Centre de Recherche des Cordeliers, INSERM, Université de Paris, Sorbonne Université, F-75006, Paris, France.
Institut Imagine, Université de Paris, F-75015, Paris, France.
Orphanet J Rare Dis. 2020 Apr 16;15(1):94. doi: 10.1186/s13023-020-01374-z.
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.
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.
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.
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.
罕见病影响着全球约 3.5 亿人。由于大多数临床医生缺乏相关知识和数量有限的专家中心，导致诊断常常延误。因此，已经开发了计算机化的诊断支持系统来解决这些问题，其中许多系统依赖于罕见病专业知识，并利用不断增加的生成和可获取的健康相关数据。我们的目标是对所有旨在支持罕见病诊断的举措进行综述。
基于 Arksey 和 O'Malley 提出的方法进行了范围综述。制定了一个图表分析表格，用于对数据进行分类。
在图表制作过程结束时，保留了 68 项研究。诊断目标从 1 种罕见病到所有罕见病不等。用于诊断支持的材料主要包括表型概念、图像或体液。57%的研究使用了专业知识。三分之二的研究依赖于机器学习算法，三分之一的研究使用简单的相似性。也遇到了手动算法。大多数研究通过与参考资料或外部验证进行比较，其评估结果令人满意。有 14 项研究提供了在线工具，其中大多数旨在通过考虑基于表型概念的查询来支持所有罕见病的诊断。
涌现出了许多依赖不同材料和使用各种方法的解决方案，且初步结果令人满意。然而，方法和评估过程的多样性使得结果的比较变得复杂。应努力充分验证这些工具，并保证其可重复性和可解释性。
