AI and Rare Diseases ── 2026-10-15
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（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
- Abstract
Accurate and timely detection of sickle red blood cells has a significant impact on the patients' treatment. Developing a computer vision system that is both lightweight and resource-efficient is important for enabling real-time edge-based applications in resource-constrained healthcare environments. This study proposes a hybrid sickle cell detection framework that uses Bi-lightweight Knowledge Distillation (KD)-guided CNNs, combining two lightweight student models from different teacher networks with Machine Learning (ML) classifiers to identify sickle cells with accuracy and efficiency. Using depthwise separable convolution layers, lightweight models are created that drastically lower model complexity. These lightweight models are improved by extracting knowledge from top-performing teacher networks, such as MobileNetV2 and DenseNet121. Using this Knowledge Distillation learning technique, lightweight models can enhance their representational capabilities without adding to their computational complexity. To improve decision-making performance, the Bi-distilled student models are combined with conventional machine learning KNN classifiers to create a hybrid ensemble that combines the advantages of ML-based classification with dual KD-guided CNN-based feature extraction. The Sickle Cell Disease Dataset is used to train and validate the proposed system. The learned student models are then converted into TensorFlow Lite (TFLite) format using Float32 quantization to further reduce computational complexity. In combination with dual KD-guided CNN models and a KNN classifier, the proposed hybrid model achieves a high classification accuracy of 95.45% and an F1-score of 95.02% while being small at only 3.96 MB. Deploying and evaluating the framework on a resource-constrained Raspberry Pi 4 Model B demonstrate its potential for real-time, edge-based, and point-of-care applications.
Journal IF-equivalent: 5.0 (OpenAlex 2-year mean citedness, value as of 2026-10-13, retrieved 2026-10-15; not the official Clarivate IF)Reference: Reddy MMK, Lathvik CK, R AA, K K. Hybrid multi-model framework with knowledge distillation for precise and efficient sickle cell detection. Front Digit Health. 2026 Sep 29;8:1938284. doi:10.3389/fdgth.2026.1938284.Checked: Abstract only
See all 1 Rare blood disorders papers →
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
INTRODUCTION: Idiopathic pulmonary fibrosis (IPF) has an unpredictable course and poor outcome, and lung function-based risk stratification remains incomplete. Whether absolute and lung volume-normalized deep learning-derived CT pulmonary vascular measurements show distinct associations with disease severity and mortality in patients with IPF remains unclear. METHODS: In this dual-center retrospective study, 118 adults with newly diagnosed IPF, including 57 from the Italian cohort and 61 from the French cohort, underwent baseline chest CT analyzed with a deep learning-based quantitative pipeline combining lung parenchyma (whole lung volume, WLV; fibrosis extent) and pulmonary vascular metrics (total blood volume, TBV; and derived BV 5 and BV >10 indices representing vessels with cross-sectional areas of 10 mm²). Twenty subjects without lung disease served as a comparison group. Associations with fibrosis were assessed by Pearson correlation, mortality by multivariable Cox hazards models, and longitudinal lung function by linear mixed-effects models. Additional models examined the prognostic associations of vascular metrics after accounting for WLV. RESULTS: Compared with controls, IPF patients had lower WLV and higher absolute TBV. Relative TBV (normalized to WLV) correlated strongly with fibrosis, while absolute TBV showed weaker associations. Relative vascular metrics inversely related to baseline lung function, whereas absolute TBV showed no significant association. Baseline TBV, BV 5 , and BV >10 did not predict forced vital capacity (FVC) or diffusing capacity of the lung for CO trajectories, but higher WLV predicted better FVC over time. Over a median 7.8-year follow-up, 72 deaths occurred. In multivariable models, higher absolute TBV, lower-lobe BV, BV 5 , BV >10 , and WLV, but not normalized TBV, were associated with reduced mortality after adjustment for established clinical variables. These findings suggest that preservation of lung volume and pulmonary vascular volume may be associated with improved survival. However, the associations of absolute vascular metrics with mortality were substantially attenuated after additional adjustment for WLV. CONCLUSION: Normalized and absolute pulmonary vascular metrics capture different aspects of IPF, with normalized measures associated with fibrosis burden and functional impairment, while absolute vascular volumes were associated with mortality, although this association was substantially attenuated after accounting for WLV. Absolute vascular volume may therefore partly reflect preserved lung volume rather than an independent prognostic effect. Prospective validation is warranted.
Journal IF-equivalent: 2.6 (OpenAlex 2-year mean citedness, value as of 2026-10-13, retrieved 2026-10-15; not the official Clarivate IF)Reference: Rea G, Sanduzzi Zamparelli S, Mazzocca A, Lieto R, Bruzzese D, Crestani B, et al. Absolute and lung volume-normalized deep learning-derived pulmonary vascular measures in idiopathic pulmonary fibrosis: distinct clinical associations and the role of lung volume in prognosis. Front Med. 2026 Sep 29;13:1833470. doi:10.3389/fmed.2026.1833470.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.