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Table 5 Characteristics of online tools

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

Tool name Date Data sources Performances: Top 10 ranking Related articles URL
Phenomizer 2009 Phenotype concepts NA [63] http://compbio.charite.de/phenomizer
BOQA 2012 Phenotype concepts NA [64] http://compbio.charite.de/boqa/
Phenotips 2013 Phenotype concepts NA [65] http://phenotips.org
FindZebra 2013 Phenotype concepts 63% [66] http://www.findzebra.com/
PhenIX 2014 Phenotype concepts/genes ~ 99% [67] http://compbio.charite.de/PhenIX/
Phenolyzer 2015 Phenotype concepts/genes ~ 85% [69] http://phenolyzer.usc.edu
RDD 2016, 2017 Phenotype concepts 38% [2, 70] http://diseasediscovery.udl.cat/
IEMbase 2018 Phenotype concepts 90% [54] http://www.iembase.org/app
PubCaseFinder 2018 Phenotype concepts 57% [71] https://pubcasefinder.dbcls.jp/
RDAD 2018 Phenotype concepts/genes 95% [73] http://www.unimd.org/RDAD/
GDDP 2019 Phenotype concepts ~ 32% [77] https://gddp.research.cchmc.org/
Xrare 2019 Phenotype concepts/genes ~ 95% [78] https://web.stanford.edu/~xm24/Xrare/
CC-Cruiser 2017 Images NA [44] https://www.cc-cruiser.com/
DeepGestalt 2019 Images NA [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