AI for Ophthalmology: Retinal Image Analysis

Module: Healthcare AI | Difficulty: Advanced

Diabetic Retinopathy Grades

Retinal Vessel Width (CRAE)

Ophthalmology AI Performance

import torch
import torch.nn as nn
import torchvision.models as models

class RetinalClassifier(nn.Module):
    def __init__(self, num_classes=5, pretrained=True):
        super().__init__()
        backbone = models.efficientnet_b4(pretrained=pretrained)
        self.features = backbone.features
        num_features = backbone.classifier[1].in_features
        self.attention_pool = nn.Sequential(
            nn.Linear(num_features, 128), nn.Tanh(), nn.Linear(128, 1))
        self.classifier = nn.Sequential(
            nn.Dropout(0.5), nn.Linear(num_features, 256),
            nn.ReLU(), nn.Linear(256, num_classes))

    def forward(self, x):
        features = self.features(x)
        features = nn.functional.adaptive_avg_pool2d(features, (1, 1))
        features = features.flatten(1)
        attn = torch.softmax(self.attention_pool(features), dim=0)
        features = attn * features
        return self.classifier(features)

class ImageQualityAssessor(nn.Module):
    def __init__(self):
        super().__init__()
        self.backbone = models.resnet18(pretrained=True)
        self.quality_head = nn.Linear(512, 3)

    def forward(self, x):
        features = self.backbone(x)
        quality_scores = torch.sigmoid(self.quality_head(features))
        return quality_scores

model = RetinalClassifier(num_classes=5)
x = torch.randn(1, 3, 512, 512)
output = model(x)
print(f'DR grade distribution: {torch.softmax(output, dim=-1).detach().numpy()[0]}')

qa_model = ImageQualityAssessor()
quality = qa_model(x)
print(f'Quality scores: {quality.detach().numpy()[0]}')

Research Insight: AI-based diabetic retinopathy screening has achieved FDA approval and is being deployed in primary care settings. The key clinical challenge is that AI systems must achieve very high sensitivity (>95%) for referable DR to be safe as a screening tool. Calibration across different camera systems and populations remains an active area of research.