AI and Rare Diseases ── 2026-10-07
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（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
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
Hypertrophic Cardiomyopathy (HCM) and Cardiac Amyloidosis (CA) are two cardiac conditions that can advance to heart failure if left untreated, which present considerable diagnostic challenges due to their overlapping echocardiographic appearance. To address these challenges, this study develops a multi-view deep learning framework that classifies 2D echocardiographic data into five clinically relevant views after image pre-processing: apical 4-chamber, parasternal long axis of left ventricle, parasternal short axis at levels of the mitral valve, papillary muscle, and apex. The framework independently extracts distinctive features from each view, which are then fused for accurate disease classification. The cohort for this study included 212 patients with HCM, 119 with CA, and 200 control subjects with normal cardiac function, enrolled from 2018 to 2022. Utilizing fivefold cross-validation, the model demonstrated precision of 0.83, sensitivity of 0.81, specificity of 0.89, and a micro-F1 score of 0.82. These results affirm the effectiveness of the framework as a reliable diagnostic tool for differentiating between HCM and CA Using a clinical dataset.
Journal IF-equivalent: 5.2 (OpenAlex 2-year mean citedness, value as of 2026-10-06, retrieved 2026-10-07; not the official Clarivate IF)Reference: Li X, Peng B, Li X, Wang Z, Deng H, Luo X, et al. A deep learning-driven pipeline for differentiating hypertrophic cardiomyopathy from cardiac amyloidosis using 2D multi-view echocardiography. Sci Rep. 2026 Oct 5 [Epub ahead of print]. doi:10.1038/s41598-025-15443-5.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.