scholarly journals GestaltMatcher: Overcoming the limits of rare disease matching using facial phenotypic descriptors

2021 ◽  
Author(s):  
Peter Krawitz ◽  
Tzung-Chien Hsieh ◽  
Aviram Bar-Haim ◽  
Shahida Moosa ◽  
Nadja Ehmke ◽  
...  
Keyword(s):  
Facial Dysmorphism ◽  
Disease Genes ◽  
Facial Morphology ◽  
Craniofacial Abnormalities ◽  
Rare Disorders ◽  
Training Set ◽  
Monogenic Disorders ◽  
Computer Aided ◽  
Phenotype Space ◽  
Space Distance

Abstract 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.

scholarly journals GestaltMatcher: Overcoming the limits of rare disease matching using facial phenotypic descriptors

2021 ◽  
Author(s):  
Tzung-Chien Hsieh ◽  
Aviram Bar-Haim ◽  
Shahida Moosa ◽  
Nadja Ehmke ◽  
Karen W. Gripp ◽  
...  
Keyword(s):  
Facial Dysmorphism ◽  
Disease Genes ◽  
Facial Morphology ◽  
Craniofacial Abnormalities ◽  
Rare Disorders ◽  
Training Set ◽  
Monogenic Disorders ◽  
Computer Aided ◽  
Phenotype Space ◽  
Space Distance

AbstractThe 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.

scholarly journals Computer-Aided Bacillus Detection in Whole-Slide Pathological Images Using a Deep Convolutional Neural Network

2020 ◽  
Vol 10 (12) ◽  
pp. 4059
Author(s):  
Chung-Ming Lo ◽  
Yu-Hung Wu ◽  
Yu-Chuan (Jack) Li ◽  
Chieh-Chi Lee
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Color Information ◽  
Training Set ◽  
Image Block ◽  
Negative Image ◽  
Cad System ◽  
Computer Aided ◽  
Whole Slide Images

Mycobacterial infections continue to greatly affect global health and result in challenging histopathological examinations using digital whole-slide images (WSIs), histopathological methods could be made more convenient. However, screening for stained bacilli is a highly laborious task for pathologists due to the microscopic and inconsistent appearance of bacilli. This study proposed a computer-aided detection (CAD) system based on deep learning to automatically detect acid-fast stained mycobacteria. A total of 613 bacillus-positive image blocks and 1202 negative image blocks were cropped from WSIs (at approximately 20 × 20 pixels) and divided into training and testing samples of bacillus images. After randomly selecting 80% of the samples as the training set and the remaining 20% of samples as the testing set, a transfer learning mechanism based on a deep convolutional neural network (DCNN) was applied with a pretrained AlexNet to the target bacillus image blocks. The transferred DCNN model generated the probability that each image block contained a bacillus. A probability higher than 0.5 was regarded as positive for a bacillus. Consequently, the DCNN model achieved an accuracy of 95.3%, a sensitivity of 93.5%, and a specificity of 96.3%. For samples without color information, the performances were an accuracy of 73.8%, a sensitivity of 70.7%, and a specificity of 75.4%. The proposed DCNN model successfully distinguished bacilli from other tissues with promising accuracy. Meanwhile, the contribution of color information was revealed. This information will be helpful for pathologists to establish a more efficient diagnostic procedure.

scholarly journals Radiographic remission in rheumatoid arthritis quantified by computer-aided joint space analysis (CASJA): a post hoc analysis of the RAPID 1 trial

2020 ◽  
Vol 22 (1) ◽  
Author(s):  
Alexander Pfeil ◽  
Anica Nussbaum ◽  
Diane M. Renz ◽  
Tobias Hoffmann ◽  
Ansgar Malich ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Certolizumab Pegol ◽  
Joint Space Width ◽  
Joint Space ◽  
Post Hoc Analysis ◽  
Space Analysis ◽  
Computer Aided ◽  
Post Hoc ◽  
Space Width ◽  
Space Distance

Abstract Background The reduction of finger joint space width (JSW) in patients with rheumatoid arthritis (RA) is strongly associated with joint destruction. Treatment with certolizumab pegol (CZP), a PEGylated anti-TNF, has been proven to be effective in RA patients. The computer-aided joint space analysis (CAJSA) provides the semiautomated measurement of joint space width at the metacarpal-phalangeal joints (MCP) based on hand radiographs. The aim of this post hoc analysis of the RAPID 1 trial was to quantify MCP joint space distance (JSD-MCP) measured by CAJSA between baseline and week 52 in RA patients treated with certolizumab pegol (CZP) plus methotrexate (MTX) compared with MTX/placebo. Methods Three hundred twenty-eight patients were included in the post hoc analysis and received placebo plus MTX, CZP 200 mg plus MTX and CZP 400 mg plus MTX. All patients underwent X-rays of the hand at baseline and week 52 as well as assessment of finger joint space narrowing of the MCP using CAJSA (Version 1.3.6; Sectra; Sweden). The joint space width (JSW) was expressed as mean joint space distance of the MCP joints I to V (JSD-MCPtotal). Results The MTX group showed a significant reduction of joint space of − 4.8% (JSD-MCPtotal), whereas in patients treated with CZP 200 mg/MTX and CZP 400 mg/MTX a non-significant change (JSD-MCPtotal + 0.6%) was observed. Over 52 weeks, participants with DAS28 remission (DAS28 ≤ 2.6) exhibited a significant joint space increase of + 3.3% (CZP 200 mg plus MTX) and + 3.9% (CZP pegol 400 mg plus MTX). Conclusion CZP plus MTX did not reduce JSD-MCPtotal estimated by CAJSA compared with MTX/placebo. Furthermore, clinical remission (DAS28 ≤ 2.6) in patients treated with CZP plus MTX was associated with an increasing JSD, indicating radiographic remission in RA.

scholarly journals AB085. Diagnosis of Prader-Willi syndrome using computer-aided facial dysmorphism analysis in Thai patients

2017 ◽  
Vol 5 (S2) ◽  
pp. AB085-AB085
Author(s):  
Thipwimol Tim-Aroon ◽  
Nantiya Mongkollarp ◽  
Waraphorn Khunin ◽  
Duangrurdee Wattanasirichaigoon
Keyword(s):  
Facial Dysmorphism ◽  
Prader Willi Syndrome ◽  
Computer Aided

scholarly journals Case Report: Pansynostosis, Chiari I Malformation and Syringomyelia in a Child With Frontometaphyseal Dysplasia 1

2021 ◽  
Vol 9 ◽  
Author(s):  
Jaewon Kim ◽  
Dong-Woo Lee ◽  
Dae-Hyun Jang
Keyword(s):  
Chiari I Malformation ◽  
Facial Dysmorphism ◽  
Craniofacial Abnormalities ◽  
Resonance Imaging ◽  
Targeted Next Generation Sequencing ◽  
The Central Nervous System ◽  
Chiari I ◽  
Whole Spine ◽  
Generation Sequencing ◽  
Spine Magnetic Resonance Imaging

Frontometaphyseal dysplasia 1 (FMD1) is a rare otopalatodigital spectrum disorder (OPDSD) that is inherited as an X-linked trait and it is caused by gain-of-function mutations in the FLNA. It is characterized by generalized skeletal dysplasia, and craniofacial abnormalities including facial dysmorphism (supraorbital hyperostosis, hypertelorism, and down-slanting palpebral fissures). The involvement of the central nervous system in patients with OPDSD is rare. Herein, we present the case of a 12-year-old boy with facial dysmorphism, multiple joint contractures, sensorineural hearing loss, scoliosis, craniosynostosis, and irregular sclerosis with hyperostosis of the skull. Brain and whole-spine magnetic resonance imaging revealed Chiari I malformation with extensive hydrosyringomyelia from the C1 to T12 levels. Targeted next-generation sequencing identified a hemizygous pathologic variant (c.3557C>T/p.Ser1186Leu) in the FLNA, confirming the diagnosis of FMD1. This is the first report of a rare case of OPDSD with pansynostosis and Chiari I malformation accompanied by extensive syringomyelia.

Delineation of Syndromes Due to Partial 6q Imbalances. Trisomy 6q21→qter and Monosomy 6q221→qter in Two Unrelated Patients

1978 ◽  
Vol 27 ◽  
pp. 57-66 ◽  
Author(s):  
B. Dallapiccola ◽  
Franca Dagna Bricarelli ◽  
A. Rasore Quartino ◽  
Maria Cristina Mazzilli ◽  
Rosanna Chisci ◽  
...  
Keyword(s):  
Mental Retardation ◽  
De Novo ◽  
Psychomotor Retardation ◽  
Facial Dysmorphism ◽  
Chromosome 6 ◽  
Craniofacial Abnormalities ◽  
Nasal Bridge ◽  
Growth Deficiency ◽  
Biparietal Diameter ◽  
The Face

Two unrelated patients carrying imbalances involving the long arm of chromosome 6 are described. In the first trisomy 6q21→qter had segregated from a maternal translocation t(6 ; 16)(q15 ; q24). The clinical data of the proposita are compared with those of three other published cases. A partial 6q trisomy syndrome is postulated characterized by: growth deficiency of prenatal onset, psychomotor retardation, craniofacial abnormalities (microcephalia, hypertelorism, downward slanting palpebral fissures, flattened nasal bridge, long philtrum, hypoplastic perioral features, large jaw resulting in a round appearance of the face, receding chin, malformed ears) and dysmorphic extremities (contractures of limbs due to short flexor tendons, hypoplastic fingers, toes and nails). In the second case, monosomy 6q221→qter resulted from a de novo rearrangement and was responsible for mental retardation and facial dysmorphism (reduced biparietal diameter, hypotelorism, absent eyebrows, prominent nose, ptosis, receding chin, dysmorphic ears). Studies of HLA and PGM3 segregation showed normal inheritance patterns and ruled out the location of these genes in bands 6q221→qter.

scholarly journals Large-scale open-source three-dimensional growth curves for clinical facial assessment and objective description of facial dysmorphism

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Harold S. Matthews ◽  
Richard L. Palmer ◽  
Gareth S. Baynam ◽  
Oliver W. Quarrell ◽  
Ophir D. Klein ◽  
...  
Keyword(s):  
Open Source ◽  
Clinical Diagnosis ◽  
Large Scale ◽  
Three Dimensional ◽  
Growth Curves ◽  
Facial Dysmorphism ◽  
Facial Morphology ◽  
Craniofacial Dysmorphism ◽  
Facial Shape ◽  
The Rich

AbstractCraniofacial dysmorphism is associated with thousands of genetic and environmental disorders. Delineation of salient facial characteristics can guide clinicians towards a correct clinical diagnosis and understanding the pathogenesis of the disorder. Abnormal facial shape might require craniofacial surgical intervention, with the restoration of normal shape an important surgical outcome. Facial anthropometric growth curves or standards of single inter-landmark measurements have traditionally supported assessments of normal and abnormal facial shape, for both clinical and research applications. However, these fail to capture the full complexity of facial shape. With the increasing availability of 3D photographs, methods of assessment that take advantage of the rich information contained in such images are needed. In this article we derive and present open-source three-dimensional (3D) growth curves of the human face. These are sequences of age and sex-specific expected 3D facial shapes and statistical models of the variation around the expected shape, derived from 5443 3D images. We demonstrate the use of these growth curves for assessing patients and show that they identify normal and abnormal facial morphology independent from age-specific facial features. 3D growth curves can facilitate use of state-of-the-art 3D facial shape assessment by the broader clinical and biomedical research community. This advance in phenotype description will support clinical diagnosis and the understanding of disease pathogenesis including genotype–phenotype relations.

scholarly journals Predicting Retrosynthetic Reaction using Self-Corrected Transformer Neural Networks

2019 ◽  
Author(s):  
Shuangjia Zheng ◽  
Jiahua Rao ◽  
Zhongyue Zhang ◽  
Jun Xu ◽  
Yuedong Yang
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
State Of The Art ◽  
Neural Network Architecture ◽  
Training Set ◽  
Computer Aided ◽  
Synthesis Planning ◽  
Target Molecules ◽  
Template Free ◽  
Synthetic Routes

<p><a>Synthesis planning is the process of recursively decomposing target molecules into available precursors. Computer-aided retrosynthesis can potentially assist chemists in designing synthetic routes, but at present it is cumbersome and provides results of dissatisfactory quality. In this study, we develop a template-free self-corrected retrosynthesis predictor (SCROP) to perform a retrosynthesis prediction task trained by using the Transformer neural network architecture. In the method, the retrosynthesis planning is converted as a machine translation problem between molecular linear notations of reactants and the products. Coupled with a neural network-based syntax corrector, our method achieves an accuracy of 59.0% on a standard benchmark dataset, which increases >21% over other deep learning methods, and >6% over template-based methods. More importantly, our method shows an accuracy 1.7 times higher than other state-of-the-art methods for compounds not appearing in the training set.</a></p>

scholarly journals Computer-aided prediction of biological activity spectra for chemical compounds: opportunities and limitation

2018 ◽  
Vol 1 (1) ◽  
pp. e00004 ◽  
Author(s):  
D.A. Filimonov ◽  
D.S. Druzhilovskiy ◽  
A.A. Lagunin ◽  
T.A. Gloriozova ◽  
A.V. Rudik ◽  
...  
Keyword(s):  
Biological Activity ◽  
Biological Activities ◽  
Expression Profiles ◽  
Molecular Transport ◽  
Chemical Compounds ◽  
Training Set ◽  
Structure Activity Relationships ◽  
Web Resource ◽  
Structure Activity ◽  
Computer Aided

An essential characteristic of chemical compounds is their biological activity since its presence can become the basis for the use of the substance for therapeutic purposes, or, on the contrary, limit the possibilities of its practical application due to the manifestation of side action and toxic effects. Computer assessment of the biological activity spectra makes it possible to determine the most promising directions for the study of the pharmacological action of particular substances, and to filter out potentially dangerous molecules at the early stages of research. For more than 25 years, we have been developing and improving the computer program PASS (Prediction of Activity Spectra for Substances), designed to predict the biological activity spectrum of substance based on the structural formula of its molecules. The prediction is carried out by the analysis of structure-activity relationships for the training set, which currently contains information on structures and known biological activities for more than one million molecules. The structure of the organic compound is represented in PASS using Multilevel Neighborhoods of Atoms descriptors; the activity prediction for new compounds is performed by the naive Bayes classifier and the structure-activity relationships determined by the analysis of the training set. We have created and improved both local versions of the PASS program and freely available web resources based on PASS (http://www.way2drug.com). They predict several thousand biological activities (pharmacological effects, molecular mechanisms of action, specific toxicity and adverse effects, interaction with the unwanted targets, metabolism and action on molecular transport), cytotoxicity for tumor and non-tumor cell lines, carcinogenicity, induced changes of gene expression profiles, metabolic sites of the major enzymes of the first and second phases of xenobiotics biotransformation, and belonging to substrates and/or metabolites of metabolic enzymes. The web resource Way2Drug is used by over 18,000 researchers from more than 90 countries around the world, which allowed them to obtain over 600,000 predictions and publish about 500 papers describing the obtained results. The analysis of the published works shows that in some cases the interpretation of the prediction results presented by the authors of these publications requires an adjustment. In this work, we provide the theoretical basis and consider, on particular examples, the opportunities and limitations of computer-aided prediction of biological activity spectra.

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