Gender disparity in stoke: Women have higher ICH scores than men at initial ED presentation for intracerebral hemorrhage

Ganti, Latha; Shameem, Masra; Houck, Jessica; Stead, Thor S; Stead, Tej G; Cesarz, Taylor; Mirajkar, Amber

doi:10.1016/j.jnma.2023.01.013

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…Should these models perform sub-optimally on certain tasks, the efficacy of our methodology could be compromised. Lastly, our model integrated CT imaging data and text, which has been demonstrated to influence prognosis classification outcomes, such as gender [ 27 , 28 , 29 ], early cognitive status [ 30 ], and the location and volume of the hemorrhage [ 31 , 32 ]. Therefore, any loss of information between modalities might impair the model’s performance.…”

Section: Discussionmentioning

confidence: 99%

Intracerebral Hemorrhage Prognosis Classification via Joint-Attention Cross-Modal Network

Xu,

Fu,

Jin

et al. 2024

Brain Sciences

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Intracerebral hemorrhage (ICH) is a critical condition characterized by a high prevalence, substantial mortality rates, and unpredictable clinical outcomes, which results in a serious threat to human health. Improving the timeliness and accuracy of prognosis assessment is crucial to minimizing mortality and long-term disability associated with ICH. Due to the complexity of ICH, the diagnosis of ICH in clinical practice heavily relies on the professional expertise and clinical experience of physicians. Traditional prognostic methods largely depend on the specialized knowledge and subjective judgment of healthcare professionals. Meanwhile, existing artificial intelligence (AI) methodologies, which predominantly utilize features derived from computed tomography (CT) scans, fall short of capturing the multifaceted nature of ICH. Although existing methods are capable of integrating clinical information and CT images for prognosis, the effectiveness of this fusion process still requires improvement. To surmount these limitations, the present study introduces a novel AI framework, termed the ICH Network (ICH-Net), which employs a joint-attention cross-modal network to synergize clinical textual data with CT imaging features. The architecture of ICH-Net consists of three integral components: the Feature Extraction Module, which processes and abstracts salient characteristics from the clinical and imaging data, the Feature Fusion Module, which amalgamates the diverse data streams, and the Classification Module, which interprets the fused features to deliver prognostic predictions. Our evaluation, conducted through a rigorous five-fold cross-validation process, demonstrates that ICH-Net achieves a commendable accuracy of up to 87.77%, outperforming other state-of-the-art methods detailed within our research. This evidence underscores the potential of ICH-Net as a formidable tool in prognosticating ICH, promising a significant advancement in clinical decision-making and patient care.

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