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Table 5 Characteristics of online tools

From: Diagnosis support systems for rare diseases: a scoping review

Tool nameDateData sourcesPerformances: Top 10 rankingRelated articlesURL
Phenomizer2009Phenotype conceptsNA[63]http://compbio.charite.de/phenomizer
BOQA2012Phenotype conceptsNA[64]http://compbio.charite.de/boqa/
Phenotips2013Phenotype conceptsNA[65]http://phenotips.org
FindZebra2013Phenotype concepts63%[66]http://www.findzebra.com/
PhenIX2014Phenotype concepts/genes~ 99%[67]http://compbio.charite.de/PhenIX/
Phenolyzer2015Phenotype concepts/genes~ 85%[69]http://phenolyzer.usc.edu
RDD2016, 2017Phenotype concepts38%[2, 70]http://diseasediscovery.udl.cat/
IEMbase2018Phenotype concepts90%[54]http://www.iembase.org/app
PubCaseFinder2018Phenotype concepts57%[71]https://pubcasefinder.dbcls.jp/
RDAD2018Phenotype concepts/genes95%[73]http://www.unimd.org/RDAD/
GDDP2019Phenotype concepts~ 32%[77]https://gddp.research.cchmc.org/
Xrare2019Phenotype concepts/genes~ 95%[78]https://web.stanford.edu/~xm24/Xrare/
CC-Cruiser2017ImagesNA[44]https://www.cc-cruiser.com/
DeepGestalt2019ImagesNA[62]https://www.face2gene.com/
  1. For each online tool, we listed the publication year, the materials used, the performance indicated in each publication, and the URLs provided in the publications. For the performance, the proportion of accurate diagnoses within the top 10 most relevant disease for each patient is given for all algorithms based on diagnoses recommendation (i.e., providing for each patient a list of potential diagnoses ranked by relevance). These results were provided by the authors of each tool and thus do not allow a comparison of tool performance, as the nature and volume of each dataset were different