Clinical AI-OCT Decision Support

• Clinical AI-OCT Decision Support is an AI-driven framework that automates the analysis of OCT/OCTA images to assist in clinical diagnosis and procedural planning.
• Deep learning models such as U-Net, VGG classifiers, and lightweight CNNs extract structural and vascular features, achieving high sensitivity and specificity in disease detection.
• These systems integrate with clinical workflows via on-device, cloud, and PACS solutions, offering explainable outputs with heatmaps and quantitative biomarkers for enhanced clinician trust.

Clinical AI-OCT Decision Support systems employ advanced machine learning architectures to automate and optimize the interpretation of optical coherence tomography (OCT) and OCT angiography (OCTA) images for medical decision support. These systems synthesize quantitative and qualitative imaging biomarkers, surpassing traditional human analysis in sensitivity and specificity for disease detection, vascular mapping, and procedural planning, and are increasingly integrated into diverse clinical workflows across ophthalmology, cardiology, oncology, and systemic disease management.

1. Neural Network Architectures and Input-Output Mappings

The foundation of Clinical AI-OCT Decision Support consists of deep convolutional neural networks tailored to the OCT/OCTA data domains:

• U-Net–type Fully Convolutional Autoencoders: Models such as that described by Lee et al. deploy nine encoder-decoder blocks with skip connections (copy+concatenate) for direct mapping from structural OCT B-scans ($x \in [0,1]^{384 \times 128}$) to inferred vasculature maps ($\hat{y} \in \mathbb{R}^{384 \times 128}$). Training minimizes $\mathcal{L}(\theta) = \text{MSE}(f_\theta(x), y)$ using Adam optimizer, yielding parameter counts on the order of 7.85 million and inference latencies of 10–20 ms per scan (Lee et al., 2018).
• Deep Attentive Networks: Architectures with stacked Dense Residual Inception and partial attention (≈250 convolutional layers, ≈50M parameters) provide rich feature aggregation across retinal layers, supporting multi-class diagnosis and confidence-calibrated disease prevalence outputs. End-to-end transfer learning cascades and advanced augmentation (resize-crop, photometric jitter, cutout masking) increase generalizability (Haloi, 2018).
• Efficient Lightweight CNNs: LightOCT employs only two convolutional layers and a fully connected layer, optimized for small data regimes or real-time, CPU-only inference; batch processing achieves ~6 ms/image. Transfer learning workflows enable rapid adaptation to new datasets (Butola et al., 2018).
• Transfer-Learned VGG-Classifiers: For both macular OCT (Lee et al., 2016) and OCTA (Le et al., 2019), VGG16 or its variants serve as robust backbones for disease classification, supporting three-way (Normal/NoDR/NPDR) and binary anomaly detection.
• Retrieval-Augmented Large Models (RAG-LLM): CA-GPT (DeepSeek-R1, 14B parameters) orchestrates modular reasoning via retrieval of >100,000 curated PCI cases and clinical guidelines, embedding small-model analytic outputs (lumen segmentation, plaque scoring) within the RAG-driven recommendation pipeline for structured, traceable procedural decision support (Fang et al., 11 Dec 2025).

2. Data Sources, Preprocessing, and Augmentation

Clinical AI-OCT frameworks are trained on extensive, multimodal datasets, with careful curation and preprocessing to optimize performance and transferability:

• Dataset Composition: Training sets encompass hundreds of thousands of B-scans and volumes, sourced from unique patients and covering normal anatomy as well as diabetic retinopathy, AMD, vein occlusion, and other pathologies (Lee et al., 2018, Bhatia et al., 2019). OCTA datasets (e.g., OCTA500) augment the structural data with high-resolution vascular maps for supervised or transfer learning (Thrasher et al., 21 Jul 2024).
• Preprocessing Protocols: Rigid registration (OCTA to OCT), segmentation of target slabs (superficial or deep), intensity normalization, and cropping preserve native resolution and facilitate alignment between modalities (Lee et al., 2018). Quality assessment modules automatically flag ungradable scans, and intensity/contrast normalization mitigate vendor-to-vendor variability (Bhatia et al., 2019).
• Data Augmentation Strategies: Random rotations, flips, elastic transformations, cutout masking, and photometric jitter (hue/contrast/saturation shifts) are applied on-the-fly to increase model robustness against artifacts and domain shifts. AutoAugment and AugMix are employed for synthetic diversity in class-imbalanced settings (Thrasher et al., 21 Jul 2024).

3. Quantitative Performance Metrics and Validation Studies

AI-OCT decision support systems achieve and report high accuracy, sensitivity, specificity, and agreement indices versus expert clinicians and gold-standard modalities.

Application	AUROC	Sensitivity (%)	Specificity (%)	Data Source
Retinal Flow Mapping	0.99*	95.2	96.7	U-Net vs. OCTA ground truth (Lee et al., 2018)
AMD Classification	0.9383	85.41	93.82	VGG-based classifier (Lee et al., 2016)
General Anomaly (Pegasus-OCT)	≥0.98	94–100	92–98	Multi-center OCT (Bhatia et al., 2019)
SRF/ME Detection (Fluid Intelligence)	N/R	82.5–97	52–100	Mobile/Cloud AI (Odaibo et al., 2019)
PCI Planning (CA-GPT)	N/R	90.3*	N/A	RAG-LLM, expert agreement (Fang et al., 11 Dec 2025)

*AUROC for en-face vessel order detection, CA-GPT stent diameter agreement versus expert standard.

• AI-OCT U-Net models outperformed human clinicians in vessel detection across sensitivity, specificity, PPV, and NPV (P < 10⁻⁵) (Lee et al., 2018). Agreement indices for procedural planning in cardiovascular applications exceeded junior operator and general-purpose LLMs for key metrics (90.3% stent diameter, 80.6% stent length within ±0.5/5 mm, P < 0.05–0.001) (Fang et al., 11 Dec 2025).
• For classification tasks (AMD, DME, drusen/CNV), deep classifiers routinely attain AUROCs ≥0.93 and accuracies >85%, validated across patient-level, volume-level, and B-scan-level splits (Lee et al., 2016, Bhatia et al., 2019, Haloi, 2018). Mobile cloud-based fluid detection yielded sensitivity of 89.3% and specificity of 81.23% over five centers (Odaibo et al., 2019).
• Active learning strategies on OCTA improve macro-F1 by up to 49% over class-weighting, undersampling, or oversampling, with Ratio/Entropy sampling yielding F1 = 0.712–0.732 (Thrasher et al., 21 Jul 2024).

4. Integration into Clinical Workflows

Practical deployment of AI-OCT decision support leverages device-side, server-side, and cloud architectures:

• On-Device/Edge Inference: Models such as U-Net and LightOCT (≤10 ms per scan) run in real time on embedded GPU (NVIDIA Jetson, RTX 2070) or even CPU-only workstations (Lee et al., 2018, Butola et al., 2018).
• Cloud and Mobile Platforms: Fluid Intelligence enables cloud-based processing with front-end capture via smartphone, transmitting cropped B-Scan images to HIPAA-compliant servers and integrating results into EMR systems via HL7/FHIR (Odaibo et al., 2019).
• PACS/Viewer Integration: Standalone apps (Windows/Mac), DICOM listeners, and plugin modules interface directly with commercial OCT platforms, providing automated flagging, per-scan probability bars, heatmaps for lesion localization, and structured diagnostic reports (DICOM SR, HL7-FHIR) (Bhatia et al., 2019, Thrasher et al., 21 Jul 2024).
• PCI Decision Support: CA-GPT modular reasoning integrates seamlessly into OCT consoles (Vivolight P80), supporting automated, guideline-traceable recommendations on device sizing, deployment strategies, and post-procedural assessment (Fang et al., 11 Dec 2025).

5. Interpretability, Explainability, and Clinical Utility

Decision support output includes several interpretability features and direct clinical impact:

• Heatmaps and Attention Visualizations: Grad-CAM overlays, occlusion-based heatmaps, and partial-attention feature maps highlight influential regions and multi-scale contributions, increasing clinician trust and enabling explainable audits (Haloi, 2018, Bhatia et al., 2019).
• Quantitative Biomarkers: AI-inferred flow maps enable automated ischemia zone flagging, vessel density estimation, and volumetric tracking for progression modeling (Lee et al., 2018, Thrasher et al., 21 Jul 2024).
• Risk Stratification and Robustness Assessment: Geometric deep learning (DGCNN) and autoencoder-based models infer biomechanical robustness of the optic nerve head for glaucoma risk without mechanical testing, identifying the scleral canal and lamina cribrosa insertion as critical features (AUC = 0.76 ± 0.08) (Braeu et al., 2022).
• Procedural Planning Enhancement: CA-GPT delivers guideline-anchored device recommendations, expediting intra-procedural decision-making and standardizing operator interpretation (Fang et al., 11 Dec 2025).

6. Limitations, Challenges, and Future Directions

Several constraints remain for widespread AI-OCT clinical deployment:

• Vendor and Protocol Generalizability: Most models are trained on single-vendor datasets; prospective multi-center studies and cross-device/federated learning are required for domain-adapted performance (Lee et al., 2018, Lee et al., 2016, Bhatia et al., 2019).
• Regulatory and Clinical Validation: FDA 510(k), CE-marking, and real-world effectiveness trials are necessary for regulatory clearance and to quantify outcome impacts (Bhatia et al., 2019, Odaibo et al., 2019).
• Image Quality Control: Poor scan quality, motion artifacts, and segmentation errors remain failure points; automated QC submodules and robust augmentation mitigate some risks (Bhatia et al., 2019, Odaibo et al., 2019).
• Integration Barriers: Achieving seamless EHR linkage, DICOM compliance, and minimal alert fatigue in clinical settings is ongoing (Khan et al., 27 May 2025).
• Model Expansion and Explainability: Integrating interpretability modules (saliency, feature importance), multi-label classification for expanded pathologies (ERM, macular hole), and incorporating longitudinal data and multimodal biomarkers for personalized risk assessments are key future directions (Haloi, 2018, Bhatia et al., 2019, Khan et al., 27 May 2025).

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Clinical AI-OCT Decision Support systems represent a maturing domain where deep learning architectures—especially U-Net style autoencoders, VGG/ResNet/Transformer derivatives, and RAG-enhanced LLMs—deliver expert-level or super-expert performance for retinal and intravascular imaging analysis, decision guidance, and procedural planning. Rigorous data handling, explainable interfaces, and robust workflow integration position these modules as indispensable adjuncts for next-generation precision diagnostics and therapy optimization in imaging-rich specialties (Lee et al., 2018, Fang et al., 11 Dec 2025, Bhatia et al., 2019, Thrasher et al., 21 Jul 2024).