诊断时间进行了测定.
结果：共有405/502例患者出现ICH，分为以下类型：实质内（n=172）；脑室内（n=26）；蛛网膜下（n=163）；硬膜下（n=178）；和硬膜外（n=15）。该算法对ICH的评估显示出较高的诊断准确性（91.24％），敏感性为90.37％，特异性为94.85％。为了区分不同的ICH类型，该算法的灵敏度为93.47%，特异性为99.79%，准确率为98.54%。要检测中线偏移，该算法的灵敏度为100%。在处理时间上,与放射科医生的首次诊断时间相比，该算法明显更快(15.37±1.85vs277±14s，p<0.001)。
结论：一种新颖的深度学习算法可以为未增强CT扫描对ICH的识别和分类提供很高的诊断准确性，结合短处理时间。这有可能帮助和改善放射科医师在NCCT扫描中的ICH评估，尤其是在紧急情况下，当需要时间效率时。
方法：这项回顾性研究是使用原型算法进行的，该算法在TBI背景下评估了502例ICH的NCCT头部扫描。四名委员会认证的放射科医师一致评估了CT扫描，以建立出血存在和ICH类型的参考标准。因此,所有CT扫描由算法和董事会认证的放射科医师独立分析,以评估ICH的存在和类型.此外,对两种方法的诊断时间进行了测定.
结果：共有405/502例患者出现ICH，分为以下类型：实质内（n=172）；脑室内（n=26）；蛛网膜下（n=163）；硬膜下（n=178）；和硬膜外（n=15）。该算法对ICH的评估显示出较高的诊断准确性（91.24％），敏感性为90.37％，特异性为94.85％。为了区分不同的ICH类型，该算法的灵敏度为93.47%，特异性为99.79%，准确率为98.54%。要检测中线偏移，该算法的灵敏度为100%。在处理时间上,与放射科医生的首次诊断时间相比，该算法明显更快(15.37±1.85vs277±14s，p<0.001)。
结论：一种新颖的深度学习算法可以为未增强CT扫描对ICH的识别和分类提供很高的诊断准确性，结合短处理时间。这有可能帮助和改善放射科医师在NCCT扫描中的ICH评估，尤其是在紧急情况下，当需要时间效率时。
