Novel Non-Invasive Deep Learning Model Based on Tongue Images for Early Differentiation of Ischemic and Hemorrhagic Stroke

Stroke, the second leading cause of death globally, demands rapid differentiation between ischemic stroke (IS) and intracerebral hemorrhage (ICH) to guide optimal therapeutic strategies. Current diagnostic modalities (computed tomography [CT] or magnetic resonance imaging [MRI]) are hindered by limited accessibility and prolonged turnaround times, particularly in resource-constrained settings. Herein, we propose StrokeNet, a deep learning model integrating a modified ResNet-18 backbone with wavelet-based hierarchical attention (HWAttention) modules, for the early differentiation of stroke subtypes via tongue images, which serve as an underutilized non-invasive diagnostic tool in modern stroke care. A single-center cross-sectional study was conducted, enrolling 201 acute stroke patients (144 IS, 57 ICH). Tongue regions were accurately segmented using the YOLOv5 object detection model, and six-channel composite images (combining full facial and cropped tongue RGB features) were constructed as model inputs. On the internal validation set, StrokeNet achieved a classification accuracy of 82.93%, an area under the receiver operating characteristic curve (AUC) of 0.8463 (95% CI:0.7125–0.9799), a sensitivity of 89.47% (for IS), a specificity of 68.00% (for ICH), and an F1-score of 0.8793. The model outperformed mainstream baseline architectures (EfficientNet-B2, ResNet-18) across key metrics, with ablation experiments confirming that the HWAttention module and six-channel input design synergistically enhanced discriminative feature capture. Clinical risk factor analysis identified age > 60 years as an independent predictor of ICH (adjusted OR = 2.326, 95% CI: 1.243–4.352, p = 0.008). Subgroup analysis validated the model’s robustness across age, gender, comorbidity status and neurological deficit severity. To our knowledge, this study is the first to leverage artificial intelligence (AI)-driven tongue imaging for stroke subtype differentiation. StrokeNet offers non-invasiveness, rapid diagnostic turnaround and low cost, establishing a novel paradigm to optimize emergency triage in resource-limited healthcare settings and address global disparities in stroke care.