AI and Rare Diseases ── 2026-10-19
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（1） · Immune and connective tissue（0） · 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
- Abstract
BACKGROUND AND AIMS: Thalassemia remains a global public health challenge, yet definitive diagnosis via genetic testing is often restricted by high costs and infrastructure requirements. We aimed to develop and externally evaluate an interpretable deep-learning framework using routine complete blood count (CBC) parameters for thalassemia screening. MATERIALS AND METHODS: Using 2317 CBC records from the internal derivation cohort, we implemented a two-stage analytical architecture. First, exploratory factor analysis (EFA) with Varimax rotation reduced nine continuous parameters to three factors, providing a lower-dimensional representation that attenuated multicollinearity among the erythrocyte indices. These latent factors, alongside gender, formed a 4-dimensional feature space that was processed through a deep neural network (DNN) optimized via a two-stage Bayesian-Grid search strategy and out-of-fold threshold selection. The locked model and operating threshold were evaluated without refitting or threshold reselection across four independent external centers (N = 1442). RESULTS: In the internal test set, the model achieved an AUROC of 0.9598 and a specificity of 0.9337. External AUROC ranged from 0.8771 to 0.9439 across the four centers. Sensitivity was 0.8387 overall but was lower for silent α-thalassemia carriers (0.5270, 39/74) than for non-silent α-thalassemia carriers (0.8692, 226/260). SHAP-based interpretability indicated that the model's learned associations are consistent with the microcytic, hypochromic phenotype of thalassemia, with reduced MCV and MCH alongside a raised red-cell count emerging as the dominant drivers of a positive prediction. Health economic simulations suggest this algorithmic triage could reduce genetic-testing referrals and associated costs by approximately 76% in a preliminary 10%-prevalence illustration, although a subset of milder carriers is missed at the chosen threshold and the estimate is not a formal economic evaluation. CONCLUSIONS: By integrating statistical feature engineering with deep learning, this framework provides a CBC-based pre-genetic-testing risk-stratification (triage) approach. The locked model and operating threshold were evaluated without refitting in four external centers, with between-center heterogeneity quantified, although absolute risk estimates require site-specific recalibration.
Journal IF-equivalent: 1.9 (OpenAlex 2-year mean citedness, value as of 2026-10-18, retrieved 2026-10-19; not the official Clarivate IF)Reference: Su X, Lin Y, Wang K, Lin Y, Wu L, Jiang X, et al. ThaCoder: an explainable deep learning framework for thalassemia screening using routine complete blood count: a multi-center validation study. Clinica Chimica Acta. 2026 Oct [Epub ahead of print]. doi:10.1016/j.cca.2026.122912.Checked: Abstract only
See all 1 Rare blood disorders papers →
Autoinflammatory, immunodeficiency and rare connective-tissue diseases: treatment, trials and AI
No new qualifying paper for this issue.
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.