AIMC Topic: Rare Diseases

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Feasibility study to improve deep learning in OCT diagnosis of rare retinal diseases with few-shot classification.

Medical & biological engineering & computing
Deep learning (DL) has been successfully applied to the diagnosis of ophthalmic diseases. However, rare diseases are commonly neglected due to insufficient data. Here, we demonstrate that few-shot learning (FSL) using a generative adversarial network...

Multi-task Learning via Adaptation to Similar Tasks for Mortality Prediction of Diverse Rare Diseases.

AMIA ... Annual Symposium proceedings. AMIA Symposium
The mortality prediction of diverse rare diseases using electronic health record (EHR) data is a crucial task for intelligent healthcare. However, data insufficiency and the clinical diversity of rare diseases make it hard for deep learning models to...

An integrative knowledge graph for rare diseases, derived from the Genetic and Rare Diseases Information Center (GARD).

Journal of biomedical semantics
BACKGROUND: The Genetic and Rare Diseases (GARD) Information Center was established by the National Institutes of Health (NIH) to provide freely accessible consumer health information on over 6500 genetic and rare diseases. As the cumulative scientif...

Quantitative retrospective natural history modeling for orphan drug development.

Journal of inherited metabolic disease
The natural history of most rare diseases is incompletely understood and usually relies on studies with low level of evidence. Consistent with the goals for future research of rare disease research set by the International Rare Diseases Research Cons...

Biomedical Holistic Ontology for People with Rare Diseases.

International journal of environmental research and public health
This research provides a biomedical ontology to adequately represent the information necessary to manage a person with a disease in the context of a specific patient. A bottom-up approach was used to build the ontology, best ontology practices descri...

The use of machine learning in rare diseases: a scoping review.

Orphanet journal of rare diseases
BACKGROUND: Emerging machine learning technologies are beginning to transform medicine and healthcare and could also improve the diagnosis and treatment of rare diseases. Currently, there are no systematic reviews that investigate, from a general per...

Artificial Intelligence System Approaching Neuroradiologist-level Differential Diagnosis Accuracy at Brain MRI.

Radiology
Background Although artificial intelligence (AI) shows promise across many aspects of radiology, the use of AI to create differential diagnoses for rare and common diseases at brain MRI has not been demonstrated. Purpose To evaluate an AI system for ...

Detection of Rare Objects by Flow Cytometry: Imaging, Cell Sorting, and Deep Learning Approaches.

International journal of molecular sciences
Flow cytometry nowadays is among the main working instruments in modern biology paving the way for clinics to provide early, quick, and reliable diagnostics of many blood-related diseases. The major problem for clinical applications is the detection ...