一、系统架构总览

二、核心模块详细设计

1. 术前预测系统

算法伪代码：

# Transformer-TCN 融合模型
class HybridModel(nn.Module):
    def __init__(self):
        # 时序特征处理
        self.tcn = TemporalConvNet(num_inputs=128, num_channels=[64, 32])
        # 多模态融合
        self.transformer = TransformerEncoder(
            layers=4, 
            d_model=256,
            heads=8
        )
        # 风险预测头
        self.risk_head = nn.Sequential(
            nn.Linear(256, 128),
            nn.ReLU(),
            nn.Linear(128, 3)  # 输出3类风险等级
        
    def forward(self, clinical, imaging, temporal):
        # 临床数据处理
        clinical_feat = self.clinical_net(clinical)
        # 影像特征提取
        img_feat = self.resnet3d(imaging)
        # 时序特征处理
        temp_feat = self.tcn(temporal)
        
        # 多模态融合
        fused = torch.cat([clinical_feat, img_feat, temp_feat], dim=1)
        # Transformer编码
        encoded = self.transformer(fused)
        # 风险预测
        risk = self.risk_head(encoded[:, -1])
        return risk

# 手术方案生成器
def generate_surgery_plan(risk_score, stenosis_map):
    if risk_score > 0.85:
        plan = "紧急CEA/CAS手术"
    elif risk_score > 0.6:
        plan = "48小时内血管介入"
    else:
        plan = "药物治疗+监测"
    
    # 基于狭窄定位生成细节
    if stenosis_map["ICA"] > 70%:
        plan += " + 颈动脉支架"
    if stenosis_map["MCA"] > 50%:
        plan += " + 抗凝强化"
    return plan

2. 术中决策系统

实时推理伪代码：

def intraoperative_decision(vitals):
    # 实时特征提取
    features = extract_features(
        eeg = vitals["eeg_power"],
        bp = [vitals["systolic"], vitals["diastolic"]],
        spo2 = vitals["oxygen_sat"]
    )
    
    # ONNX加速推理
    session = ort.InferenceSession("intraop_model.onnx")
    inputs = {
   "input": features}
    outputs = session.run(None, inputs)
    
    # 解析输出
    risk = outputs[0][0]
    action = outputs[1][0]
    
    # 执行决策
    if action == "ADJUST_ANESTHESIA":
        adjust_anesthesia_pump(risk * 0.2)  # 剂量调整系数
    elif action == "ALTER_PATH":
        update_surgical_navigation(risk)

# 麻醉调控算法
def adjust_anesthesia_pump(adjustment_factor):
    current_dose = get_current_dose()
    new_dose = current_dose * (1 + adjustment_factor)
    # 安全范围限制
    new_dose = clamp(new_dose, MIN_DOSE, MAX_DOSE)
    send_to_pump(new_dose)

3. 术后管理系统