AI and Rare Diseases ── 2026-10-28
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（1） · Inborn errors of metabolism（0） · Genetic syndromes（0） · Rare blood disorders（1） · 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
- Abstract
Background/Objective : Systemic lupus erythematosus (SLE) is a complex autoimmune disease with clinical and serological heterogeneity. Current laboratory assays show variable performance for disease detection and risk stratification, resulting in diagnostic delays exceeding six years. There is an unmet need for improved diagnostic and monitoring tools. This study examines the clinical application of Fourier-transform infrared (FTIR) spectroscopy as a rapid, label-free technique for classifying SLE and other connective tissue diseases (CTDs). Methods : Serum samples from patients with SLE, Sjögren's syndrome (SS), undifferentiated CTD (UCTD), and healthy controls (HCs) were analysed by FTIR spectroscopy followed by genetic algorithm-linear discriminant analysis (GA-LDA) modelling at two time points: baseline and six-month follow-up. Results : FTIR spectroscopy reliably differentiated CTDs from HCs with accuracies of 94.2% (inclusive of all time points), 99.3% at baseline, and 97.5% at follow-up. SLE patients were successfully separated from disease controls, achieving classification accuracies of 100% at baseline and 97.4% at follow-up. Subgroup analysis also demonstrated good classification accuracies for SLE (81.7%), SS (82.9%), and UCTD (86.1%), with discriminatory spectral features aligning with disease-related biochemical assignments. Notably, wavenumbers associated with proteins, carbohydrates, lipids, and DNA showed increased absorbance intensities in CTD patients, potentially reflecting immune dysregulation and metabolic changes. Conclusions : FTIR spectroscopy can reliably distinguish patients with SLE from both other CTDs and healthy individuals and may offer greater diagnostic utility than conventional serological testing. Identification of longitudinal variations in wavenumber biomarkers further supports the clinical potential of this technique and suggests possible future applications in therapeutic monitoring and risk stratification within rheumatological autoimmune disorders.
Journal IF-equivalent: 5.4 (OpenAlex 2-year mean citedness, value as of 2026-10-26, retrieved 2026-10-28; not the official Clarivate IF)Reference: Callery EL, Dyball S, Morais CLM, Taylor JV, Madenidou AV, Ur Rehman I, et al. Towards a Novel Diagnostic Tool for Autoimmune Rheumatic Diseases: Classification of SLE Using FTIR Spectroscopy and Machine Learning. Biomedicines. 2026 Sep 28;14(10):2191. doi:10.3390/biomedicines14102191.Checked: Abstract only
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
Routine complete blood count (CBC) measurements are widely available and may support the initial assessment of thalassemia-related hematological conditions. However, overlapping hematological profiles and class imbalance can complicate differentiation among groups. This study evaluated seven supervised machine learning classifiers for multiclass classification of normal hematological profiles, suspected α-thalassemia profiles, Hb E carrier-related profiles, and other thalassemia-related/hemoglobinopathic profiles using a publicly available CBC dataset. An unresampled baseline was compared with four synthetic oversampling strategies: SMOTE, Borderline-SMOTE, SVM-SMOTE, and KMeans-SMOTE. Model development and hyperparameter optimization were performed within five-fold stratified cross-validation, with model selection based on cross-validated Matthews correlation coefficient (MCC). The untouched internal hold-out test set was reserved for final evaluation. CatBoost without resampling yielded the highest cross-validated MCC (0.6378). On the internal hold-out test set, it achieved an accuracy of 0.7950, an MCC of 0.6537, Cohen's kappa of 0.6499, and micro- and macro-average ROC-AUC values of 0.9554 and 0.9227, respectively. However, class-specific sensitivity was heterogeneous, ranging from 0.4028 for suspected α-thalassemia to 0.9767 for the normal hematological profile. Synthetic oversampling did not consistently increase overall CV MCC, although sensitivity improved for some minority classes. These findings characterize the performance and limitations of CBC-based multiclass classification and indicate the need for further external and clinically oriented validation before screening or referral applications can be considered.
Journal IF-equivalent: 5.4 (OpenAlex 2-year mean citedness, value as of 2026-10-15, retrieved 2026-10-16; not the official Clarivate IF)Reference: Basancelebi M, Yapici IS, Uzun Arslan R. Machine Learning-Based Multiclass Classification of Hb E Carrier Status and Thalassemia-Related Hematological Profiles Using Routine Complete Blood Count Parameters. Bioengineering. 2026 Sep 24;13(10):1118. doi:10.3390/bioengineering13101118.Checked: Abstract only
See all 1 Rare blood disorders papers →
Autoinflammatory, immunodeficiency and rare connective-tissue diseases: treatment, trials and AI
- Abstract
BACKGROUND: The Human Phenotype Ontology (HPO) terms for systemic autoinflammatory diseases (SAIDs) were revised in 2022, but their diagnostic accuracy has not yet been validated in real-life cohorts. OBJECTIVES: To evaluate the diagnostic accuracy of HPO terms in a real-life cohort of patients enrolled in a large international registry for SAIDs through a machine learning (ML) approach. METHODS: Clinical and genetic data were extracted from the Eurofever Registry (EF) for 2,576 patients. Due to limited sample sizes for rarer conditions, analyses focused on FMF, MKD, TRAPS, CAPS and PFAPA. The 223 clinical EF variables were codified into HPO terms, including terms with missing HPO correspondence. Supervised ML models (Elastic Net, k-Nearest Neighbors, Random Forest, and XGBoost) were trained to classify the five diseases using both multiclass and one-vs-rest (OVR) strategies. RESULTS: A full HPO correspondence was found for 185 EF terms. Clinically relevant variables frequently missing from HPO, including "ancestry" and "fever length", were incorporated into the final models. On the held-out test set, the XGBoost OVR framework achieved the following recall values: 87.2% for FMF, 90.4% for PFAPA, 81.1% for CAPS, 55.7% for TRAPS, and 55.6% for MKD. Feature importance analyses highlighted "fever length" as key discriminative variable. Error analysis revealed an enrichment of patients carrying the R92Q TNFRSF1A variant among false-negative predictions for TRAPS. Unsupervised analysis showed no clear separation between FMF patients carrying one versus two pathogenic MEFV mutations. CONCLUSIONS: HPO-based phenotypic modeling supports accurate classification of the 5 most frequent recurrent fevers included in this study. The HPO database should be enriched by including the missing terms, especially "fever length", "ancestry", and the symptoms frequencies according to Eurofever. An ontology-driven diagnostic tool applicable to both clinical research and computational settings for recurrent fevers is now available.
Journal IF-equivalent: 5.6 (OpenAlex 2-year mean citedness, value as of 2026-10-26, retrieved 2026-10-28; not the official Clarivate IF)Reference: Matucci-Cerinic C, Cavalca G, Vergani M, Caorsi R, van Gjin M, Boursier G, et al. Machine learning–based validation of Human Phenotype Ontology (HPO) terms for systemic autoinflammatory diseases using the Eurofever Registry: the ODINO project. Front Immunol. 2026 Oct 12;17:1936099. doi:10.3389/fimmu.2026.1936099.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.