GestaltMatcher facilitates rare disease matching using facial phenotype descriptors.
GestaltMatcher facilitates rare disease matching using facial phenotype descriptors.
复制标题
DOI：
10.1038/s41588-021-01010-x
复制
发表时间：
2022-03
期刊：
影响因子：
30.8
通讯作者：
Krawitz, Peter M.
中科院分区：
文献类型：
--
作者：
Hsieh, Tzung-Chien;Bar-Haim, Aviram;Moosa, Shahida;Ehmke, Nadja;Gripp, Karen W.;Pantel, Jean Tori;Danyel, Magdalena;Mensah, Martin Atta;Horn, Denise;Rosnev, Stanislav;Fleischer, Nicole;Bonini, Guilherme;Hustinx, Alexander;Schmid, Alexander;Knaus, Alexej;Javanmardi, Behnam;Klinkhammer, Hannah;Lesmann, Hellen;Sivalingam, Sugirthan;Kamphans, Tom;Meiswinkel, Wolfgang;Ebstein, Frederic;Krueger, Elke;Kuery, Sebastien;Bezieau, Stephane;Schmidt, Axel;Peters, Sophia;Engels, Hartmut;Mangold, Elisabeth;Kreiss, Martina;Cremer, Kirsten;Perne, Claudia;Betz, Regina C.;Bender, Tim;Grundmann-Hauser, Kathrin;Haack, Tobias B.;Wagner, Matias;Brunet, Theresa;Bentzen, Heidi Beate;Averdunk, Luisa;Coetzer, Kimberly Christine;Lyon, Gholson J.;Spielmann, Malte;Schaaf, Christian P.;Mundlos, Stefan;Noethen, Markus M.;Krawitz, Peter M.
许多单基因疾病导致具有特征性面部形态的颅面畸形。这些疾病可以在计算机辅助下一代表型分析工具（如DeepGestalt）的支持下更有效地诊断。这些工具通过对数千张患者照片的训练，学会了将面部表型与潜在的综合征联系起来。然而，这种“监督”方法意味着只有当疾病是训练集的一部分时，诊断才是可能的。为了提高对超罕见疾病的识别，我们创建了GestaltMatcher，它使用基于DeepGestalt框架的深度卷积神经网络。我们使用了17,560名患有1,115种罕见疾病的患者的照片来定义“临床面部表型空间”。表型空间中病例之间的距离定义了综合征的相似性，允许测试患者与分子诊断相匹配，即使该疾病未包括在训练集中。也可以检测到具有先前未知疾病基因的患者之间的相似性。因此，与突变数据相结合，GestaltMatcher可以加速超罕见疾病和面部畸形患者的临床诊断，并能够描绘新的表型。
Many 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 the disorder was part of the training set. To improve recognition of ultra-rare disorders, we created GestaltMatcher, which uses a deep convolutional neural network based on the DeepGestalt framework. We used photographs of 17,560 patients with 1,115 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, as well as enable the delineation of novel phenotypes.
登录
查看更多内容
影响因子：
7.4
作者：
Pantel JT;Hajjir N;Danyel M;Elsner J;Abad-Perez AT;Hansen P;Mundlos S;Spielmann M;Horn D;Ott CE;Mensah MA
通讯作者：
Mensah MA
影响因子：
9.8
作者：
Morimoto, Marie;Waller-Evans, Helen;Malicdan, May Christine V.
通讯作者：
Malicdan, May Christine V.
影响因子：
9.8
作者：
Schuurs-Hoeijmakers, Janneke H. M.;Oh, Edwin C.;Brunner, Han G.
通讯作者：
Brunner, Han G.
影响因子：
9.8
作者：
Diets, Illja J.;van der Donk, Roos;Jongmans, Marjolijn C. J.
通讯作者：
Jongmans, Marjolijn C. J.
影响因子：
3.5
作者：
Dudding-Byth, Tracy;Baxter, Anne;Lovell, Brian C.
通讯作者：
Lovell, Brian C.