AI and Rare Diseases ── 2026-10-09
New papers on rare diseases (other than neurological and cancer) and AI, collected from journal feeds, PubMed (searched across all journals) and OpenAlex (a public index used instead of Google Scholar), judged by Jev (an AI that makes language judgements): original research, AI central, which of 8 disease groups, treatment or trials, and then ranked by importance up to a daily limit. Journals are not filtered by impact factor; each paper shows its journal's IF-equivalent (OpenAlex two-year mean citedness) and the date of that value. Each entry shows the full original abstract.
Read by disease: Platforms（0） · Inborn errors of metabolism（0） · Genetic syndromes（0） · Rare blood disorders（0） · Immune and connective tissue（1） · Heart and lung（0） · Kidney, liver and gut（0） · Skin and bone（0）
Rare-disease diagnosis and drug discovery platforms and AI
No new qualifying paper for this issue.
Inborn errors of metabolism: treatment, trials and AI
No new qualifying paper for this issue.
Genetic syndromes and paediatric rare diseases: treatment, trials and AI
No new qualifying paper for this issue.
Rare blood disorders: treatment, trials and AI
No new qualifying paper for this issue.
Autoinflammatory, immunodeficiency and rare connective-tissue diseases: treatment, trials and AI
- Abstract
Introduction: Cystoid macular edema (CME) is a vision-threatening complication of retinal vasculitis, a rare inflammatory disease for which large imaging datasets are difficult to obtain. Detection of CME on fluorescein angiography (FA) requires expert interpretation, motivating automated image-based approaches to support clinical decision-making. Deep learning methods, particularly convolutional neural networks (CNNs), have shown strong performance in medical image analysis but are constrained by limited labeled data in rare diseases. Transfer learning may mitigate this limitation by adapting pretrained models to small, task-specific datasets.
Methods: We analyzed 207 de-identified late-phase 55° FA images from 117 patients with retinal vasculitis. The primary target was concurrent patient-level CME status, established through specialist review of clinical records and optical coherence tomography obtained at the same clinical visit as FA. A patient was classified as CME-positive when CME was present in at least one eye. An ImageNet-pretrained ResNet-18 was evaluated using five-fold nested, patient-level cross-validation. Within each outer-development cohort, 144 candidate configurations were compared by four-fold inner cross-validation and selected according to mean inner-validation AUROC. Each selected configuration was evaluated only on its corresponding untouched outer-test fold.
Results: Across 117 pooled patient-level outer-test predictions, the model-selection pipeline achieved an AUROC of 0.708 (95% CI, 0.610–0.802), an AUPRC of 0.626 (95% CI, 0.493–0.755), and an accuracy of 0.726 (95% CI, 0.641–0.803). Sensitivity was 0.694 (95% CI, 0.558–0.822), specificity was 0.750 (95% CI, 0.643–0.853), and the F1 score was 0.680 (95% CI, 0.563–0.780). Exploratory Grad-CAM analysis visualized regions associated with model predictions. Discussion The nested model-selection pipeline showed moderate discrimination with a relatively balanced operating profile and slightly higher specificity than sensitivity. Clinically relevant false-positive and false-negative errors, together with the absence of external validation, preclude clinical deployment.
Journal IF-equivalent: 2.1 (OpenAlex 2-year mean citedness, value as of 2026-10-08, retrieved 2026-10-09; not the official Clarivate IF)Reference: Wang C, Chakrabarty K, Anesi SD, Chang PY. Classification of cystoid macular edema status from retinal vasculitis fluorescein angiography images using a convolutional neural network. Front Ophthalmol. 2026 Oct 7;6:1901237. doi:10.3389/fopht.2026.1901237.Checked: Abstract only
See all 1 Immune and connective tissue papers →
Rare cardiovascular and respiratory diseases: treatment, trials and AI
No new qualifying paper for this issue.
Rare kidney, liver and gastrointestinal diseases: treatment, trials and AI
No new qualifying paper for this issue.
Rare skin and bone diseases: treatment, trials and AI
No new qualifying paper for this issue.