AIMC Topic: Rare Diseases

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Automatic detection of rare pathologies in fundus photographs using few-shot learning.

Medical image analysis
In the last decades, large datasets of fundus photographs have been collected in diabetic retinopathy (DR) screening networks. Through deep learning, these datasets were used to train automatic detectors for DR and a few other frequent pathologies, w...

Improving rare disease classification using imperfect knowledge graph.

BMC medical informatics and decision making
BACKGROUND: Accurately recognizing rare diseases based on symptom description is an important task in patient triage, early risk stratification, and target therapies. However, due to the very nature of rare diseases, the lack of historical data poses...

Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter?

Genes
The amount of data collected and managed in (bio)medicine is ever-increasing. Thus, there is a need to rapidly and efficiently collect, analyze, and characterize all this information. Artificial intelligence (AI), with an emphasis on deep learning, h...

Common pre-diagnostic features in individuals with different rare diseases represent a key for diagnostic support with computerized pattern recognition?

PloS one
BACKGROUND: Rare diseases (RD) result in a wide variety of clinical presentations, and this creates a significant diagnostic challenge for health care professionals. We hypothesized that there exist a set of consistent and shared phenomena among all ...

HPO2Vec+: Leveraging heterogeneous knowledge resources to enrich node embeddings for the Human Phenotype Ontology.

Journal of biomedical informatics
BACKGROUND: In precision medicine, deep phenotyping is defined as the precise and comprehensive analysis of phenotypic abnormalities, aiming to acquire a better understanding of the natural history of a disease and its genotype-phenotype associations...

Towards the automated economic assessment of newborn screening for rare diseases.

Journal of biomedical informatics
OBJECTIVE: Economic assessments of newborn screening programs for rare diseases involve the use of models and require huge efforts to synthesize information from different sources. Sharing and automatically or semi-automatically reusing this informat...

Rare disease knowledge enrichment through a data-driven approach.

BMC medical informatics and decision making
BACKGROUND: Existing resources to assist the diagnosis of rare diseases are usually curated from the literature that can be limited for clinical use. It often takes substantial effort before the suspicion of a rare disease is even raised to utilize t...

Xrare: a machine learning method jointly modeling phenotypes and genetic evidence for rare disease diagnosis.

Genetics in medicine : official journal of the American College of Medical Genetics
PURPOSE: Despite the successful progress next-generation sequencing technologies has achieved in diagnosing the genetic cause of rare Mendelian diseases, the current diagnostic rate is still far from satisfactory because of heterogeneity, imprecision...

DeepNEU: cellular reprogramming comes of age - a machine learning platform with application to rare diseases research.

Orphanet journal of rare diseases
BACKGROUND: Conversion of human somatic cells into induced pluripotent stem cells (iPSCs) is often an inefficient, time consuming and expensive process. Also, the tendency of iPSCs to revert to their original somatic cell type over time continues to ...

An ontological foundation for ocular phenotypes and rare eye diseases.

Orphanet journal of rare diseases
BACKGROUND: The optical accessibility of the eye and technological advances in ophthalmic diagnostics have put ophthalmology at the forefront of data-driven medicine. The focus of this study is rare eye disorders, a group of conditions whose clinical...