BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Faviez C, Chen X, Garcelon N, Neuraz A, Knebelmann B, Salomon R, Lyonnet S, Saunier S, Burgun A. Diagnosis support systems for rare diseases: a scoping review. Orphanet J Rare Dis 2020;15:94. [PMID: 32299466 DOI: 10.1186/s13023-020-01374-z] [Cited by in Crossref: 11] [Cited by in F6Publishing: 8] [Article Influence: 5.5] [Reference Citation Analysis]
Number Citing Articles
1 Álvaro-Sánchez S, Abreu-Rodríguez I, Abulí A, Serra-Juhe C, Garrido-Navas MDC. Current Status of Genetic Counselling for Rare Diseases in Spain. Diagnostics (Basel) 2021;11:2320. [PMID: 34943558 DOI: 10.3390/diagnostics11122320] [Reference Citation Analysis]
2 Radin M, Foddai SG, Barinotti A, Cecchi I, Rubini E, Sciascia S, Roccatello D. Reducing the diagnostic delay in Antiphospholipid Syndrome over time: a real world observation. Orphanet J Rare Dis 2021;16:280. [PMID: 34134750 DOI: 10.1186/s13023-021-01906-1] [Reference Citation Analysis]
3 Schon KR, Ratnaike T, van den Ameele J, Horvath R, Chinnery PF. Mitochondrial Diseases: A Diagnostic Revolution. Trends Genet 2020;36:702-17. [PMID: 32674947 DOI: 10.1016/j.tig.2020.06.009] [Cited by in Crossref: 21] [Cited by in F6Publishing: 23] [Article Influence: 10.5] [Reference Citation Analysis]
4 Decherchi S, Pedrini E, Mordenti M, Cavalli A, Sangiorgi L. Opportunities and Challenges for Machine Learning in Rare Diseases. Front Med (Lausanne) 2021;8:747612. [PMID: 34676229 DOI: 10.3389/fmed.2021.747612] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Willmen T, Völkel L, Ronicke S, Hirsch MC, Kaufeld J, Rychlik RP, Wagner AD. Health economic benefits through the use of diagnostic support systems and expert knowledge. BMC Health Serv Res 2021;21:947. [PMID: 34503507 DOI: 10.1186/s12913-021-06926-y] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Ji M, Genchev GZ, Huang H, Xu T, Lu H, Yu G. Evaluation Framework for Successful Artificial Intelligence-Enabled Clinical Decision Support Systems: Mixed Methods Study. J Med Internet Res 2021;23:e25929. [PMID: 34076581 DOI: 10.2196/25929] [Reference Citation Analysis]
7 Wester L, Mücke M, Bender TTA, Sellin J, Klawonn F, Conrad R, Szczypien N. Pain drawings as a diagnostic tool for the differentiation between two pain-associated rare diseases (Ehlers-Danlos-Syndrome, Guillain-Barré-Syndrome). Orphanet J Rare Dis 2020;15:323. [PMID: 33203450 DOI: 10.1186/s13023-020-01542-1] [Reference Citation Analysis]
8 Jiang J, Lei S, Zhu M, Li R, Yue J, Chen J, Li Z, Gong J, Lin D, Wu X, Lin Z, Lin H. Improving the Generalizability of Infantile Cataracts Detection via Deep Learning-Based Lens Partition Strategy and Multicenter Datasets. Front Med (Lausanne) 2021;8:664023. [PMID: 34026791 DOI: 10.3389/fmed.2021.664023] [Reference Citation Analysis]
9 Hurvitz N, Azmanov H, Kesler A, Ilan Y. Establishing a second-generation artificial intelligence-based system for improving diagnosis, treatment, and monitoring of patients with rare diseases. Eur J Hum Genet 2021. [PMID: 34276056 DOI: 10.1038/s41431-021-00928-4] [Reference Citation Analysis]
10 Dohi E, Bangash AH. Visualizing the phenotype diversity: a case study of Alexander disease. Genomics Inform 2021;19:e28. [PMID: 34638175 DOI: 10.5808/gi.21016] [Reference Citation Analysis]
11 Zerka F, Urovi V, Bottari F, Leijenaar RTH, Walsh S, Gabrani-Juma H, Gueuning M, Vaidyanathan A, Vos W, Occhipinti M, Woodruff HC, Dumontier M, Lambin P. Privacy preserving distributed learning classifiers - Sequential learning with small sets of data. Comput Biol Med 2021;136:104716. [PMID: 34364262 DOI: 10.1016/j.compbiomed.2021.104716] [Reference Citation Analysis]