AI and Rare Diseases ── 2026-11-01
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（1） · Rare blood disorders（0） · Immune and connective tissue（0） · Heart and lung（1） · 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
- Abstract
OBJECTIVE: Testicular volume serves as a crucial indicator for evaluating therapeutic outcomes in patients with congenital hypogonadotropic hypogonadism (CHH), which is a rare disease. We aim to develop a multimodal machine learning model that integrates clinical data, testicular ultrasonography, and pituitary magnetic resonance imaging (MRI) radiomic features to predict whether testicular volume exceeds 4 mL following treatment in CHH patients. METHODS: This study retrospectively included 66 male patients with CHH from 2015 to 2024, with a follow-up period of 12 to 36 months. All patients received gonadotropin or GnRH pulse pump therapy. The patients' visit information was systematically collected, and the peak testicular volume during follow-up was used as the outcome. Multiple machine learning prediction models were constructed using clinical information, initial-visit testicular ultrasound images, and pituitary MRI images, and were evaluated using ten-fold cross-validation. Model performance was compared using the Delong test. Model interpretability was analyzed using SHapley Additive exPlanations (SHAP). RESULTS: A total of 28 patients achieved a testicular volume greater than 4 mL after treatment, indicating favorable reproductive outcomes. The multimodal model that integrated all information demonstrated the highest predictive performance, with an area under the receiver operating characteristic curve (AUC) of 0.932 (95% CI: 0.891-0.997), outperforming the clinical model (AUC = 0.863, 95% CI: 0.747-1.000). The best AUCs for models based solely on ultrasound and MRI radiomic features were 0.798 (95% CI: 0.697-1.000) and 0.680 (95% CI: 0.526-0.868), respectively. The combined imaging model (ultrasound + MRI) yielded an AUC of 0.759 (95% CI: 0.510-0.969). Among the top 10 SHAP-ranked features in the multimodal model, two originated from ultrasound, one from MRI, and seven from clinical variables. CONCLUSION: The multimodal model, which integrated clinical factors with radiomic features from both ultrasound and magnetic resonance imaging, demonstrated favorable performance in predicting reproductive outcomes in patients with CHH after treatment.
Journal IF-equivalent: 3.8 (OpenAlex 2-year mean citedness, value as of 2026-10-31, retrieved 2026-11-01; not the official Clarivate IF)Reference: Li X, Ruan C, Wu Z, Wang S, Liang T, Pan D, et al. A multimodal machine learning model for predicting reproductive outcomes in the rare congenital hypogonadotropic hypogonadism after treatment. BMC Med Imaging. 2026 Aug 4;26(1):564. doi:10.1186/s12880-026-02634-3. PMID: 42963540.Checked: Abstract only
See all 1 Genetic syndromes papers →
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
Cystic Fibrosis (CF) and Primary Ciliary Dyskinesia (PCD) are rare genetic disorders primarily affecting the respiratory system that can lead to bronchiectasis. They share similar symptoms including recurrent lung infections, cough and difficulties in clearing mucus. Thoracic chest computed tomography (CT) imaging is widely used to assess structural lung disease in both CF and PCD. In an attempt to unify interpretation strategies of structural lung disease, semi-quantitative and quantitative scoring tools were developed for diagnosis, monitoring progression, and evaluating treatment responses. However, the manual scoring procedure is time consuming, tedious and subjective. Its accuracy depends on the expertise and experience of the radiologist performing the analysis. Advances in artificial intelligence (AI) have led to the introduction of several automated CT scoring techniques in the scientific literature to alleviate these challenges. We synthesized findings from 50 peer-reviewed articles to assess trends in automated CT scoring techniques for assessing structural lung changes in patients with CF and PCD, identify the existing challenge to develop clinical grade systems, and propose future research directions to advance the existing research towards mature technology. The results reveal that the attention given to this topic is relatively low both from the algorithmic development perspective and in terms of data availability. Among the included studies, only two focused on PCD. Furthermore, no regulatory-approved solutions were reported so far. This manuscript summarizes the existing progress, the trends compared to relevant fields, the research gaps and the future research avenues. It provides valuable insights for researchers and clinicians on the pathway to developing a fully automated and clinical-grade scoring system for structural lung disease in CF and PCD evaluation. Clinical trial number: Not applicable.
Journal IF-equivalent: 2.9 (OpenAlex 2-year mean citedness, value as of 2026-10-31, retrieved 2026-11-01; not the official Clarivate IF)Reference: Fante KA, Anagnostopoulou P, Yiallouros P, Robinson P, Vavourakis V. Automated methods for assessing structural lung disease in patients with cystic fibrosis and primary ciliary dyskinesia: a scoping review. BMC Pulm Med. 2026 Oct 30 [Epub ahead of print]. doi:10.1186/s12890-026-04857-0.Checked: Abstract only
See all 1 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.