The majority of monogenic disorders cause craniofacial abnormalities with characteristic facial morphology. These disorders can be diagnosed more e ciently with the support of computer-aided nextgeneration phenotyping tools, such as DeepGestalt. These tools have learned to associate facial phenotypes with the underlying syndrome through training on thousands of patient photographs. However, this "supervised" approach means that diagnoses are only possible if they were part of the training set. To improve recognition of ultra-rare diseases, we created GestaltMatcher, which uses a deep convolutional neural network based on the DeepGestalt framework. We used photographs of 21,836 patients with 1,362 rare disorders to de ne a "Clinical Face Phenotype Space". Distance between cases in the phenotype space de nes syndromic similarity, allowing test patients to be matched to a molecular diagnosis even when the disorder was not included in the training set. Similarities among patients with previously unknown disease genes can also be detected. Therefore, in concert with mutation data, GestaltMatcher could accelerate the clinical diagnosis of patients with ultra-rare disorders and facial dysmorphism.
The majority of monogenic disorders cause craniofacial abnormalities with characteristic facial morphology. These disorders can be diagnosed more efficiently with the support of computer-aided next-generation phenotyping tools, such as DeepGestalt. These tools have learned to associate facial phenotypes with the underlying syndrome through training on thousands of patient photographs. However, this “supervised” approach means that diagnoses are only possible if they were part of the training set. To improve recognition of ultra-rare diseases, we created GestaltMatcher, which uses a deep convolutional neural network based on the DeepGestalt framework. We used photographs of 21,836 patients with 1,362 rare disorders to define a “Clinical Face Phenotype Space”. Distance between cases in the phenotype space defines syndromic similarity, allowing test patients to be matched to a molecular diagnosis even when the disorder was not included in the training set. Similarities among patients with previously unknown disease genes can also be detected. Therefore, in concert with mutation data, GestaltMatcher could accelerate the clinical diagnosis of patients with ultra-rare disorders and facial dysmorphism.
Background The diagnosis of rare diseases poses a particular challenge to clinicians. This study analyzes whether patients’ pain drawings (PDs) help in the differentiation of two pain-associated rare diseases, Ehlers-Danlos Syndrome (EDS) and Guillain-Barré Syndrome (GBS). Method The study was designed as a prospective, observational, single-center study. The sample comprised 60 patients with EDS (3 male, 52 female, 5 without gender information; 39.2 ± 11.4 years) and 32 patients with GBS (10 male, 20 female, 2 without gender information; 50.5 ± 13.7 years). Patients marked areas afflicted by pain on a sketch of a human body with anterior, posterior, and lateral views. PDs were electronically scanned and processed. Each PD was classified based on the Ružička similarity to the EDS and the GBS averaged image (pain profile) in a leave-one-out cross validation approach. A receiver operating characteristic (ROC) curve was plotted. Results 60–80% of EDS patients marked the vertebral column with the neck and the tailbone and the knee joints as pain areas, 40–50% the shoulder-region, the elbows and the thumb saddle joint. 60–70% of GBS patients marked the dorsal and plantar side of the feet as pain areas, 40–50% the palmar side of the fingertips, the dorsal side of the left palm and the tailbone. 86% of the EDS patients and 96% of the GBS patients were correctly identified by computing the Ružička similarity. The ROC curve yielded an excellent area under the curve value of 0.95. Conclusion PDs are a useful and economic tool to differentiate between GBS and EDS. Further studies should investigate its usefulness in the diagnosis of other pain-associated rare diseases. This study was registered in the German Clinical Trials Register, No. DRKS00014777 (Deutsches Register klinischer Studien, DRKS), on 01.06.2018.
Next‐generation phenotyping (NGP) is an application of advanced methods of computer vision on medical imaging data such as portrait photos of individuals with rare disorders. NGP on portraits results in gestalt scores that can be used for the selection of appropriate genetic tests, and for the interpretation of the molecular data. Here, we report on an exceptional case of a young girl that was presented at the age of 8 and 15 and enrolled in NGP diagnostics on the latter occasion. The girl had clinical features associated with Koolen‐de Vries syndrome (KdVS) and a suggestive facial gestalt. However, chromosomal microarray (CMA), Sanger sequencing, multiplex ligation‐dependent probe analysis (MLPA), and trio exome sequencing remained inconclusive. Based on the highly indicative gestalt score for KdVS, the decision was made to perform genome sequencing to also evaluate noncoding variants. This analysis revealed a 4.7 kb de novo deletion partially affecting intron 6 and exon 7 of the KANSL1 gene. This is the smallest reported structural variant to date for this phenotype. The case illustrates how NGP can be integrated into the iterative diagnostic process of test selection and interpretation of sequencing results.
Most individuals with rare diseases initially consult their primary care physician. For a subset of rare diseases, efficient diagnostic pathways are available. However, ultra-rare diseases often require both expert clinical knowledge and comprehensive genetic diagnostics, which poses structural challenges for public healthcare systems. To address these challenges within Germany, a novel structured diagnostic concept, based on multidisciplinary expertise at established university hospital centers for rare diseases (CRDs), was evaluated in the three year prospective study TRANSLATE NAMSE. A key goal of TRANSLATE NAMSE was to assess the clinical value of exome sequencing (ES) in the ultra-rare disease population. The aims of the present study were to perform a systematic investigation of the phenotypic and molecular genetic data of TRANSLATE NAMSE patients who had undergone ES in order to determine the yield of both ultra-rare diagnoses and novel gene-disease associations; and determine whether the complementary use of machine learning and artificial intelligence (AI) tools improved diagnostic effectiveness and efficiency. ES was performed for 1,577 patients (268 adult and 1,309 pediatric). Molecular genetic diagnoses were established in 499 patients (74 adult and 425 pediatric). A total of 370 distinct molecular genetic causes were established. The majority of these concerned known disorders, most of which were ultra-rare. During the diagnostic process, 34 novel and 23 candidate genotype-phenotype associations were delineated, mainly in individuals with neurodevelopmental disorders. To determine the likelihood that ES will lead to a molecular diagnosis in a given patient, based on the respective clinical features only, we developed a statistical framework called YieldPred. The genetic data of a subcohort of 224 individuals that also gave consent to the computer-assisted analysis of their facial images were processed with the AI tool Prioritization of Exome Data by Image Analysis (PEDIA) and showed superior performance in variant prioritization. The present analyses demonstrated that the novel structured diagnostic concept facilitated the identification of ultra-rare genetic disorders and novel gene-disease associations on a national level and that the machine learning and AI tools improved diagnostic effectiveness and efficiency for ultra-rare genetic disorders.
BackgroundRare diseases (RD) in rheumatology are heterogeneous and thus lack a systemic classification. By definition, prevalences of RD range up to 5/10 000 in Europe. Clinical courses comprise a vast variety of musculoskeletal symptoms such as arthritis, myositis, vasculitis, autoimmune organ involvement and bone diseases. The impact of RD for health care systems is widely unknown and individual patients are prone to unsatisfactory diagnoses and/or treatments.ObjectivesThe objective of this study was to identify and classify RD in rheumatology and estimate their combined prevalence. The data may give a better understanding for this area of rheumatology, may aid specified centers for RD, and perhaps sharpen the overall perception of health care systems.MethodsDatabases (pubmed.org, abstract archives of the European League Against Rheumatism and the American College of Rheumatology, and Orpha.net) were searched for the following terms: rare in combination with arthritis, arteritis, connective tissue disease, rheumatic and vasculitis. Furthermore, terms were used in various combinations including arthralgia, autoimmune, fever, inflammation, joint pain, muscle pain, myalgia and swollen joint. Identified syndromes were then classified according to the following terms and data: differential diagnosis, etiology, genetics, prevalence, principal symptom, prognosis, and therapy. The overall total point prevalence for all RD in rheumatology was estimated by adding up all single point prevalences available.ResultsA total of 82 syndromes and diseases were identified. Classification was as follows: arthritis (n=17) and vasculitis (n=18), fever syndromes (n=13), collagen diseases (n=10), myositis (n=9), and overlaps/others (n=15). Fifty-two diseases showed a chronic progressive course. The mortality was variable in many diseases and thus could not be determined precisely. Point prevalence data were available for 49 syndromes and diseases, and added up to a total point prevalence of 49/ 10 000. Forty-nine of these in part pathophysiologically quite distinct RD are treated with corticosteroids.ConclusionsRare diseases in rheumatology are most often chronic or progressive. Symptoms are variable, they include arthritis, organ and skin involvement. The estimated combined prevalence of all RD in rheumatology is significant with 49/10 000: it is double or more than that of ankylosing spondylitis with 18/10 000 (1). Treatment options are often restricted to corticosteroids presumably because of the scarcity of randomized controlled trials. Health care systems should assign RD the same importance as any other common disease in rheumatology.ReferencesExarchou S, Lindström U, Askling J, Eriksson JK, Forsblad-d'Elia H, Neovius M, Turesson C, Kristensen LE, Jacobsson LT. The prevalence of clinically diagnosed ankylosing spondylitis and its clinical manifestations: a nationwide register study. Arthritis Res Ther 2015; 201; 17(1):118.Disclosure of InterestNone declared
Background Rare diseases (RDs) in rheumatology as a group have a high prevalence, but randomized controlled trials are hampered by their heterogeneity and low individual prevalence. To survey the current evidence of pharmacotherapies for rare rheumatic diseases, we conducted a systematic review and meta-analysis. Randomized controlled trials (RCTs) of RDs in rheumatology for different pharmaco-interventions were included into this meta-analysis if there were two or more trials investigating the same RD and using the same assessment tools or outcome parameters. The Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, and PUBMED were searched up to April 2nd 2020. The overall objective of this study was to identify RCTs of RDs in rheumatology, evaluate the overall quality of these studies, outline the evidence of pharmacotherapy, and summarize recommended therapeutic regimens. Results We screened 187 publications, and 50 RCTs met our inclusion criteria. In total, we analyzed data of 13 different RDs. We identified several sources of potential bias, such as a lack of description of blinding methods and allocation concealment, as well as small size of the study population. Meta-analysis was possible for 26 studies covering six RDs: Hunter disease, Behçet’s disease, giant cell arteritis, ANCA-associated vasculitis, reactive arthritis, and systemic sclerosis. The pharmacotherapies tested in these studies consisted of immunosuppressants, such as corticosteroids, methotrexate and azathioprine, or biologicals. We found solid evidence for idursulfase as a treatment for Hunter syndrome. In Behçet’s disease, apremilast and IF-α showed promising results with regard to total and partial remission, and Tocilizumab with regard to relapse-free remission in giant cell arteritis. Rituximab, cyclophosphamide, and azathioprine were equally effective in ANCA-associated vasculitis, while mepolizumab improved the efficacy of glucocorticoids. The combination of rifampicin and azithromycin showed promising results in reactive arthritis, while there was no convincing evidence for the efficacy of pharmacotherapy in systemic sclerosis. Conclusion For some diseases such as systemic sclerosis, ANCA-associated vasculitis, or Behcet's disease, higher quality trials were available. These RCTs showed satisfactory efficacies for immunosuppressants or biological drugs, except for systemic sclerosis. More high quality RCTs are urgently warranted for a wide spectrum of RDs in rheumatology.
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