Diagnosis support systems for rare diseases: a scoping review

Abstract

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.

Keywords: Artificial intelligence; Clinical decision support; Diagnosis; Genetic diseases; Machine learning; Patient similarity; Phenotype; Rare disease; Scoping review.

Fig. 1

Flowchart of the screening process

Fig. 2

Correlations between the number of targeted diseases and material nature. All studies directed to all rare/genetic diseases were based on phenotype concepts. Studies directed to a class or one specific disease could take advantage of disease-related materials such as images or fluids

Fig. 3

Correlations between the knowledge model and material nature. Knowledge-based models were based on phenotype concepts or combinations of clinical features. Data-driven models were mostly based on images or fluids

Fig. 4

Correlations between the knowledge model and the methods. Data-driven systems were all based on machine learning (associated or not to simple similarity measurement). Knowledge-based systems were either based on simple similarity or manually generated algorithms