Diagnosis support systems for rare diseases: a scoping review

• Published in: Orphanet journal of rare diseases Vol. 15; no. 1; pp. 94 - 16
• Main Authors: Faviez, Carole, Chen, Xiaoyi, Garcelon, Nicolas, Neuraz, Antoine, Knebelmann, Bertrand, Salomon, Rémi, Lyonnet, Stanislas, Saunier, Sophie, Burgun, Anita
• Format: Journal Article
• Language: English
• Published: London BioMed Central 16.04.2020; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Algorithms; Analysis; Artificial Intelligence; Bibliographic literature; Bioengineering; Clinical decision making; Clinical decision support; Computer Science; Cystic fibrosis; Data mining; Decision making; Diagnosis; Disease; Genetic diseases; Human Genetics; Humans; Knowledge; Learning algorithms; Life Sciences; Machine Learning; Medical diagnosis; Medical prognosis; Medical research; Medicine; Medicine & Public Health; Metadata; Objectives; Other; Pharmacology/Toxicology; Phenotypes; Rare disease; Rare diseases; Rare Diseases - diagnosis; Reproducibility of Results; Review; Reviews; Scoping review; Search strategies; Websites; Phenotype; Rare disease; Machine learning; Scoping review; Diagnosis; Clinical decision support; Genetic diseases; Patient similarity; Artificial intelligence
• ISSN: 1750-1172; 1750-1172
• DOI: 10.1186/s13023-020-01374-z

Introduction Rare diseases affect approximately 350 million people worldwide. Delayed diagnosis is frequent due to lack of knowledge of most clinicians and a small number of expert centers. Consequently, computerized diagnosis support systems have been developed to address these issues, with many relying on rare disease expertise and taking advantage of the increasing volume of generated and accessible health-related data. Our objective is to perform a review of all initiatives aiming to support the diagnosis of rare diseases. Methods A scoping review was conducted based on methods proposed by Arksey and O’Malley. A charting form for relevant study analysis was developed and used to categorize data. Results Sixty-eight studies were retained at the end of the charting process. Diagnosis targets varied from 1 rare disease to all rare diseases. Material used for diagnosis support consisted mostly of phenotype concepts, images or fluids. Fifty-seven percent of the studies used expert knowledge. Two-thirds of the studies relied on machine learning algorithms, and one-third used simple similarities. Manual algorithms were encountered as well. Most of the studies presented satisfying performance of evaluation by comparison with references or with external validation. Fourteen studies provided online tools, most of which aimed to support the diagnosis of all rare diseases by considering queries based on phenotype concepts. Conclusion Numerous solutions relying on different materials and use of various methodologies are emerging with satisfying preliminary results. However, the variability of approaches and evaluation processes complicates the comparison of results. Efforts should be made to adequately validate these tools and guarantee reproducibility and explicability.