AI for Preoperative Planning and Surgical Simulation

Module: Healthcare AI | Difficulty: Advanced

Anatomical Measurement

Surgical Planning Score

3D Print Accuracy

Surgical Planning AI Applications

| Application | Input | Output | Accuracy | |------------|-------|--------|----------| | Implant Selection | CT/MRI | Size recommendation | 0.94 | | Approach Planning | 3D anatomy | Optimal corridor | 0.91 | | Risk Assessment | Patient data | Complication risk | 0.88 | | 3D Reconstruction | CT slices | Printable model | 0.96 | | VR Simulation | Patient anatomy | Training scenario | 0.93 |

import torch
import torch.nn as nn

class SurgicalPlanner(nn.Module):
    def __init__(self, anatomy_dim=256, patient_dim=20):
        super().__init__()
        self.anatomy_encoder = nn.Sequential(
            nn.Linear(anatomy_dim, 128), nn.ReLU(),
            nn.Linear(128, 64))
        self.patient_encoder = nn.Sequential(
            nn.Linear(patient_dim, 32), nn.ReLU(),
            nn.Linear(32, 64))
        self.planning_head = nn.Sequential(
            nn.Linear(128, 64), nn.ReLU(),
            nn.Linear(64, 10))

    def forward(self, anatomy_features, patient_features):
        anatomy = self.anatomy_encoder(anatomy_features)
        patient = self.patient_encoder(patient_features)
        combined = torch.cat([anatomy, patient], dim=1)
        plan = self.planning_head(combined)
        return plan

class ImplantSelector(nn.Module):
    def __init__(self, num_implants=50, feature_dim=100):
        super().__init__()
        self.feature_net = nn.Sequential(
            nn.Linear(feature_dim, 64), nn.ReLU(),
            nn.Linear(64, 32))
        self.implant_embed = nn.Embedding(num_implants, 32)
        self.scorer = nn.Linear(32, 1)

    def forward(self, patient_features, implant_ids):
        patient_emb = self.feature_net(patient_features)
        implant_emb = self.implant_embed(implant_ids)
        scores = self.scorer(patient_emb * implant_emb)
        return scores

planner = SurgicalPlanner(anatomy_dim=256, patient_dim=20)
anatomy = torch.randn(1, 256)
patient = torch.randn(1, 20)
plan = planner(anatomy, patient)
print(f'Surgical plan: {plan.shape}')

selector = ImplantSelector(num_implants=50, feature_dim=100)
patient_feat = torch.randn(1, 100)
implant_ids = torch.randint(0, 50, (1, 10))
scores = selector(patient_feat, implant_ids)
print(f'Implant scores: {scores.shape}')

Research Insight: AI-assisted surgical planning can significantly reduce operative time and complications for complex procedures. The integration of 3D printing with AI planning allows surgeons to rehearse on patient-specific models before the actual surgery. The challenge is automating the conversion of medical images to printable models while preserving anatomical accuracy.