AI for Surgery: Intraoperative Intelligence

Module: Healthcare AI | Difficulty: Advanced

Surgical Phase Transition

Instrument Detection IoU

Surgical AI Applications

import torch
import torch.nn as nn

class SurgicalPhaseRecognizer(nn.Module):
    def __init__(self, num_phases=7, input_dim=2048):
        super().__init__()
        self.temporal = nn.Sequential(
            nn.Conv1d(input_dim, 256, kernel_size=5, padding=2),
            nn.ReLU(),
            nn.Conv1d(256, 128, kernel_size=3, padding=1),
            nn.ReLU())
        self.lstm = nn.LSTM(128, 64, batch_first=True, bidirectional=True)
        self.phase_head = nn.Linear(128, num_phases)

    def forward(self, features):
        x = self.temporal(features)
        x = x.permute(0, 2, 1)
        lstm_out, _ = self.lstm(x)
        phases = self.phase_head(lstm_out)
        return phases

class InstrumentDetector(nn.Module):
    def __init__(self, num_instruments=10):
        super().__init__()
        self.backbone = models.resnet34(pretrained=True)
        num_features = self.backbone.fc.in_features
        self.backbone.fc = nn.Identity()
        self.bbox_head = nn.Linear(num_features, num_instruments * 4)
        self.class_head = nn.Linear(num_features, num_instruments)

    def forward(self, x):
        features = self.backbone(x)
        bboxes = self.bbox_head(features).reshape(-1, 10, 4)
        classes = torch.sigmoid(self.class_head(features))
        return bboxes, classes

phase_model = SurgicalPhaseRecognizer(num_phases=7)
features = torch.randn(1, 2048, 100)
phases = phase_model(features)
print(f'Phase predictions: {phases.shape}')

instrument_model = InstrumentDetector(num_instruments=10)
x = torch.randn(1, 3, 224, 224)
bboxes, classes = instrument_model(x)
print(f'Bboxes: {bboxes.shape}, Classes: {classes.shape}')

Research Insight: Surgical phase recognition enables real-time workflow analysis and can predict surgical steps before they occur. The most challenging aspect is handling transitions between phases, which are often ambiguous and can vary significantly between surgeons. Temporal models that capture long-range dependencies achieve the best performance for phase recognition.