AI and Rare Diseases ── 2026-11-04
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（1） · Kidney, liver and gut（1） · 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
Objective: To develop, internally validate and compare machine learning models for classifying kidney dysfunction in children aged ≤18 years with sickle cell disease (SCD), where kidney dysfunction may occur during childhood and remain clinically unrecognised.Design Retrospective study.Setting Paediatric Sickle Cell Clinic at Mulago National Referral Hospital and the Uganda Sickle Pan Africa Research Consortium registry.Participants A total of 893 children aged ≤18 years with confirmed SCD and complete renal function records were included.Main outcome measures Kidney dysfunction was defined as an estimated glomerular filtration rate (eGFR)<90 mL/min/1.73 m², while eGFR≥90 mL/min/1.73 m² was classified as normal kidney function. Random forest (RF), support vector machine (SVM) and extreme gradient boosting (XGBoost) models were developed using clinical and laboratory variables. Model performance was assessed using stratified fivefold cross-validation, with balanced accuracy, macro F1-score and area under the receiver operating characteristic curve (AUROC) as the main evaluation metrics.Results Among 893 participants, 488 (54.6%) had normal kidney function and 405 (45.4%) had kidney dysfunction. RF and XGBoost demonstrated comparable classification performance. RF achieved a balanced accuracy of 0.80 and AUROC of 0.86 (95% CI 0.84 to 0.89), while XGBoost achieved a balanced accuracy of 0.79 and AUROC of 0.87 (95% CI 0.85 to 0.89). SVM showed lower performance, with a balanced accuracy of 0.62 and AUROC of 0.69 (95% CI 0.66 to 0.72). SHAP (SHapley Additive exPlanations) analysis identified urine albumin and creatinine ratio as the strongest contributors to classification.Conclusion Machine learning models using routinely collected clinical and laboratory data showed potential for classifying kidney dysfunction in children with SCD.
Journal IF-equivalent: 0.0 (OpenAlex 2-year mean citedness, value as of 2026-11-03, retrieved 2026-11-04; not the official Clarivate IF)Reference: Esutu NE, Agaba I, Nakku-Joloba E, Kiguli S, Namazzi R, Mukose AD, et al. Development and internal validation of machine learning models for classification of kidney dysfunction in children aged ≤18 years with sickle cell disease at Mulago National Referral Hospital, Kampala, Uganda: a retrospective study. bmj connections digital health & ai. 2026 Nov 2;1(1):e000027. doi:10.1136/bmjcdh-2026-000027.Checked: Abstract only
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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
- Abstract
BACKGROUND: Artificial intelligence-enabled electrocardiography (AI-ECG) models detect hypertrophic cardiomyopathy (HCM) and cardiac amyloidosis in clinical populations; whether two independently developed models agree in athletes is unknown. METHODS: We applied two independently developed AI-ECG models to the screening electrocardiograms (ECGs) of 716 competitive athletes aged 40 years or younger, screened by ECG alone and presumed free of structural heart disease. The RST algorithms return per-condition probabilities dichotomized at published operating points; the AZ model, a four-class ECG-image classifier, assigns one class per ECG. The primary outcome was each model's positive-call rate (proportion of athletes flagged) for HCM and cardiac amyloidosis; agreement was described with Cohen kappa and positive agreement. Because the cohort was presumed disease-free, sensitivity was not assessed. RESULTS: Median age was 22.1 years; 86.0% were men. The RST algorithms flagged HCM in 13 athletes (1.8%; 95% CI, 1.0-3.1) and cardiac amyloidosis in 2 (0.3%; 0.0-1.0); the AZ model flagged HCM in 29 (4.1%; 2.7-5.8) and cardiac amyloidosis in 18 (2.5%; 1.5-3.9). Of 60 athletes flagged by either model, 2 were flagged by both (kappa 0.07 and - 0.01; positive agreement 0.10 and 0.00); continuous outputs were uncorrelated for HCM (Spearman rho 0.00). Of 59 flagged athletes with a stored cardiologist interpretation under the international criteria, 39 had a normal ECG. All 18 AZ amyloidosis calls occurred in men. CONCLUSIONS: The two models flagged different athletes at different rates. Without verified cases these rates describe referral burden, not accuracy; whether either detects disease requires validation against verified diagnoses.
Journal IF-equivalent: 2.5 (OpenAlex 2-year mean citedness, value as of 2026-11-03, retrieved 2026-11-04; not the official Clarivate IF)Reference: de Sousa Barzon Serra V, Zanco J, de Souza BL, Crystal OR, Nabi HA, Ibrahim R, et al. Positive-call rates and agreement of two artificial intelligence electrocardiogram models for hypertrophic cardiomyopathy and cardiac amyloidosis in competitive athletes. International Journal of Cardiology. 2026 Nov [Epub ahead of print]. doi:10.1016/j.ijcard.2026.135035.Checked: Abstract only
See all 1 Heart and lung papers →
Rare kidney, liver and gastrointestinal diseases: treatment, trials and AI
- Abstract
RATIONALE AND OBJECTIVES: To assess a noninvasive ultrasound habitat radiomics approach with automatic kidney segmentation for distinguishing pediatric Henoch-Schonlein purpura nephritis (HSPN) from immunoglobulin A nephropathy (IgAN). MATERIALS AND METHODS: This retrospective multicenter study included 592 biopsy-confirmed pediatric patients from three centers (HSPN, n = 338; IgAN, n = 254). The development cohort (n = 453) was split into training (n = 362) and internal validation (n = 91) cohorts, and an independent external validation cohort (n = 139) was used for final testing. Kidney regions of interest were automatically segmented using nnU-Net. K-means clustering defined three echogenic habitat subregions, and 408 radiomics features were extracted from the whole-kidney region and habitat subregions. Feature selection was performed in the training cohort, and three machine-learning classifiers were evaluated. The selected radiomics model was compared with clinical and combined models. RESULTS: nnU-Net achieved high kidney segmentation performance, with a mean Dice similarity coefficient of 0.957 and a mean intersection over union of 0.918 in the independent healthy-child test set. Fifteen radiomics features were retained, including six whole-kidney features and nine habitat-derived features. Logistic regression showed stable performance, with areas under the receiver operating characteristic curve (AUCs) of 0.806 (95% confidence interval [CI], 0.715-0.897) internally and 0.825 (95% CI, 0.758-0.892) externally. The combined model outperformed the clinical model in AUC in both cohorts (0.846 vs 0.717; 0.805 vs 0.733). CONCLUSION: This deep-learning-assisted ultrasound habitat radiomics approach enabled automatic kidney delineation and showed encouraging performance as a reproducible imaging adjunct for differentiating pediatric HSPN from IgAN.
Journal IF-equivalent: 3.4 (OpenAlex 2-year mean citedness, value as of 2026-11-03, retrieved 2026-11-04; not the official Clarivate IF)Reference: Gong S, Zhang W, Xu T, Zhang XF, Zhang MJ, Gao P, et al. Automatic Ultrasound Habitat Radiomics for Differentiating Henoch–Schonlein Purpura Nephritis from IgA Nephropathy in Children: A Multicenter Study. Acad Radiol. 2026 Nov [Epub ahead of print]. doi:10.1016/j.acra.2026.10.015. PMID: 42971817.Checked: Abstract only
See all 1 Kidney, liver and gut papers →
Rare skin and bone diseases: treatment, trials and AI
No new qualifying paper for this issue.