ArticleBMC medical imaging2021
Efficiency of a deep learning-based artificial intelligence diagnostic system in spontaneous intracerebral hemorrhage volume measurement.
Article in BMC medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
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10 citing papers in PubMed, 2 syntheses or guidelines pooled it, 32 citations in OpenAlex.
- Deep learning-based automated segmentation of intracerebral haemorrhage, intraventricular haemorrhage and perihaematomal oedema on non-contrast CT.European stroke journal · 2026Pooled it
- Systematic review of commercial artificial intelligence tools for the detection and volume quantification in intracerebral hemorrhage.European radiology · 2026Pooled it
- AI-assisted versus fully automated volumetry for hemorrhage quantification in spontaneous ICH with ventricular extension: A multi-center study.European journal of radiology open · 2026Article
- Review
- Crowd-Sourced Deep Learning for Intracranial Hemorrhage Identification: Wisdom of Crowds or Laissez-Faire.AJNR. American journal of neuroradiology · 2023Article
- External Validation and Retraining of DeepBleed: The First Open-Source 3D Deep Learning Network for the Segmentation of Spontaneous Intracerebral and Intraventricular Hemorrhage.Journal of clinical medicine · 2023Article
- Role of artificial intelligence and machine learning in the diagnosis of cerebrovascular disease.Frontiers in human neuroscience · 2023Review
- The Added Value of Intraventricular Hemorrhage on the Radiomics Analysis for the Prediction of Hematoma Expansion of Spontaneous Intracerebral Hemorrhage.Diagnostics (Basel, Switzerland) · 2022Article
- WBC image classification and generative models based on convolutional neural network.BMC medical imaging · 2022Article
- Fusion-Based Deep Learning with Nature-Inspired Algorithm for Intracerebral Haemorrhage Diagnosis.Journal of healthcare engineering · 2022Article
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAccurate 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.
methodsWe 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.
resultsIn 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).
conclusionsThe 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.
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