Efficiency of a deep learning-based artificial intelligence diagnostic system in spontaneous intracerebral hemorrhage volume measurement

• Published in: BMC medical imaging Vol. 21; no. 1; pp. 125 - 9
• Main Authors: Wang, Tao, Song, Na, Liu, Lingling, Zhu, Zichao, Chen, Bing, Yang, Wenjun, Chen, Zhiqiang
• Format: Journal Article
• Language: English
• Published: London BioMed Central 13.08.2021; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Accuracy; Acute Disease; Adult; Aged; Agreements; Algorithms; Artificial intelligence; Automation; Cerebral Hemorrhage - complications; Cerebral Hemorrhage - diagnosis; Cerebral Hemorrhage - diagnostic imaging; Cerebral Intraventricular Hemorrhage - complications; Cerebral Intraventricular Hemorrhage - diagnosis; Cerebral Intraventricular Hemorrhage - diagnostic imaging; Comparative analysis; Computed tomography; Correlation coefficient; Correlation coefficients; Deep Learning; Diagnosis, Computer-Assisted - methods; Diagnostic systems; Error analysis; Female; Hematoma; Hemorrhage; Humans; Imaging; Intracerebral hemorrhage; Machine learning; Male; Measurement; Medical diagnosis; Medicine; Medicine & Public Health; Middle Aged; Mortality; Neural networks; Patients; Physicians; Prognosis; Radiology; Retrospective Studies; Segmentation; Software; Spontaneous intracerebral hemorrhage; Statistical analysis; Stroke; Tomography, X-Ray Computed; Volume measurement; Volumetric analysis; Volumetrics; United States > US; Deep learning; Volumetrics; Artificial intelligence; Intracerebral hemorrhage; Spontaneous intracerebral hemorrhage
• ISSN: 1471-2342; 1471-2342
• DOI: 10.1186/s12880-021-00657-6

Background Accurate measurement of hemorrhage volume is critical for both the prediction of prognosis and the selection of appropriate clinical treatment after spontaneous intracerebral hemorrhage (ICH). This study aimed to evaluate the performance and accuracy of a deep learning-based automated segmentation algorithm in segmenting spontaneous intracerebral hemorrhage (ICH) volume either with or without intraventricular hemorrhage (IVH) extension. We compared this automated pipeline with two manual segmentation techniques. Methods We retrospectively reviewed 105 patients with acute spontaneous ICH. Depending on the presence of IVH extension, patients were divided into two groups: ICH without (n = 56) and with IVH (n = 49). ICH volume of the two groups were segmented and measured using a deep learning-based artificial intelligence (AI) diagnostic system and computed tomography-based planimetry (CTP), and the ABC/2 score were used to measure hemorrhage volume in the ICH without IVH group. Correlations and agreement analyses were used to analyze the differences in volume and length of processing time among the three segmentation approaches. Results In the ICH without IVH group, the ICH volumes measured using AI and the ABC/2 score were comparable to CTP segmentation. Strong correlations were observed among the three segmentation methods ( r  = 0.994, 0.976, 0.974; P  < 0.001; concordance correlation coefficient [CCC] = 0.993, 0.968, 0.967). But the absolute error of the ICH volume measured by the ABC/2 score was greater than that of the algorithm ( P  < 0.05). In the ICH with IVH group, there is no significant differences were found between algorithm and CTP( P  = 0.614). The correlation and agreement between CTP and AI were strong ( r  = 0.996, P  < 0.001; CCC = 0.996). The AI segmentation took a significantly shorter amount of time than CTP ( P  < 0.001), but was slightly longer than ABC/2 score technique ( P  = 0.002). Conclusions The deep learning-based AI diagnostic system accurately quantified volumes of acute spontaneous ICH with high fidelity and greater efficiency compared to the CTP measurement and more accurately than the ABC/2 scores. We believe this is a promising tool to help physicians achieve precise ICH quantification in practice.