Evaluating the value of machine learning models for predicting hematoma expansion in acute spontaneous intracerebral hemorrhage based on CT imaging features of hematomas and surrounding oedema
ObjectiveThis study evaluates the utility of artificial intelligence (AI) for automated segmentation of intracranial hematomas and surrounding oedema in non-contrast computed tomography (CT) images. Additionally, it aims to extract imaging features for developing machine learning models to predict h...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Neurology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2025.1567525/full |
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| author | Tianyu Yang Tianyu Yang Zhen Zhao Yan Gu Shengkai Yang Yonggang Zhang Lei Li Ting Wang Zhongchang Miao |
| author_facet | Tianyu Yang Tianyu Yang Zhen Zhao Yan Gu Shengkai Yang Yonggang Zhang Lei Li Ting Wang Zhongchang Miao |
| author_sort | Tianyu Yang |
| collection | DOAJ |
| description | ObjectiveThis study evaluates the utility of artificial intelligence (AI) for automated segmentation of intracranial hematomas and surrounding oedema in non-contrast computed tomography (CT) images. Additionally, it aims to extract imaging features for developing machine learning models to predict hematoma expansion in acute spontaneous intracerebral hemorrhage (sICH).MethodsData from 183 patients with acute spontaneous hemorrhage, treated at Lianyungang Hospital Affiliated to Xuzhou Medical University between January 2020 and December 2023, were retrospectively analyzed. Patients were divided into training (n = 128) and testing (n = 55) sets in a 7:3 ratio. CT images were segmented using United Imaging uAI software and both imaging features and clinical characteristics were extracted. Independent risk factors were identified through univariate analysis and least absolute shrinkage and selection operator (LASSO) regression. Machine learning algorithms were applied to construct predictive models for hematoma expansion. Model performance was evaluated using receiver operating characteristic (ROC) curves and the area under the curve (AUC).ResultsEight feature parameters were extracted from the CT images. The comprehensive model achieved an AUC of 0.9027, with a sensitivity of 0.8235 and specificity of 0.8831. A simplified model utilizing four imaging features yielded an AUC of 0.8897, with a sensitivity of 0.7451 and specificity of 0.9221, slightly underperforming compared to the comprehensive model. Incorporating the subjective ‘swirl sign’, identified as the most significant feature in univariate analysis, into the simplified model enhanced its performance. This optimized model achieved an AUC of 0.9524, with a sensitivity of 0.9412 and specificity of 0.9091, surpassing both the comprehensive and simplified models.ConclusionThe optimized model, based on CT imaging features of hematomas and surrounding oedema, offers a practical and reliable tool for predicting hematoma expansion in sICH. Its robust performance supports its utility in emergency settings to guide clinical decision-making effectively. |
| format | Article |
| id | doaj-art-c6c8ef6aa2084b61a2ba1ee4074492ab |
| institution | Kabale University |
| issn | 1664-2295 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Neurology |
| spelling | doaj-art-c6c8ef6aa2084b61a2ba1ee4074492ab2025-08-20T03:24:48ZengFrontiers Media S.A.Frontiers in Neurology1664-22952025-06-011610.3389/fneur.2025.15675251567525Evaluating the value of machine learning models for predicting hematoma expansion in acute spontaneous intracerebral hemorrhage based on CT imaging features of hematomas and surrounding oedemaTianyu Yang0Tianyu Yang1Zhen Zhao2Yan Gu3Shengkai Yang4Yonggang Zhang5Lei Li6Ting Wang7Zhongchang Miao8Department of Radiology, The Affiliated Lianyungang Hospital of Xuzhou Medical University, Lianyungang, ChinaDepartment of Radiology, Affiliated Binhai Hospital, Kangda College of Nanjing Medical University, Yancheng, ChinaJiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, ChinaDepartment of Radiology, The Affiliated Lianyungang Hospital of Xuzhou Medical University, Lianyungang, ChinaDepartment of Radiology, Affiliated Binhai Hospital, Kangda College of Nanjing Medical University, Yancheng, ChinaDepartment of Radiology, The Affiliated Lianyungang Hospital of Xuzhou Medical University, Lianyungang, ChinaDepartment of Radiology, The Affiliated Lianyungang Hospital of Xuzhou Medical University, Lianyungang, ChinaDepartment of Radiology, Affiliated Binhai Hospital, Kangda College of Nanjing Medical University, Yancheng, ChinaDepartment of Radiology, The Affiliated Lianyungang Hospital of Xuzhou Medical University, Lianyungang, ChinaObjectiveThis study evaluates the utility of artificial intelligence (AI) for automated segmentation of intracranial hematomas and surrounding oedema in non-contrast computed tomography (CT) images. Additionally, it aims to extract imaging features for developing machine learning models to predict hematoma expansion in acute spontaneous intracerebral hemorrhage (sICH).MethodsData from 183 patients with acute spontaneous hemorrhage, treated at Lianyungang Hospital Affiliated to Xuzhou Medical University between January 2020 and December 2023, were retrospectively analyzed. Patients were divided into training (n = 128) and testing (n = 55) sets in a 7:3 ratio. CT images were segmented using United Imaging uAI software and both imaging features and clinical characteristics were extracted. Independent risk factors were identified through univariate analysis and least absolute shrinkage and selection operator (LASSO) regression. Machine learning algorithms were applied to construct predictive models for hematoma expansion. Model performance was evaluated using receiver operating characteristic (ROC) curves and the area under the curve (AUC).ResultsEight feature parameters were extracted from the CT images. The comprehensive model achieved an AUC of 0.9027, with a sensitivity of 0.8235 and specificity of 0.8831. A simplified model utilizing four imaging features yielded an AUC of 0.8897, with a sensitivity of 0.7451 and specificity of 0.9221, slightly underperforming compared to the comprehensive model. Incorporating the subjective ‘swirl sign’, identified as the most significant feature in univariate analysis, into the simplified model enhanced its performance. This optimized model achieved an AUC of 0.9524, with a sensitivity of 0.9412 and specificity of 0.9091, surpassing both the comprehensive and simplified models.ConclusionThe optimized model, based on CT imaging features of hematomas and surrounding oedema, offers a practical and reliable tool for predicting hematoma expansion in sICH. Its robust performance supports its utility in emergency settings to guide clinical decision-making effectively.https://www.frontiersin.org/articles/10.3389/fneur.2025.1567525/fullspontaneous intracerebral hemorrhagehematoma expansionimaging featuresmachine learning modelscomputed tomography scan |
| spellingShingle | Tianyu Yang Tianyu Yang Zhen Zhao Yan Gu Shengkai Yang Yonggang Zhang Lei Li Ting Wang Zhongchang Miao Evaluating the value of machine learning models for predicting hematoma expansion in acute spontaneous intracerebral hemorrhage based on CT imaging features of hematomas and surrounding oedema Frontiers in Neurology spontaneous intracerebral hemorrhage hematoma expansion imaging features machine learning models computed tomography scan |
| title | Evaluating the value of machine learning models for predicting hematoma expansion in acute spontaneous intracerebral hemorrhage based on CT imaging features of hematomas and surrounding oedema |
| title_full | Evaluating the value of machine learning models for predicting hematoma expansion in acute spontaneous intracerebral hemorrhage based on CT imaging features of hematomas and surrounding oedema |
| title_fullStr | Evaluating the value of machine learning models for predicting hematoma expansion in acute spontaneous intracerebral hemorrhage based on CT imaging features of hematomas and surrounding oedema |
| title_full_unstemmed | Evaluating the value of machine learning models for predicting hematoma expansion in acute spontaneous intracerebral hemorrhage based on CT imaging features of hematomas and surrounding oedema |
| title_short | Evaluating the value of machine learning models for predicting hematoma expansion in acute spontaneous intracerebral hemorrhage based on CT imaging features of hematomas and surrounding oedema |
| title_sort | evaluating the value of machine learning models for predicting hematoma expansion in acute spontaneous intracerebral hemorrhage based on ct imaging features of hematomas and surrounding oedema |
| topic | spontaneous intracerebral hemorrhage hematoma expansion imaging features machine learning models computed tomography scan |
| url | https://www.frontiersin.org/articles/10.3389/fneur.2025.1567525/full |
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