AI and Rare Diseases ── 2026-11-06
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（0） · Immune and connective tissue（0） · Heart and lung（2） · Kidney, liver and gut（0） · Skin and bone（0）
Rare-disease diagnosis and drug discovery platforms and AI
- Abstract
Objective: To assess the prognostic value of AI-derived cardiothoracic structures and coronary artery calcification volumetric measurements in pulmonary hypertension (PH) using non-gated, non-contrast CT. Materials and methods Patients with suspected PH who underwent non-gated, non-contrast chest CT and right heart catheterisation within one month were retrospectively identified from the Assessing the Spectrum of PH Identified at a Referral Centre (ASPIRE) registry. Two validated deep learning models were used to segment and measure the volumes of cardiothoracic structures and coronary artery calcifications. The prognostic value of AI-derived cardiothoracic structures and coronary artery calcification volumetric measurements was assessed using univariable and multivariable Cox regression adjusted for demographics and PH subgroups and REVEAL scores.
Results: Six hundred twenty patients were included (mean age 64 ± 13 years; 62% female; 388 patients died during follow-up), comprising 202 patients with pulmonary arterial hypertension, 84 with PH-LHD, 112 with PH associated with lung disease, 65 with chronic thromboembolic PH, 9 with unclear PH, and 148 patients without PH. Calcifications were present in the right coronary artery (RCA), mainstem and left anterior descending artery (LAD), and left circumflex artery (LCX) in 45%, 67%, and 35% of patients, respectively. In multivariable Cox regression analysis adjusted for age, sex, and PH subgroup, right atrial (RA) volume was independently associated with mortality (HR 1.024, 95% CI: 1.009–1.039, p = 0.001), as were coronary artery calcifications in the RCA (HR 1.378, 95% CI: 1.113–1.705, p = 0.003), mainstem and LAD (HR 1.868, 95% CI: 1.441–2.421, p < 0.001), and LCX (HR 1.342, 95% CI: 1.084–1.660, p = 0.007). After further adjustment for the REVEAL risk score, these associations remained significant (RA volume HR 1.026, RCA calcification HR 1.604, mainstem and LAD calcification HR 2.339, LCX calcification HR 1.576; all p < 0.001).
Conclusion: AI-derived RA volume and coronary artery calcifications are independently associated with increased mortality in PH patients. Key Points Question The prognostic significance of AI-derived cardiothoracic volumes and coronary artery calcification measured on routine non-gated, non-contrast chest CT in patients with PH remains uncertain.
Findings: AI-derived RA volume and coronary artery calcification volumes independently predicted mortality in PH patients, even after adjustment for demographics, PH subgroups, and REVEAL score. Relevance statement Automated CT-based AI measurements can provide additional prognostic biomarkers to improve risk stratification in PH beyond established clinical scores.
Journal IF-equivalent: 0.0 (OpenAlex 2-year mean citedness, value as of 2026-11-02, retrieved 2026-11-05; not the official Clarivate IF)Reference: Alnasser TN, Hokmabadi A, Sharkey M, Kiely DG, Alabed S, Swift AJ. AI-derived cardiothoracic structures and coronary artery calcification volumes on non-contrast CT as predictors of mortality in pulmonary hypertension. Eur Radiol Cardiovasc Thorac. 2026 Nov 4;1(1):14. doi:10.1007/s44449-026-00005-2.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
No new qualifying paper for this issue.
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
OBJECTIVES: To evaluate the prognostic value of changes in deep learning-based CT quantification following 1 year of pirfenidone treatment in patients with idiopathic pulmonary fibrosis (IPF). MATERIALS AND METHODS: This study included patients with IPF who received at least 1 year of pirfenidone between September 2009 and March 2022 at two hospitals. The extent of normal lung and fibrosis (sum of reticular opacity and honeycombing) was quantified on baseline and 1-year follow-up chest CT scans using a deep learning-based tool, and the changes were calculated. The primary outcome was a composite of all-cause mortality or lung transplantation; the secondary outcome was a ≥ 10% absolute decline in forced vital capacity (FVC) occurring after the 1-year follow-up. Associations between changes in CT quantification and these outcomes were assessed using Cox proportional hazards regression analyses. RESULTS: A total of 436 patients (341 men; mean age, 69.8 years) were included. Greater preservation of normal lung extent over 1 year was associated with a lower risk of the composite outcome (adjusted hazard ratio (HR) per 5% increase: 0.90; 95% confidence interval (CI): 0.85, 0.96; p < 0.001), whereas increases in fibrosis extent were associated with a higher risk (adjusted HR per 5% increase: 1.16, 95% CI: 1.06, 1.27, p = 0.002). Regarding the secondary outcome of ≥ 10% absolute FVC decline, only the change in fibrosis extent remained a significant predictor (adjusted HR per 5% increase: 1.26, 95% CI: 1.09, 1.45, p = 0.001). CONCLUSIONS: Changes in deep learning-based CT quantification results over 1 year of pirfenidone treatment demonstrated significant prognostic value in patients with IPF. KEY POINTS: Question Disease progression and mortality in patients with idiopathic pulmonary fibrosis (IPF) continue to occur unpredictably beyond 1 year of pirfenidone treatment.
Findings: Changes in deep learning-based quantification of normal lung and fibrosis extent after 1 year of pirfenidone treatment were significant prognostic predictors. Critical relevance statement Incorporating 1-year changes in deep learning-based CT quantification improves prognostication of mortality, lung transplantation, and lung function decline, supporting more individualized risk stratification in idiopathic pulmonary fibrosis.
Journal IF-equivalent: 6.5 (OpenAlex 2-year mean citedness, value as of 2026-11-03, retrieved 2026-11-05; not the official Clarivate IF)Reference: Lee JY, Chae KJ, Jang Mj, Chung JH, Goo JM, Choi SM, et al. Deep learning–based quantification of CT changes after 1 year of pirfenidone treatment as a prognostic marker in idiopathic pulmonary fibrosis. Insights Imaging. 2026 Nov 3;17(1):277. doi:10.1186/s13244-026-02447-6. PMID: 42975762.Checked: Abstract only - Abstract
BACKGROUND: Emerging evidence suggests that sphingolipid metabolism is involved in respiratory diseases, including idiopathic pulmonary fibrosis (IPF). This study aimed to evaluate the diagnostic and prognostic value of sphingolipid-related genes in IPF and to identify potential sphingolipid-associated biomarkers. METHODS: Sphingolipid-related genes were obtained from the GeneCards database. Non-negative matrix factorization (NMF) was performed to identify molecular clusters and differentially expressed genes (DEGs). High-dimensional weighted gene co-expression network analysis (hdWGCNA) was used to determine fibroblast-associated genes. The intersecting genes were used to construct diagnostic and prognostic models using multiple machine learning algorithms. Hub genes were identified from the overlap between the diagnostic and prognostic signatures. In silico gene knockout was conducted using scTenifoldKnk. Functional validation was performed using Cell Counting Kit-8 (CCK-8) assay, wound healing assay, quantitative real-time polymerase chain reaction (RT-qPCR), and western blotting (WB). RESULTS: Three molecular clusters with significant prognostic differences were identified. hdWGCNA revealed 60 key fibroblast-associated genes. Diagnostic and prognostic models constructed from these genes showed promising diagnostic and prognostic utility across independent cohorts, while the prognostic model exhibited some degree of cohort-dependent variability. CCDC80 was identified as a hub gene associated with sphingolipid-defined molecular heterogeneity and fibroblast-related transcriptional programs. scTenifoldKnk analysis indicated that simulated CCDC80 knockout affected fibrosis-related signaling pathways. In vitro experiments demonstrated that CCDC80 knockdown attenuated fibroblast proliferation, migration, and fibrotic activation. CONCLUSION: This study identified sphingolipid-related molecular signatures with potential diagnostic and prognostic value in IPF and highlighted CCDC80 as a candidate profibrotic regulator. Further validation in larger and clinically standardized cohorts is required before clinical translation. CLINICAL TRIAL NUMBER: Not applicable.
Journal IF-equivalent: 3.4 (OpenAlex 2-year mean citedness, value as of 2026-10-09, retrieved 2026-10-10; not the official Clarivate IF)Reference: Liao Y, Yang L, Liu X, Yang L. Integrative Multi-omics and machine learning analysis of sphingolipid-associated molecular stratification identifies diagnostic and prognostic signatures in idiopathic pulmonary fibrosis. Orphanet J Rare Dis. 2026 Aug 25;21(1):384. doi:10.1186/s13023-026-04563-4. PMID: 42977968.Checked: Abstract only
See all 2 Heart and lung papers →
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.