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

Brasil, Sandra; Pascoal, Carlota; Francisco, Rita; Ferreira, Vanessa; Videira, Paula A.; Valadão, Gonçalo

doi:10.3390/genes10120978

Cited by 88 publications

(76 citation statements)

References 99 publications

(152 reference statements)

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“…In rare disease research, this is of particular interest due to the scarcity and the spread of the data among the different centers [ 36 ]. Various approaches have been applied in the area of rare diseases, especially in looking for genetic associations [ 37 ] and making correlations between genotype and phenotype [ 38 ].…”

Section: Discussionmentioning

confidence: 99%

Identification of risk features for complication in Gaucher’s disease patients: a machine learning analysis of the Spanish registry of Gaucher disease

Andrade‐Campos

Frutos

Cebolla

et al. 2020

Orphanet J Rare Dis

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Background Since enzyme replacement therapy for Gaucher disease (MIM#230800) has become available, both awareness of and the natural history of the disease have changed. However, there remain unmet needs such as the identification of patients at risk of developing bone crisis during therapy and late complications such as cancer or parkinsonism. The Spanish Gaucher Disease Registry has worked since 1993 to compile demographic, clinical, genetic, analytical, imaging and follow-up data from more than 400 patients. The aims of this study were to discover correlations between patients’ characteristics at diagnosis and to identify risk features for the development of late complications; for this a machine learning approach involving correlation networks and decision trees analyses was applied. Results A total of 358 patients, 340 type 1 Gaucher disease and 18 type 3 cases were selected. 18% were splenectomyzed and 39% had advanced bone disease. 81% of cases carried heterozygous genotype. 47% of them were diagnosed before the year 2000. Mean age at diagnosis and therapy were 28 and 31.5 years old (y.o.) respectively. 4% developed monoclonal gammopathy undetermined significance or Parkinson Disease, 6% cancer, and 10% died before this study. Previous splenectomy correlates with the development of skeletal complications and severe bone disease (p = 0.005); serum levels of IgA, delayed age at start therapy (> 9.5 y.o. since diagnosis) also correlates with severe bone disease at diagnosis and with the incidence of bone crisis during therapy. High IgG (> 1750 mg/dL) levels and age over 60 y.o. at diagnosis were found to be related with the development of cancer. When modelling the decision tree, patients with a delayed diagnosis and therapy were the most severe and with higher risk of complications. Conclusions Our work confirms previous observations, highlights the importance of early diagnosis and therapy and identifies new risk features such as high IgA and IgG levels for long-term complications.

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Section: Discussionmentioning

confidence: 99%

Identification of risk features for complication in Gaucher’s disease patients: a machine learning analysis of the Spanish registry of Gaucher disease

Andrade‐Campos

Frutos

Cebolla

et al. 2020

Orphanet J Rare Dis

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“…In rare disease research, this is of particular interest due to the scarcity and the spread of the data among the different centers (33). Various approaches have been applied in the area of rare diseases, especially in looking for genetic associations (34) and making correlations between genotype and phenotype (35).…”

Section: Discussionmentioning

confidence: 99%

Identification of risk features for complication in Gaucher’s Disease patients: A Machine Learning analysis of the Spanish Registry of Gaucher Disease.

Andrade‐Campos

Frutos

Cebolla

et al. 2020

Preprint

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Background Since enzyme replacement therapy (ERT) for Gaucher disease (GD) (MIM#230800) has become available, both awareness of and the natural history of the disease have changed. However, there remain unmet needs such as the identification of patients at risk of developing bone crisis during therapy and late complications such as cancer or parkinsonism. The Spanish Gaucher Disease Registry (SGDR) has worked since 1996 to compile demographic, clinical, genetic, analytical, imaging and follow-up data from more than 400 GD patients. The aims of this study were to discover correlations between patients’ characteristics at diagnosis and to identify risk features for the development of late complications; for this a machine learning approach involving network and decision trees analysis was applied. Results A total of 358 GD patients, 340 GD1 and 18 GD3 cases were selected. 18% were splenectomyzed and 39% had advanced bone disease. 81% of cases carried heterozygous genotype. 47% of them were diagnosed before the year 2000. Mean age at diagnosis and therapy were 28 and 31.5 years old (y.o.) respectively. 4% developed MGUS or Parkinson Disease, 6% cancer, and 10% died before this study. Previous splenectomy correlates with the development of skeletal complications and severe bone disease (p = 0.005); serum levels of IgA, delayed age at ERT start (> 9.5 y.o.since diagnosis) also correlates with severe bone disease at diagnosis and with the incidence of bone crisis during ERT. High IgG (> 1750 mg/dL) levels and age over 60 y.o. at diagnosis were found to be related with the development of cancer. When modelling the decision tree, patients with a delayed diagnosis and therapy were the most severe and with higher risk of complications. Conclusions Our work confirms previous observations, highlights the importance of early diagnosis and therapy and identifies new risk features such as high IgA and IgG levels for long-term complications.

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“…The development of diagnosis support tools for rare diseases has gained more interest in recent years (2 articles were published in 2009 compared with 12 articles in 2018). One recent review published in 2019 [83] aimed to analyze AI solutions in rare diseases but did not specifically focus on diagnosis support systems. Moreover, they did not include all methods based on similarity measurements between patients and rare diseases.…”

Section: Overviewmentioning

confidence: 99%

Diagnosis support systems for rare diseases: a scoping review

Faviez

Chen

Garcelon

et al. 2020

Orphanet J Rare Dis

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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.

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

Cited by 88 publications

References 99 publications

Identification of risk features for complication in Gaucher’s disease patients: a machine learning analysis of the Spanish registry of Gaucher disease

Identification of risk features for complication in Gaucher’s disease patients: a machine learning analysis of the Spanish registry of Gaucher disease

Identification of risk features for complication in Gaucher’s Disease patients: A Machine Learning analysis of the Spanish Registry of Gaucher Disease.

Diagnosis support systems for rare diseases: a scoping review

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