RetinaPack: Few-Shot Continual Retinal Pre-Screening with Offline Neuromorphic Inference

Retinal pre-screening in rural and resource-constrained settings requires portable, offline and privacy-preserving inference that can adapt to local acquisition shifts without large-scale retraining. A key difficulty is that current-stage labels can be obtained immediately, whereas prognostic outcomes mature months later, so local adaptation must improve staging without destabilizing longitudinal risk estimates. We introduce RetinaPack, a participant-disjoint longitudinal benchmark of 13,040 participants, and RetinaLPA-2S, a few-shot continual adaptation method for four-stage age-related macular degeneration (AMD) pre-screening. RetinaPack combines adjudicated change and stable pairs with crossed camera/operator repeats to separate disease-related variation from acquisition nuisance and define a pathology-referenced protected subspace. RetinaLPA-2S updates only its orthogonal complement through a 4,096-parameter two-sided low-rank adapter, while a frozen competing-risk branch preserves fixed-input prognosis during stage-only adaptation. A quantized encoder runs on the Akida AKD1000 and signed adaptation remains on the local host, enabling network-independent inference, local updating and feature replay without retinal-image storage. With 10 labels per stage, RetinaLPA-2S reaches 82.20 domain-balanced candidate F1 with 1.07-point forgetting, 1.68 points above BiLoRA-style FA. On 920 staging queries, it achieves 83.78 macro-F1 and 95.33% referral sensitivity. The complete image-to-report path requires 2.150 J, 650.4 ms P95 latency and 224 MiB peak memory, reducing processing energy by 34.35% relative to CPU INT8.