使用大模型预测结节性甲状腺肿的全流程系统技术方案

1. 术前评估与预测模块

1.1 多模态数据融合引擎

# 伪代码：多模态特征融合算法
class MultiModalFusion(nn.Module):def __init__(self):super().__init__()self.img_encoder = ResNet50(pretrained=True)  # 超声影像编码self.lab_encoder = MLP(input_dim=12, hidden_dim=64)  # 实验室指标编码self.text_encoder = BertModel.from_pretrained("bert-base-chinese")  # 病历文本编码self.fusion_layer = CrossAttention(input_dim=256)  # 跨模态注意力融合def forward(self, images, labs, texts):img_feat = self.img_encoder(images)lab_feat = self.lab_encoder(labs)text_feat = self.text_encoder(**texts).last_hidden_state.mean(dim=1)fused_feat = self.fusion_layer(img_feat, lab_feat, text_feat)return fused_feat

1.2 风险预测流程图

2. 术中智能辅助系统

2.1 实时影像分析模块

# 伪代码：术中超声影像分析
def realtime_ultrasound_analysis(frame):# 甲状腺区域检测roi = YOLOv8(frame, conf=0.85)# 结节分割与特征提取mask = UNet(roi)features = {"size": np.max(mask.shape),"echogenicity": calc_echogenicity(roi, mask),"vascularity": calc_vascular_index(frame, mask)}# 实时风险评估risk_score =术中模型.predict(features)return {"mask": mask, "risk": risk_score}

2.2 手术导航流程图