Head Non-contrast computed tomography (NCCT) scan remain the preferred primary imaging modality due to their widespread availability and speed. However, the current standard for manual annotations of abnormal brain tissue on head NCCT scans involves significant disadvantages like lack of cutoff standardization and degeneration identification. The recent advancement of deep learning-based computer-aided diagnostic (CAD) models in the multidisciplinary domain has created vast opportunities in neurological medical imaging. Significant literature has been published earlier in the automated identification of brain tissue on different imaging modalities. However, determining Intracranial hemorrhage (ICH) and infarct can be challenging due to image texture, volume size, and scan quality variability. This retrospective validation study evaluated a DL-based algorithm identifying ICH and infarct from head-NCCT scans. The head-NCCT scans dataset was collected consecutively from multiple diagnostic imaging centers across India. The study exhibits the potential and limitations of such DL-based software for introduction in routine workflow in extensive healthcare facilities.

翻译：头部非增强计算机断层扫描因其广泛可用性和快速成像优势，仍是首选的原始影像学检查方法。然而，当前头部非增强CT扫描中异常脑组织人工标注的标准流程存在显著缺陷，例如缺乏标准化临界值界定和退行性病变识别能力。近年来，基于深度学习的计算机辅助诊断模型在多学科领域的突破性进展，为神经医学影像学创造了广阔前景。已有大量文献报道了利用不同影像模态实现脑组织自动识别的成果，但由于图像纹理特征、病变体积大小及扫描质量差异，颅内出血与梗死的判定仍具挑战性。本回顾性验证研究评估了基于深度学习算法从头部非增强CT扫描中识别颅内出血与梗死的效能。研究数据来自印度多家影像诊断中心连续采集的头部非增强CT扫描数据集。本研究揭示了此类深度学习软件在大规模医疗机构常规工作流程中引入的潜力与局限性。