Importance: Non-contrast head CT scan is the current standard for initial
imaging of patients with head trauma or stroke symptoms.
Objective: To develop and validate a set of deep learning algorithms for
automated detection of following key findings from non-contrast head CT scans:
intracranial hemorrhage (ICH) and its types, intraparenchymal (IPH),
intraventricular (IVH), subdural (SDH), extradural (EDH) and subarachnoid (SAH)
hemorrhages, calvarial fractures, midline shift and mass effect.
Design and Settings: We retrospectively collected a dataset containing
313,318 head CT scans along with their clinical reports from various centers. A
part of this dataset (Qure25k dataset) was used to validate and the rest to
develop algorithms. Additionally, a dataset (CQ500 dataset) was collected from
different centers in two batches B1 & B2 to clinically validate the algorithms.
Main Outcomes and Measures: Original clinical radiology report and consensus
of three independent radiologists were considered as gold standard for Qure25k
and CQ500 datasets respectively. Area under receiver operating characteristics
curve (AUC) for each finding was primarily used to evaluate the algorithms.
Results: Qure25k dataset contained 21,095 scans (mean age 43.31; 42.87%
female) while batches B1 and B2 of CQ500 dataset consisted of 214 (mean age
43.40; 43.92% female) and 277 (mean age 51.70; 30.31% female) scans
respectively. On Qure25k dataset, the algorithms achieved AUCs of 0.9194,
0.8977, 0.9559, 0.9161, 0.9288 and 0.9044 for detecting ICH, IPH, IVH, SDH, EDH
and SAH respectively. AUCs for the same on CQ500 dataset were 0.9419, 0.9544,
0.9310, 0.9521, 0.9731 and 0.9574 respectively. For detecting calvarial
fractures, midline shift and mass effect, AUCs on Qure25k dataset were 0.9244,
0.9276 and 0.8583 respectively, while AUCs on CQ500 dataset were 0.9624, 0.9697
and 0.9216 respectively.

本研究开发和验证了一组深度学习算法，用于自动检测无对比剂头颅 CT 扫描的关键发现：颅内出血（ICH）及其类型，脑内实质（IPH），脑室内（IVH），硬膜下（SDH），硬脑膜外 (EDH) 和蛛网膜下 (SAH)，骨盖骨折，中线移位和占位效应，AUCs 测评结果显示算法的识别准确率达到了很高的水平。