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Khan DZ, Hanrahan JG, Baldeweg SE, Dorward NL, Stoyanov D, Marcus HJ. Current and Future Advances in Surgical Therapy for Pituitary Adenoma. Endocr Rev 2023; 44:947-959. [PMID: 37207359 PMCID: PMC10502574 DOI: 10.1210/endrev/bnad014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 03/14/2023] [Accepted: 05/17/2023] [Indexed: 05/21/2023]
Abstract
The vital physiological role of the pituitary gland, alongside its proximity to critical neurovascular structures, means that pituitary adenomas can cause significant morbidity or mortality. While enormous advancements have been made in the surgical care of pituitary adenomas, numerous challenges remain, such as treatment failure and recurrence. To meet these clinical challenges, there has been an enormous expansion of novel medical technologies (eg, endoscopy, advanced imaging, artificial intelligence). These innovations have the potential to benefit each step of the patient's journey, and ultimately, drive improved outcomes. Earlier and more accurate diagnosis addresses this in part. Analysis of novel patient data sets, such as automated facial analysis or natural language processing of medical records holds potential in achieving an earlier diagnosis. After diagnosis, treatment decision-making and planning will benefit from radiomics and multimodal machine learning models. Surgical safety and effectiveness will be transformed by smart simulation methods for trainees. Next-generation imaging techniques and augmented reality will enhance surgical planning and intraoperative navigation. Similarly, surgical abilities will be augmented by the future operative armamentarium, including advanced optical devices, smart instruments, and surgical robotics. Intraoperative support to surgical team members will benefit from a data science approach, utilizing machine learning analysis of operative videos to improve patient safety and orientate team members to a common workflow. Postoperatively, neural networks leveraging multimodal datasets will allow early detection of individuals at risk of complications and assist in the prediction of treatment failure, thus supporting patient-specific discharge and monitoring protocols. While these advancements in pituitary surgery hold promise to enhance the quality of care, clinicians must be the gatekeepers of the translation of such technologies, ensuring systematic assessment of risk and benefit prior to clinical implementation. In doing so, the synergy between these innovations can be leveraged to drive improved outcomes for patients of the future.
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Affiliation(s)
- Danyal Z Khan
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK
| | - John G Hanrahan
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK
| | - Stephanie E Baldeweg
- Department of Diabetes & Endocrinology, University College London Hospitals NHS Foundation Trust, London NW1 2BU, UK
- Centre for Obesity and Metabolism, Department of Experimental and Translational Medicine, Division of Medicine, University College London, London WC1E 6BT, UK
| | - Neil L Dorward
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK
| | - Danail Stoyanov
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK
- Digital Surgery Ltd, Medtronic, London WD18 8WW, UK
| | - Hani J Marcus
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK
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Ruiter AM, Wang Z, Yin Z, Naber WC, Simons J, Blom JT, van Gemert JC, Verschuuren JJGM, Tannemaat MR. Assessing facial weakness in myasthenia gravis with facial recognition software and deep learning. Ann Clin Transl Neurol 2023; 10:1314-1325. [PMID: 37292032 PMCID: PMC10424649 DOI: 10.1002/acn3.51823] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 05/23/2023] [Accepted: 05/24/2023] [Indexed: 06/10/2023] Open
Abstract
OBJECTIVE Myasthenia gravis (MG) is an autoimmune disease leading to fatigable muscle weakness. Extra-ocular and bulbar muscles are most commonly affected. We aimed to investigate whether facial weakness can be quantified automatically and used for diagnosis and disease monitoring. METHODS In this cross-sectional study, we analyzed video recordings of 70 MG patients and 69 healthy controls (HC) with two different methods. Facial weakness was first quantified with facial expression recognition software. Subsequently, a deep learning (DL) computer model was trained for the classification of diagnosis and disease severity using multiple cross-validations on videos of 50 patients and 50 controls. Results were validated using unseen videos of 20 MG patients and 19 HC. RESULTS Expression of anger (p = 0.026), fear (p = 0.003), and happiness (p < 0.001) was significantly decreased in MG compared to HC. Specific patterns of decreased facial movement were detectable in each emotion. Results of the DL model for diagnosis were as follows: area under the curve (AUC) of the receiver operator curve 0.75 (95% CI 0.65-0.85), sensitivity 0.76, specificity 0.76, and accuracy 76%. For disease severity: AUC 0.75 (95% CI 0.60-0.90), sensitivity 0.93, specificity 0.63, and accuracy 80%. Results of validation, diagnosis: AUC 0.82 (95% CI: 0.67-0.97), sensitivity 1.0, specificity 0.74, and accuracy 87%. For disease severity: AUC 0.88 (95% CI: 0.67-1.0), sensitivity 1.0, specificity 0.86, and accuracy 94%. INTERPRETATION Patterns of facial weakness can be detected with facial recognition software. Second, this study delivers a 'proof of concept' for a DL model that can distinguish MG from HC and classifies disease severity.
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Affiliation(s)
- Annabel M. Ruiter
- Department of NeurologyLeiden University Medical CenterLeidenthe Netherlands
| | - Ziqi Wang
- Vision LabDelft University of TechnologyDelftthe Netherlands
| | - Zhao Yin
- Vision LabDelft University of TechnologyDelftthe Netherlands
| | - Willemijn C. Naber
- Department of NeurologyLeiden University Medical CenterLeidenthe Netherlands
| | - Jerrel Simons
- Department of NeurologyLeiden University Medical CenterLeidenthe Netherlands
| | - Jurre T. Blom
- Medical Illustrator at www.jurreblom.nlApeldoornthe Netherlands
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Schneider H, Hadj-Rabia S, Faschingbauer F, Bodemer C, Grange DK, Norton ME, Cavalli R, Tadini G, Stepan H, Clarke A, Guillén-Navarro E, Maier-Wohlfart S, Bouroubi A, Porte F. Protocol for the Phase 2 EDELIFE Trial Investigating the Efficacy and Safety of Intra-Amniotic ER004 Administration to Male Subjects with X-Linked Hypohidrotic Ectodermal Dysplasia. Genes (Basel) 2023; 14:153. [PMID: 36672894 PMCID: PMC9858920 DOI: 10.3390/genes14010153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 12/09/2022] [Accepted: 12/28/2022] [Indexed: 01/09/2023] Open
Abstract
X-linked hypohidrotic ectodermal dysplasia (XLHED) is a rare genetic disorder characte-rised by abnormal development of the skin and its appendages, such as hair and sweat glands, the teeth, and mucous glands of the airways, resulting in serious, sometimes life-threatening complications like hyperthermia or recurrent respiratory infections. It is caused by pathogenic variants of the ectodysplasin A gene (EDA). Most affected males are hemizygous for EDA null mutations that lead to the absence or inactivity of the signalling protein ectodysplasin A1 (EDA1) and, thus, to the full-blown phenotype with inability to perspire and few if any teeth. There are currently no long-term treatment options for XLHED. ER004 represents a first-in-class protein replacement molecule designed for specific, high-affinity binding to the endogenous EDA1 receptor (EDAR). Its proposed mechanism of action is the replacement of missing EDA1 in yet unborn patients with XLHED. Once bound to EDAR, ER004 activates the EDA/NFκB signalling pathway, which triggers the transcription of genes involved in the normal development of multiple tissues. Following preclinical studies, named-patient use cases demonstrated significant potential of ER004 in affected males treated in utero during the late second and third trimesters of pregnancy. In order to confirm these results, we started the EDELIFE trial, a prospective, open-label, genotype-match controlled, multicentre clinical study to investigate the efficacy and safety of intra-amniotic ER004 administration as a prenatal treatment for male subjects with XLHED. This article summarises the rationale, the study protocol, ethical issues of the trial, and potential pitfalls.
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Affiliation(s)
- Holm Schneider
- Center for Ectodermal Dysplasias and Department of Pediatrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, 91054 Erlangen, Germany
| | - Smail Hadj-Rabia
- Department of Dermatology, Reference Centre for Genodermatoses and Rare Skin Diseases (MAGEC), Hopital Universitaire Necker-Enfants Malades, Assistance Publique—Hospitals of Paris, University of Paris-Cité, 75743 Paris, France
| | - Florian Faschingbauer
- Department of Obstetrics and Gynecology, University Hospital Erlangen, 91054 Erlangen, Germany
| | - Christine Bodemer
- Department of Dermatology, Reference Centre for Genodermatoses and Rare Skin Diseases (MAGEC), Hopital Universitaire Necker-Enfants Malades, Assistance Publique—Hospitals of Paris, University of Paris-Cité, 75743 Paris, France
| | - Dorothy K. Grange
- Department of Pediatrics, Division of Genetics and Genomic Medicine, Washington University, St. Louis, MO 63110, USA
| | - Mary E. Norton
- Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Francisco, CA 94143, USA
| | - Riccardo Cavalli
- Pediatric Dermatology Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Department of Clinical Sciences and Community Health, 20122 Milan, Italy
| | - Gianluca Tadini
- Pediatric Dermatology Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Department of Clinical Sciences and Community Health, 20122 Milan, Italy
| | - Holger Stepan
- Department of Obstetrics, University Hospital Leipzig, 04103 Leipzig, Germany
| | - Angus Clarke
- Institute of Medical Genetics, Division of Cancer & Genetics, School of Medicine, Cardiff University, Cardiff, CF10 3AT, UK
| | - Encarna Guillén-Navarro
- Medical Genetics Section, Department of Pediatrics, Virgen de la Arrixaca University Hospital, IMIB-Arrixaca, University of Murcia, and CIBERER, Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Sigrun Maier-Wohlfart
- Center for Ectodermal Dysplasias and Department of Pediatrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, 91054 Erlangen, Germany
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4
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Joshi RS, Rigau M, García-Prieto CA, Castro de Moura M, Piñeyro D, Moran S, Davalos V, Carrión P, Ferrando-Bernal M, Olalde I, Lalueza-Fox C, Navarro A, Fernández-Tena C, Aspandi D, Sukno FM, Binefa X, Valencia A, Esteller M. Look-alike humans identified by facial recognition algorithms show genetic similarities. Cell Rep 2022; 40:111257. [PMID: 36001980 DOI: 10.1016/j.celrep.2022.111257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 06/05/2022] [Accepted: 08/01/2022] [Indexed: 11/03/2022] Open
Abstract
The human face is one of the most visible features of our unique identity as individuals. Interestingly, monozygotic twins share almost identical facial traits and the same DNA sequence but could exhibit differences in other biometrical parameters. The expansion of the world wide web and the possibility to exchange pictures of humans across the planet has increased the number of people identified online as virtual twins or doubles that are not family related. Herein, we have characterized in detail a set of "look-alike" humans, defined by facial recognition algorithms, for their multiomics landscape. We report that these individuals share similar genotypes and differ in their DNA methylation and microbiome landscape. These results not only provide insights about the genetics that determine our face but also might have implications for the establishment of other human anthropometric properties and even personality characteristics.
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Affiliation(s)
- Ricky S Joshi
- Josep Carreras Leukaemia Research Institute (IJC), Badalona, 08916 Barcelona, Spain
| | - Maria Rigau
- Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain
| | - Carlos A García-Prieto
- Josep Carreras Leukaemia Research Institute (IJC), Badalona, 08916 Barcelona, Spain; Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain
| | | | - David Piñeyro
- Josep Carreras Leukaemia Research Institute (IJC), Badalona, 08916 Barcelona, Spain; Centro de Investigacion Biomedica en Red Cancer (CIBERONC), 28029 Madrid, Spain
| | - Sebastian Moran
- Josep Carreras Leukaemia Research Institute (IJC), Badalona, 08916 Barcelona, Spain
| | - Veronica Davalos
- Josep Carreras Leukaemia Research Institute (IJC), Badalona, 08916 Barcelona, Spain
| | - Pablo Carrión
- Institute of Evolutionary Biology (CSIC-Universitat Pompeu Fabra), 08003 Barcelona, Spain
| | - Manuel Ferrando-Bernal
- Institute of Evolutionary Biology (CSIC-Universitat Pompeu Fabra), 08003 Barcelona, Spain
| | - Iñigo Olalde
- Institute of Evolutionary Biology (CSIC-Universitat Pompeu Fabra), 08003 Barcelona, Spain
| | - Carles Lalueza-Fox
- Institute of Evolutionary Biology (CSIC-Universitat Pompeu Fabra), 08003 Barcelona, Spain
| | - Arcadi Navarro
- Institute of Evolutionary Biology (CSIC-Universitat Pompeu Fabra), 08003 Barcelona, Spain; Centre for Genomic Regulation (CNAG-CRG), 08003 Barcelona, Catalonia, Spain; Institucio Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Spain
| | | | - Decky Aspandi
- Departament de Tecnologies de la Informació i les Comunicaciones (DTIC), Universitat Pompeu Fabra (UPF), 08018 Barcelona, Spain
| | - Federico M Sukno
- Departament de Tecnologies de la Informació i les Comunicaciones (DTIC), Universitat Pompeu Fabra (UPF), 08018 Barcelona, Spain
| | - Xavier Binefa
- Departament de Tecnologies de la Informació i les Comunicaciones (DTIC), Universitat Pompeu Fabra (UPF), 08018 Barcelona, Spain
| | - Alfonso Valencia
- Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain; Institucio Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Spain
| | - Manel Esteller
- Josep Carreras Leukaemia Research Institute (IJC), Badalona, 08916 Barcelona, Spain; Centro de Investigacion Biomedica en Red Cancer (CIBERONC), 28029 Madrid, Spain; Institucio Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Spain; Physiological Sciences Department, School of Medicine and Health Sciences, University of Barcelona (UB), L'Hospitalet, 08907 Barcelona, Spain.
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5
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Review on Facial-Recognition-Based Applications in Disease Diagnosis. Bioengineering (Basel) 2022; 9:bioengineering9070273. [PMID: 35877324 PMCID: PMC9311612 DOI: 10.3390/bioengineering9070273] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 06/13/2022] [Accepted: 06/20/2022] [Indexed: 01/19/2023] Open
Abstract
Diseases not only manifest as internal structural and functional abnormalities, but also have facial characteristics and appearance deformities. Specific facial phenotypes are potential diagnostic markers, especially for endocrine and metabolic syndromes, genetic disorders, facial neuromuscular diseases, etc. The technology of facial recognition (FR) has been developed for more than a half century, but research in automated identification applied in clinical medicine has exploded only in the last decade. Artificial-intelligence-based FR has been found to have superior performance in diagnosis of diseases. This interdisciplinary field is promising for the optimization of the screening and diagnosis process and assisting in clinical evaluation and decision-making. However, only a few instances have been translated to practical use, and there is need of an overview for integration and future perspectives. This review mainly focuses on the leading edge of technology and applications in varieties of disease, and discusses implications for further exploration.
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6
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Attallah O. A deep learning-based diagnostic tool for identifying various diseases via facial images. Digit Health 2022; 8:20552076221124432. [PMID: 36105626 PMCID: PMC9465585 DOI: 10.1177/20552076221124432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 08/18/2022] [Indexed: 11/16/2022] Open
Abstract
With the current health crisis caused by the COVID-19 pandemic, patients have
become more anxious about infection, so they prefer not to have direct contact
with doctors or clinicians. Lately, medical scientists have confirmed that
several diseases exhibit corresponding specific features on the face the face.
Recent studies have indicated that computer-aided facial diagnosis can be a
promising tool for the automatic diagnosis and screening of diseases from facial
images. However, few of these studies used deep learning (DL) techniques. Most
of them focused on detecting a single disease, using handcrafted feature
extraction methods and conventional machine learning techniques based on
individual classifiers trained on small and private datasets using images taken
from a controlled environment. This study proposes a novel computer-aided facial
diagnosis system called FaceDisNet that uses a new public dataset based on
images taken from an unconstrained environment and could be employed for
forthcoming comparisons. It detects single and multiple diseases. FaceDisNet is
constructed by integrating several spatial deep features from convolutional
neural networks of various architectures. It does not depend only on spatial
features but also extracts spatial-spectral features. FaceDisNet searches for
the fused spatial-spectral feature set that has the greatest impact on the
classification. It employs two feature selection techniques to reduce the large
dimension of features resulting from feature fusion. Finally, it builds an
ensemble classifier based on stacking to perform classification. The performance
of FaceDisNet verifies its ability to diagnose single and multiple diseases.
FaceDisNet achieved a maximum accuracy of 98.57% and 98% after the ensemble
classification and feature selection steps for binary and multiclass
classification categories. These results prove that FaceDisNet is a reliable
tool and could be employed to avoid the difficulties and complications of manual
diagnosis. Also, it can help physicians achieve accurate diagnoses without the
need for physical contact with the patients.
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Affiliation(s)
- Omneya Attallah
- Department of Electronics and Communications Engineering, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria, Egypt
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7
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Rouxel F, Yauy K, Boursier G, Gatinois V, Barat-Houari M, Sanchez E, Lacombe D, Arpin S, Giuliano F, Haye D, Rio M, Toutain A, Dieterich K, Brischoux-Boucher E, Julia S, Nizon M, Afenjar A, Keren B, Jacquette A, Moutton S, Jacquemont ML, Duflos C, Capri Y, Amiel J, Blanchet P, Lyonnet S, Sanlaville D, Genevieve D. Using deep-neural-network-driven facial recognition to identify distinct Kabuki syndrome 1 and 2 gestalt. Eur J Hum Genet 2021; 30:682-686. [PMID: 34803161 DOI: 10.1038/s41431-021-00994-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 10/06/2021] [Accepted: 10/25/2021] [Indexed: 11/09/2022] Open
Abstract
Kabuki syndrome (KS) is a rare genetic disorder caused by mutations in two major genes, KMT2D and KDM6A, that are responsible for Kabuki syndrome 1 (KS1, OMIM147920) and Kabuki syndrome 2 (KS2, OMIM300867), respectively. We lack a description of clinical signs to distinguish KS1 and KS2. We used facial morphology analysis to detect any facial morphological differences between the two KS types. We used a facial-recognition algorithm to explore any facial morphologic differences between the two types of KS. We compared several image series of KS1 and KS2 individuals, then compared images of those of Caucasian origin only (12 individuals for each gene) because this was the main ethnicity in this series. We also collected 32 images from the literature to amass a large series. We externally validated results obtained by the algorithm with evaluations by trained clinical geneticists using the same set of pictures. Use of the algorithm revealed a statistically significant difference between each group for our series of images, demonstrating a different facial morphotype between KS1 and KS2 individuals (mean area under the receiver operating characteristic curve = 0.85 [p = 0.027] between KS1 and KS2). The algorithm was better at discriminating between the two types of KS with images from our series than those from the literature (p = 0.0007). Clinical geneticists trained to distinguished KS1 and KS2 significantly recognised a unique facial morphotype, which validated algorithm findings (p = 1.6e-11). Our deep-neural-network-driven facial-recognition algorithm can reveal specific composite gestalt images for KS1 and KS2 individuals.
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Affiliation(s)
- Flavien Rouxel
- Montpellier University, Département de Génétique Médicale, Maladies Rares et Médecine Personnalisée, Génétique clinique, CHU Montpellier, Centre de référence anomalies du développement SOOR, INSERM U1183, Montpellier, France
| | - Kevin Yauy
- Montpellier University, Département de Génétique Médicale, Maladies Rares et Médecine Personnalisée, Génétique clinique, CHU Montpellier, Centre de référence anomalies du développement SOOR, INSERM U1183, Montpellier, France
| | - Guilaine Boursier
- Département de Génétique Médicale, Maladies Rares et Médecine Personnalisée, Génétique des Maladies Rares et Auto-inflammatoires, CHU Montpellier, Université de Montpellier, Montpellier, France
| | - Vincent Gatinois
- Département de Génétique Médicale, Maladies Rares et Médecine Personnalisée, laboratoire de génétique chromosomique, CHU Montpellier, Université de Montpellier, Montpellier, France
| | - Mouna Barat-Houari
- Département de Génétique Médicale, Maladies Rares et Médecine Personnalisée, Génétique des Maladies Rares et Auto-inflammatoires, CHU Montpellier, Université de Montpellier, Montpellier, France
| | - Elodie Sanchez
- Montpellier University, Département de Génétique Médicale, Maladies Rares et Médecine Personnalisée, Génétique clinique, CHU Montpellier, Centre de référence anomalies du développement SOOR, INSERM U1183, Montpellier, France
| | - Didier Lacombe
- Service de génétique médicale, Centre de référence anomalies du développement SOOR, CHU Bordeaux, INSERM U1211, Université de Bordeaux, Bordeaux, France
| | - Stéphanie Arpin
- Service de Génétique, CHU Tours, UMR 1253, iBrain, Université de Tours, Inserm, Tours, France
| | - Fabienne Giuliano
- Service de Médecine Génétique, CHUV, Université de Lausanne, Lausanne, France
| | - Damien Haye
- Génétique médicale, Hôpital Robert Debré, APHP, Paris, France.,Génétique médicale, Hôpital Pitié-Salpétrière, APHP, Paris, France
| | - Marlène Rio
- Fédération de génétique, et Institut Imagine, UMR-1163, Hôpital Universitaire Necker-Enfants Malades, APHP, Paris, France
| | - Annick Toutain
- Service de Génétique, CHU Tours, UMR 1253, iBrain, Université de Tours, Inserm, Tours, France
| | - Klaus Dieterich
- Service de Génétique Médicale, CHU Grenoble Alpes, Univ. Grenoble Alpes, Inserm, U1216, GIN, 38000, Grenoble, France
| | | | - Sophie Julia
- Service de génétique clinique, CHU Toulouse, Toulouse, France
| | - Mathilde Nizon
- CHU Nantes, Service de Génétique Médicale, 9 quai Moncousu, 44093, Nantes, CEDEX 1, France
| | - Alexandra Afenjar
- APHP, Département de génétique, Sorbonne Université, GRC n°19, ConCer-LD, Centre de Référence déficiences intellectuelles de causes rares, Hôpital Armand Trousseau, F-75012, Paris, France
| | - Boris Keren
- Génétique médicale, Hôpital Pitié-Salpétrière, APHP, Paris, France
| | | | - Sebastien Moutton
- Centre Pluridisciplinaire de Diagnostic PréNatal, Pôle mère enfant, Maison de Santé Protestante Bordeaux Bagatelle, 33400, Talence, France
| | | | - Claire Duflos
- Département d'information médicale, CHU de Montpellier, Montpellier, France
| | - Yline Capri
- Génétique médicale, Hôpital Robert Debré, APHP, Paris, France
| | - Jeanne Amiel
- Fédération de génétique, et Institut Imagine, UMR-1163, Hôpital Universitaire Necker-Enfants Malades, APHP, Paris, France
| | - Patricia Blanchet
- Montpellier University, Département de Génétique Médicale, Maladies Rares et Médecine Personnalisée, Génétique clinique, CHU Montpellier, Centre de référence anomalies du développement SOOR, INSERM U1183, Montpellier, France
| | - Stanislas Lyonnet
- Fédération de génétique, et Institut Imagine, UMR-1163, Hôpital Universitaire Necker-Enfants Malades, APHP, Paris, France
| | | | - David Genevieve
- Montpellier University, Département de Génétique Médicale, Maladies Rares et Médecine Personnalisée, Génétique clinique, CHU Montpellier, Centre de référence anomalies du développement SOOR, INSERM U1183, Montpellier, France.
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8
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Liu P, Gong M, Li J, Baynam G, Zhu W, Zhu Y, Chen L, Gu W, Zhang S. Innovation in Informatics to Improve Clinical Care and Drug Accessibility for Rare Diseases in China. Front Pharmacol 2021; 12:719415. [PMID: 34721018 PMCID: PMC8553959 DOI: 10.3389/fphar.2021.719415] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 09/03/2021] [Indexed: 12/23/2022] Open
Abstract
Background: In China, there are severe unmet medical needs of people living with rare diseases. Relatedly, there is a dearth of data to inform rare diseases policy. This is historically partially due to the lack of informatics infrastructure, including standards and terminology, data sharing mechanisms and network; and concerns over patient privacy protection. Objective: This study aims to introduce the progress of China's rare disease informatics platform and knowledgebase, and to discuss critical enablers of rare disease informatics innovation, including: data standardization; knowledgebase construction; national policy support; and multi-stakeholder participation. Methods: A systemic national strategy, delivered through multi-stakeholder engagement, has been implemented to create and accelerate the informatics infrastructure to support rare diseases management. This includes a disease registry system, together with more than 80 hospitals, to perform comprehensive research information collection, including clinical, genomic and bio-sample data. And a case reporting system, with a network of 324 hospitals, covering all mainland Chinese provinces, to further support reporting of rare diseases data. International standards were incorporated, and privacy issues were addressed through HIPAA compliant rules. Results: The National Rare Diseases Registry System of China (NRDRS) now covers 166 rare diseases and more than 63,000 registered patients. The National Rare Diseases Case Reporting System of China (NRDCRS) was primarily founded on the National Network of Rare Diseases (NNRD) of 324 hospitals and focused on real-time rare diseases case reporting; more than 400,000 cases have been reported. Based on the data available in the two systems, the National Center for Health Technology Assessment (HTA) of Orphan Medicinal Products (OMP) has been established and the expert consensus on HTA of OMP was produced. The largest knowledgebase for rare disease in Chinese has also been developed. Conclusion: A national strategy and the coordinating mechanism is the key to success in the improvement of Chinese rare disease clinical care and drug accessibility. Application of innovative informatics solutions can help accelerate the process, improve quality and increase efficiency.
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Affiliation(s)
- Peng Liu
- State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Mengchun Gong
- Institute of Health Management, Southern Medical University, Guangzhou, China
| | - Jie Li
- Digital Health China Technologies Co., LTD, Beijing, China
| | - Gareth Baynam
- Western Australian Register of Developmental Anomalies, King Edward Memorial Hospital, Perth, WA, Australia.,Division of Paediatrics and Telethon Kids Institute, Faculty of Health and Medical Sciences, Perth, WA, Australia
| | - Weiguo Zhu
- State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Yicheng Zhu
- State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Limeng Chen
- State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Weihong Gu
- State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Shuyang Zhang
- State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
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9
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Happy Cow or Thinking Pig? WUR Wolf—Facial Coding Platform for Measuring Emotions in Farm Animals. AI 2021. [DOI: 10.3390/ai2030021] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Emotions play an indicative and informative role in the investigation of farm animal behaviors. Systems that respond and can measure emotions provide a natural user interface in enabling the digitalization of animal welfare platforms. The faces of farm animals can be one of the richest channels for expressing emotions. WUR Wolf (Wageningen University & Research: Wolf Mascot), a real-time facial recognition platform that can automatically code the emotions of farm animals, is presented in this study. The developed Python-based algorithms detect and track the facial features of cows and pigs, analyze the appearance, ear postures, and eye white regions, and correlate these with the mental/emotional states of the farm animals. The system is trained on a dataset of facial features of images of farm animals collected in over six farms and has been optimized to operate with an average accuracy of 85%. From these, the emotional states of animals in real time are determined. The software detects 13 facial actions and an inferred nine emotional states, including whether the animal is aggressive, calm, or neutral. A real-time emotion recognition system based on YoloV3, a Faster YoloV4-based facial detection platform and an ensemble Convolutional Neural Networks (RCNN) is presented. Detecting facial features of farm animals simultaneously in real time enables many new interfaces for automated decision-making tools for livestock farmers. Emotion sensing offers a vast potential for improving animal welfare and animal–human interactions.
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10
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Abstract
Novel gene-disease discoveries, rapid advancements in technology, and improved bioinformatics tools all have the potential to yield additional molecular diagnoses through the reanalysis of exome sequencing data. Collaborations between clinical laboratories, ordering physicians, and researchers are also driving factors that can contribute to these new insights. Automation in ongoing natural history collection, evolving phenotype updates, advancements in processing next-generation sequencing data, and up-to-date variant-gene-disease databases are increasingly needed for systematic exome reanalysis. Here, we review some of the advantages and challenges for clinician-initiated and laboratory-initiated exome reanalysis, and we propose a model for the future that could potentially maximize the clinical utility of exome reanalysis by integrating information from electronic medical records and knowledge databases into routine clinical workflows.
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11
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Pan Z, Shen Z, Zhu H, Bao Y, Liang S, Wang S, Li X, Niu L, Dong X, Shang X, Chen S, Pan H, Xiong G. Clinical application of an automatic facial recognition system based on deep learning for diagnosis of Turner syndrome. Endocrine 2021; 72:865-873. [PMID: 33170449 DOI: 10.1007/s12020-020-02539-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Accepted: 10/26/2020] [Indexed: 12/18/2022]
Abstract
PURPOSE Automated facial recognition technology based on deep learning has achieved high accuracy in diagnosing various endocrine diseases and genetic syndromes. This study attempts to establish a facial diagnostic system for Turner syndrome (TS) based on deep convolutional neural networks. METHODS Photographs of 207 TS patients and 1074 female controls were collected from July 2016 to April 2019. Finally, 170 patients diagnosed with TS and 1053 female controls were included. Deep convolutional neural networks were used to develop the facial diagnostic system. A prospective study, which included two TS patients and 35 controls, was conducted to test the efficacy in the real clinical setting. RESULTS The average areas under the curve (AUCs) in three different scenarios were 0.9540 ± 0.0223, 0.9662 ± 0.0108 and 0.9557 ± 0.0119, separately. The average sensitivity and specificity of the prospective study were 96.7% and 97.0%, respectively. CONCLUSIONS The facial diagnostic system achieved high accuracy. Prospective study results demonstrated the application value of this system, which is promising in the screening of Turner syndrome.
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Affiliation(s)
- Zhouxian Pan
- Department of Allergy, Peking Union Medical College Hospital (PUMCH), Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC), 100730, Beijing, China
| | - Zhen Shen
- State Key Laboratory for Management and Control of Complex Systems, Beijing Engineering Research Center of Intelligent Systems and Technology, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
- Qingdao Academy of Intelligent Industries, 266109, Qingdao, China
| | - Huijuan Zhu
- Department of Endocrinology, Endocrine Key Laboratory of Ministry of Health, PUMCH, CAMS & PUMC, 100730, Beijing, China
| | - Yin Bao
- State Key Laboratory for Management and Control of Complex Systems, Beijing Engineering Research Center of Intelligent Systems and Technology, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
- School of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK
| | - Siyu Liang
- Department of Endocrinology, Endocrine Key Laboratory of Ministry of Health, PUMCH, CAMS & PUMC, 100730, Beijing, China
| | - Shirui Wang
- Department of Endocrinology, Endocrine Key Laboratory of Ministry of Health, PUMCH, CAMS & PUMC, 100730, Beijing, China
| | - Xiangying Li
- Department of Endocrinology, Endocrine Key Laboratory of Ministry of Health, PUMCH, CAMS & PUMC, 100730, Beijing, China
| | - Lulu Niu
- State Key Laboratory for Management and Control of Complex Systems, Beijing Engineering Research Center of Intelligent Systems and Technology, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, 101408, Beijing, China
| | - Xisong Dong
- State Key Laboratory for Management and Control of Complex Systems, Beijing Engineering Research Center of Intelligent Systems and Technology, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Xiuqin Shang
- State Key Laboratory for Management and Control of Complex Systems, Beijing Engineering Research Center of Intelligent Systems and Technology, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Shi Chen
- Department of Endocrinology, Endocrine Key Laboratory of Ministry of Health, PUMCH, CAMS & PUMC, 100730, Beijing, China.
| | - Hui Pan
- Department of Endocrinology, Endocrine Key Laboratory of Ministry of Health, PUMCH, CAMS & PUMC, 100730, Beijing, China.
| | - Gang Xiong
- State Key Laboratory for Management and Control of Complex Systems, Beijing Engineering Research Center of Intelligent Systems and Technology, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.
- Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing, Cloud Computing Center, Chinese Academy of Sciences, 523808, Dongguan, China.
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12
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Pantel JT, Hajjir N, Danyel M, Elsner J, Abad-Perez AT, Hansen P, Mundlos S, Spielmann M, Horn D, Ott CE, Mensah MA. Efficiency of Computer-Aided Facial Phenotyping (DeepGestalt) in Individuals With and Without a Genetic Syndrome: Diagnostic Accuracy Study. J Med Internet Res 2020; 22:e19263. [PMID: 33090109 PMCID: PMC7644377 DOI: 10.2196/19263] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 06/26/2020] [Accepted: 07/26/2020] [Indexed: 12/11/2022] Open
Abstract
Background Collectively, an estimated 5% of the population have a genetic disease. Many of them feature characteristics that can be detected by facial phenotyping. Face2Gene CLINIC is an online app for facial phenotyping of patients with genetic syndromes. DeepGestalt, the neural network driving Face2Gene, automatically prioritizes syndrome suggestions based on ordinary patient photographs, potentially improving the diagnostic process. Hitherto, studies on DeepGestalt’s quality highlighted its sensitivity in syndromic patients. However, determining the accuracy of a diagnostic methodology also requires testing of negative controls. Objective The aim of this study was to evaluate DeepGestalt's accuracy with photos of individuals with and without a genetic syndrome. Moreover, we aimed to propose a machine learning–based framework for the automated differentiation of DeepGestalt’s output on such images. Methods Frontal facial images of individuals with a diagnosis of a genetic syndrome (established clinically or molecularly) from a convenience sample were reanalyzed. Each photo was matched by age, sex, and ethnicity to a picture featuring an individual without a genetic syndrome. Absence of a facial gestalt suggestive of a genetic syndrome was determined by physicians working in medical genetics. Photos were selected from online reports or were taken by us for the purpose of this study. Facial phenotype was analyzed by DeepGestalt version 19.1.7, accessed via Face2Gene CLINIC. Furthermore, we designed linear support vector machines (SVMs) using Python 3.7 to automatically differentiate between the 2 classes of photographs based on DeepGestalt's result lists. Results We included photos of 323 patients diagnosed with 17 different genetic syndromes and matched those with an equal number of facial images without a genetic syndrome, analyzing a total of 646 pictures. We confirm DeepGestalt’s high sensitivity (top 10 sensitivity: 295/323, 91%). DeepGestalt’s syndrome suggestions in individuals without a craniofacially dysmorphic syndrome followed a nonrandom distribution. A total of 17 syndromes appeared in the top 30 suggestions of more than 50% of nondysmorphic images. DeepGestalt’s top scores differed between the syndromic and control images (area under the receiver operating characteristic [AUROC] curve 0.72, 95% CI 0.68-0.76; P<.001). A linear SVM running on DeepGestalt’s result vectors showed stronger differences (AUROC 0.89, 95% CI 0.87-0.92; P<.001). Conclusions DeepGestalt fairly separates images of individuals with and without a genetic syndrome. This separation can be significantly improved by SVMs running on top of DeepGestalt, thus supporting the diagnostic process of patients with a genetic syndrome. Our findings facilitate the critical interpretation of DeepGestalt’s results and may help enhance it and similar computer-aided facial phenotyping tools.
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Affiliation(s)
- Jean Tori Pantel
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.,Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Nurulhuda Hajjir
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.,Klinik für Pädiatrie mit Schwerpunkt Gastroenterologie, Nephrologie und Stoffwechselmedizin, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Magdalena Danyel
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.,Berlin Center for Rare Diseases, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Jonas Elsner
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Angela Teresa Abad-Perez
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Peter Hansen
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.,The Jackson Laboratory for Genomic Medicine, Farmington, CT, United States
| | - Stefan Mundlos
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.,RG Development & Disease, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Malte Spielmann
- RG Development & Disease, Max Planck Institute for Molecular Genetics, Berlin, Germany.,Institute of Human Genetics, University of Lübeck, Lübeck, Germany
| | - Denise Horn
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Claus-Eric Ott
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Martin Atta Mensah
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.,Berlin Institute of Health, Berlin, Germany
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13
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Qin B, Quan Q, Wu J, Liang L, Li D. Diagnostic performance of artificial intelligence to detect genetic diseases with facial phenotypes: A protocol for systematic review and meta analysis. Medicine (Baltimore) 2020; 99:e20989. [PMID: 32629715 PMCID: PMC7337515 DOI: 10.1097/md.0000000000020989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Many genetic diseases are known to have distinctive facial phenotypes, which are highly informative to provide an opportunity for automated detection. However, the diagnostic performance of artificial intelligence to identify genetic diseases with facial phenotypes requires further investigation. The objectives of this systematic review and meta-analysis are to evaluate the diagnostic accuracy of artificial intelligence to identify the genetic diseases with face phenotypes and then find the best algorithm. METHODS The systematic review will be conducted in accordance with the "Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols" guidelines. The following electronic databases will be searched: PubMed, Web of Science, IEEE, Ovid, Cochrane Library, EMBASE and China National Knowledge Infrastructure. Two reviewers will screen and select the titles and abstracts of the studies retrieved independently during the database searches and perform full-text reviews and extract available data. The main outcome measures include diagnostic accuracy, as defined by accuracy, recall, specificity, and precision. The descriptive forest plot and summary receiver operating characteristic curves will be used to represent the performance of diagnostic tests. Subgroup analysis will be performed for different algorithms aided diagnosis tests. The quality of study characteristics and methodology will be assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Data will be synthesized by RevMan 5.3 and Meta-disc 1.4 software. RESULTS The findings of this systematic review and meta-analysis will be disseminated in a relevant peer-reviewed journal and academic presentations. CONCLUSION To our knowledge, there have not been any systematic review or meta-analysis relating to diagnosis performance of artificial intelligence in identifying the genetic diseases with face phenotypes. The findings would provide evidence to formulate a comprehensive understanding of applications using artificial intelligence in identifying the genetic diseases with face phenotypes and add considerable value in the future of precision medicine. OSF REGISTRATION DOI 10.17605/OSF.IO/P9KUH.
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Affiliation(s)
- Bosheng Qin
- College of Information Science and Electronic Engineering
| | - Qiyao Quan
- College of Computer Science and Technology
| | | | - Letian Liang
- College of Electrical Engineering, Zhejiang University, Hangzhou, China
| | - Dongxiao Li
- College of Information Science and Electronic Engineering
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14
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Gomez DA, Bird LM, Fleischer N, Abdul-Rahman OA. Differentiating molecular etiologies of Angelman syndrome through facial phenotyping using deep learning. Am J Med Genet A 2020; 182:2021-2026. [PMID: 32524756 DOI: 10.1002/ajmg.a.61720] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 04/20/2020] [Accepted: 05/17/2020] [Indexed: 11/08/2022]
Abstract
Angelman syndrome (AS) is caused by several genetic mechanisms that impair the expression of maternally-inherited UBE3A through deletions, paternal uniparental disomy (UPD), UBE3A pathogenic variants, or imprinting defects. Current methods of differentiating the etiology require molecular testing, which is sometimes difficult to obtain. Recently, computer-based facial analysis systems have been used to assist in identifying genetic conditions based on facial phenotypes. We sought to understand if the facial-recognition system DeepGestalt could find differences in phenotype between molecular subtypes of AS. Images and molecular data on 261 individuals with AS ranging from 10 months through 32 years were analyzed by DeepGestalt in a cross-validation model with receiver operating characteristic (ROC) curves generated. The area under the curve (AUC) of the ROC for each molecular subtype was compared and ranked from least to greatest differentiable phenotype. We determined that DeepGestalt demonstrated a high degree of discrimination between the deletion subtype and UPD or imprinting defects, and a lower degree of discrimination with the UBE3A pathogenic variants subtype. Our findings suggest that DeepGestalt can recognize subclinical differences in phenotype based on etiology and may provide decision support for testing.
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Affiliation(s)
- Diego A Gomez
- College of Arts and Sciences, Creighton University, Omaha, Nebraska, USA
| | - Lynne M Bird
- Department of Pediatrics, University of California San Diego, San Diego, California, USA.,Division of Genetics/Dysmorphology, Rady Children's Hospital San Diego, San Diego, California, USA
| | | | - Omar A Abdul-Rahman
- Department of Genetic Medicine, Munroe-Meyer Institute, University of Nebraska Medical Center, Omaha, Nebraska, USA
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15
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Pode-Shakked B, Finezilber Y, Levi Y, Liber S, Fleischer N, Greenbaum L, Raas-Rothschild A. Shared facial phenotype of patients with mucolipidosis type IV: A clinical observation reaffirmed by next generation phenotyping. Eur J Med Genet 2020; 63:103927. [PMID: 32298796 DOI: 10.1016/j.ejmg.2020.103927] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Revised: 04/03/2020] [Accepted: 04/11/2020] [Indexed: 10/24/2022]
Abstract
BACKGROUND Mucolipidosis type IV (ML-IV) is a rare autosomal-recessive lysosomal storage disease, caused by mutations in MCOLN1. ML-IV manifests with developmental delay, esotropia and corneal clouding. While the clinical phenotype is well-described, the diagnosis of ML-IV is often challenging and elusive. OBJECTIVE Our experience with ML-IV patients brought to the clinical observation that they share common and identifiable facial features, not yet described in the literature to date. Here, we utilized a computerized facial analysis tool to establish this association. METHODS Using the DeepGestalt algorithm, 50 two-dimensional facial images of ten ML-IV patients were analyzed, and compared to unaffected controls (n = 98) and to individuals affected with other genetic disorders (n = 99). Results were expressed in terms of the area-under-the-curve (AUC) of the receiver-operating-characteristic curve (ROC). RESULTS When compared to unaffected cases and to cases diagnosed with syndromes other than ML-IV, the ML-IV cohort showed an AUC of 0.822 (p value < 0.01) and an AUC of 0.885 (p value < 0.001), respectively. CONCLUSIONS We describe recognizable facial features typical in patients with ML-IV. Reaffirmed by the DeepGestalt technology, the described common facial phenotype adds to the tools currently available for clinicians and may thus assist in reaching an earlier diagnosis of this rare and underdiagnosed disorder.
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Affiliation(s)
- Ben Pode-Shakked
- The Institute for Rare Diseases, The Edmond and Lily Safra Children's Hospital, Sheba Medical Center, Tel-Hashomer, Israel; The Talpiot Medical Leadership Program, Sheba Medical Center, Tel-Hashomer, Israel; Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Yael Finezilber
- The Danek Gertner Institute of Human Genetics, Sheba Medical Center, Tel-Hashomer, Israel; Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Yonit Levi
- The Institute for Rare Diseases, The Edmond and Lily Safra Children's Hospital, Sheba Medical Center, Tel-Hashomer, Israel
| | - Shiri Liber
- The Institute for Rare Diseases, The Edmond and Lily Safra Children's Hospital, Sheba Medical Center, Tel-Hashomer, Israel; Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | | | - Lior Greenbaum
- The Danek Gertner Institute of Human Genetics, Sheba Medical Center, Tel-Hashomer, Israel; The Joseph Sagol Neuroscience Center, Sheba Medical Center, Tel-Hashomer, Israel; Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Annick Raas-Rothschild
- The Institute for Rare Diseases, The Edmond and Lily Safra Children's Hospital, Sheba Medical Center, Tel-Hashomer, Israel; Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel.
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16
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Myers L, Anderlid BM, Nordgren A, Lundin K, Kuja-Halkola R, Tammimies K, Bölte S. Clinical versus automated assessments of morphological variants in twins with and without neurodevelopmental disorders. Am J Med Genet A 2020; 182:1177-1189. [PMID: 32162839 DOI: 10.1002/ajmg.a.61545] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Revised: 12/05/2019] [Accepted: 02/14/2020] [Indexed: 12/28/2022]
Abstract
Physical examinations are recommended as part of a comprehensive evaluation for individuals with neurodevelopmental disorders (NDDs), such as autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder. These examinations should include assessment for morphological variants. Previous studies have shown an increase in morphological variants in individuals with NDDs, particularly ASD, and that these variants may be present in greater amounts in individuals with genetic alterations. Unfortunately, assessment for morphological variants can be subjective and time-consuming, and require a high degree of clinical expertise. Therefore, objective, automated methods of morphological assessment are desirable. This study compared the use of Face2Gene, an automated tool to explore facial morphological variants, to clinical consensus assessment, using a cohort of N = 290 twins enriched for NDDs (n = 135 with NDD diagnoses). Agreement between automated and clinical assessments were satisfactory to complete (78.3-100%). In our twin sample, individuals with NDDs did not have greater numbers of facial morphological variants when compared to those with typical development, nor when controlling for shared genetic and environmental factors within twin pairs. Common facial morphological variants in those with and without NDDs were similar and included thick upper lip vermilion, abnormality of the nasal tip, long face, and upslanted palpebral fissure. We conclude that although facial morphological variants can be assessed reliably in NDDs with automated tools like Face2Gene, clinical utility is limited when just exploring the facial region. Therefore, currently, automated assessments may best complement, rather than replace, in-person clinical assessments.
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Affiliation(s)
- Lynnea Myers
- Center of Neurodevelopmental Disorders (KIND), Division of Neuropsychiatry, Centre for Psychiatry Research; Department of Women's and Children's Health, Karolinska Institutet, Stockholm Health Care Services, Stockholm, Sweden
| | - Britt-Marie Anderlid
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.,Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
| | - Ann Nordgren
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.,Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
| | - Karl Lundin
- Center of Neurodevelopmental Disorders (KIND), Division of Neuropsychiatry, Centre for Psychiatry Research; Department of Women's and Children's Health, Karolinska Institutet, Stockholm Health Care Services, Stockholm, Sweden
| | - Ralf Kuja-Halkola
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Kristiina Tammimies
- Center of Neurodevelopmental Disorders (KIND), Division of Neuropsychiatry, Centre for Psychiatry Research; Department of Women's and Children's Health, Karolinska Institutet, Stockholm Health Care Services, Stockholm, Sweden
| | - Sven Bölte
- Center of Neurodevelopmental Disorders (KIND), Division of Neuropsychiatry, Centre for Psychiatry Research; Department of Women's and Children's Health, Karolinska Institutet, Stockholm Health Care Services, Stockholm, Sweden.,Child and Adolescent Psychiatry, Stockholm Health Care Services, Stockholm, Sweden.,Curtin Autism Research Group, School of Occupational Therapy, Social Work and Speech Pathology, Curtin University, Perth, Western Australia
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17
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Abstract
Dysmorphology is the practice of defining the morphologic phenotype of syndromic disorders. Genomic sequencing has advanced our understanding of human variation and molecular dysmorphology has evolved in response to the science of relating embryologic developmental implications of abnormal gene signaling pathways to the resultant phenotypic presentation. Machine learning has enabled the application of deep convoluted neural networks to recognize the comparative likeness of these phenotypes relative to the causal genotype or disrupted gene pathway.
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Affiliation(s)
- Donald Basel
- Department of Pediatrics, Division of Genetics, Medical College of Wisconsin, 9000 West Wisconsin Avenue, MS #716, Milwaukee, WI 53226, USA.
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18
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Bijarnia-Mahay S, Arora V. Next Generation Clinical Practice — It’s Man Versus Artificial Intelligence! Indian Pediatr 2019. [DOI: 10.1007/s13312-019-1671-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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19
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Using facial analysis technology in a typical genetic clinic: experience from 30 individuals from a single institution. J Hum Genet 2019; 64:1243-1245. [PMID: 31551534 DOI: 10.1038/s10038-019-0673-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 08/15/2019] [Accepted: 09/08/2019] [Indexed: 12/13/2022]
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20
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Nellåker C, Alkuraya FS, Baynam G, Bernier RA, Bernier FP, Boulanger V, Brudno M, Brunner HG, Clayton-Smith J, Cogné B, Dawkins HJ, deVries BB, Douzgou S, Dudding-Byth T, Eichler EE, Ferlaino M, Fieggen K, Firth HV, FitzPatrick DR, Gration D, Groza T, Haendel M, Hallowell N, Hamosh A, Hehir-Kwa J, Hitz MP, Hughes M, Kini U, Kleefstra T, Kooy RF, Krawitz P, Küry S, Lees M, Lyon GJ, Lyonnet S, Marcadier JL, Meyn S, Moslerová V, Politei JM, Poulton CC, Raymond FL, Reijnders MR, Robinson PN, Romano C, Rose CM, Sainsbury DC, Schofield L, Sutton VR, Turnovec M, Van Dijck A, Van Esch H, Wilkie AO. Enabling Global Clinical Collaborations on Identifiable Patient Data: The Minerva Initiative. Front Genet 2019; 10:611. [PMID: 31417602 PMCID: PMC6681681 DOI: 10.3389/fgene.2019.00611] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Accepted: 06/12/2019] [Indexed: 01/25/2023] Open
Abstract
The clinical utility of computational phenotyping for both genetic and rare diseases is increasingly appreciated; however, its true potential is yet to be fully realized. Alongside the growing clinical and research availability of sequencing technologies, precise deep and scalable phenotyping is required to serve unmet need in genetic and rare diseases. To improve the lives of individuals affected with rare diseases through deep phenotyping, global big data interrogation is necessary to aid our understanding of disease biology, assist diagnosis, and develop targeted treatment strategies. This includes the application of cutting-edge machine learning methods to image data. As with most digital tools employed in health care, there are ethical and data governance challenges associated with using identifiable personal image data. There are also risks with failing to deliver on the patient benefits of these new technologies, the biggest of which is posed by data siloing. The Minerva Initiative has been designed to enable the public good of deep phenotyping while mitigating these ethical risks. Its open structure, enabling collaboration and data sharing between individuals, clinicians, researchers and private enterprise, is key for delivering precision public health.
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Affiliation(s)
- Christoffer Nellåker
- Nuffield Department of Women’s and Reproductive Health, University of Oxford, Oxford, United Kingdom
- Big Data Institute, University of Oxford, Oxford, United Kingdom
- Institute for Biomedical Engineering, University of Oxford, Oxford, United Kingdom
| | - Fowzan S. Alkuraya
- Department of Genetics, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Gareth Baynam
- Western Australian Register of Developmental Anomalies, and Genetic Services of Western Australia, King Edward Memorial, Subiaco, WA, Australia
- Telethon Kids Institute and School of Paediatrics and Child Health, University of Western Australia, Perth, WA, Australia
- Spatial Sciences, Science and Engineering, Curtin University, Perth, WA, Australia
| | - Raphael A. Bernier
- Department of Psychiatry & Behavioral Science, University of Washington School of Medicine, Seattle, WA, United States
| | | | - Vanessa Boulanger
- National Organization for Rare Disorders, Danbury, CT, United States
| | - Michael Brudno
- Department of Computer Science, University of Toronto and the Hospital for Sick Children, Toronto, Canada
| | - Han G. Brunner
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, Netherlands
| | - Jill Clayton-Smith
- Manchester Centre for Genomic Medicine, Central Manchester University Hospitals NHS Foundation Trust, MAHSC, Saint Mary’s Hospital, Manchester, United Kingdom
| | - Benjamin Cogné
- CHU Nantes, Service de Génétique Médicale, Nantes, France
| | - Hugh J.S. Dawkins
- Office of Population Health Genomics, Public and Aboriginal Health Division, Department of Health Government of Western Australia, Perth, WA, Australia
- Sir Walter Murdoch School of Policy and International Affairs, Murdoch University
- Centre for Population Health Research, Curtin University of Technology, Perth, WA, Australia
| | - Bert B.A. deVries
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, Netherlands
| | - Sofia Douzgou
- Manchester Centre for Genomic Medicine, Central Manchester University Hospitals NHS Foundation Trust, MAHSC, Saint Mary’s Hospital, Manchester, United Kingdom
| | | | - Evan E. Eichler
- Department of Genome Science, University of Washington School of Medicine, Seattle, WA, United States
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, United States
| | - Michael Ferlaino
- Nuffield Department of Women’s and Reproductive Health, University of Oxford, Oxford, United Kingdom
- Big Data Institute, University of Oxford, Oxford, United Kingdom
| | - Karen Fieggen
- Division of Human Genetics, Level 3, Wernher and Beit North, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Observatory, South Africa
| | - Helen V. Firth
- Wellcome Trust Sanger Institute, Hinxton, Cambridge, United Kingdom
| | - David R. FitzPatrick
- MRC Human Genetics Unit, IGMM, University of Edinburgh, Western General Hospital, Edinburgh, United Kingdom
| | - Dylan Gration
- Genetic Services of Western Australia, King Edward Memorial Hospital, Subiaco, WA, Australia
| | - Tudor Groza
- The Garvan Institute, Sydney, NSW, Australia
| | - Melissa Haendel
- Oregon Health & Science University, Portland, OR, United States
| | - Nina Hallowell
- Big Data Institute, University of Oxford, Oxford, United Kingdom
- Wellcome Centre for Ethics and Humanities, University of Oxford, Oxford, United Kingdom
- Ethox Centre, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Ada Hamosh
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Jayne Hehir-Kwa
- Princess Máxima Center for Pediatric Oncology, Utrecht, Netherlands
| | - Marc-Phillip Hitz
- Department of Congenital Heart Disease and Pediatric Cardiology, University Hospital of Schleswig-Holstein–Campus Kiel, Kiel, Germany
| | - Mark Hughes
- Department of Clinical Neurosciences, Western General Hospital, Edinburgh, United Kingdom
| | - Usha Kini
- Oxford Centre for Genomic Medicine, Oxford, United Kingdom
| | - Tjitske Kleefstra
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, Netherlands
| | - R Frank Kooy
- Department of Medical Genetics, University of Antwerp, Antwerp, Belgium
| | - Peter Krawitz
- Institut für Genomische Statistik und Bioinformatik, Universitätsklinikum Bonn, Rheinische-Friedrich-Wilhelms-Universität, Bonn, Germany
| | - Sébastien Küry
- CHU Nantes, Service de Génétique Médicale, Nantes, France
| | - Melissa Lees
- Great Ormond Street Hospital for Children NHS Foundation Trust, London, United Kingdom
| | - Gholson J. Lyon
- George A. Jervis Clinic and Institute for Basic Research in Developmental Disabilities (IBR), Staten Island, NY, United States
| | | | | | - Stephen Meyn
- Department of Computer Science, University of Toronto and the Hospital for Sick Children, Toronto, Canada
| | - Veronika Moslerová
- Department of Biology and Medical Genetics, 2nd Faculty of Medicine, Charles University and University Hospital, Prague, Czechia
| | - Juan M. Politei
- Laboratorio Chamoles, Errores Congénitos del Metabolismo, Buenos Aires, Argentina
| | - Cathryn C. Poulton
- Department of Paediatrics and Neonates, Fiona Stanley Hospital, Perth, WA, Australia
| | - F Lucy Raymond
- CIMR (Wellcome Trust/MRC Building), Cambridge, United Kingdom
| | - Margot R.F. Reijnders
- Department of Clinical Genetics, Maastricht University Medical Center, Maastricht, Netherlands
| | | | | | - Catherine M. Rose
- Victorian Clinical Genetics Service and Murdoch Childrens Research Institute, The Royal Children’s Hospital, Parkville, VIC, Australia
| | - David C.G. Sainsbury
- Northern & Yorkshire Cleft Lip and Palate Service, Royal Victoria Infirmary, Newcastle upon Tyne, United Kingdom
| | - Lyn Schofield
- Genetic Services of Western Australia, King Edward Memorial Hospital, Subiaco, WA, Australia
| | - Vernon R. Sutton
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, United States
| | - Marek Turnovec
- Department of Biology and Medical Genetics, 2nd Faculty of Medicine, Charles University and University Hospital, Prague, Czechia
| | - Anke Van Dijck
- Department of Medical Genetics, University and University Hospital Antwerp, Antwerp, Belgium
| | - Hilde Van Esch
- Center for Human Genetics, University Hospitals Leuven, University of Leuven, Leuven, Belgium
| | - Andrew O.M. Wilkie
- Clinical Genetics Group, MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Headington, Oxford, United Kingdom
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21
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Differentiation of MISSLA and Fanconi anaemia by computer-aided image analysis and presentation of two novel MISSLA siblings. Eur J Hum Genet 2019; 27:1827-1835. [PMID: 31320746 DOI: 10.1038/s41431-019-0469-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Revised: 06/07/2019] [Accepted: 06/25/2019] [Indexed: 01/15/2023] Open
Abstract
Variants in DONSON were recently identified as the cause of microcephaly, short stature, and limb abnormalities syndrome (MISSLA). The clinical spectra of MISSLA and Fanconi anaemia (FA) strongly overlap. For that reason, some MISSLA patients have been clinically diagnosed with FA. Here, we present the clinical data of siblings with MISSLA featuring a novel DONSON variant and summarize the current literature on MISSLA. Additionally, we perform computer-aided image analysis using the DeepGestalt technology to test how distinct the facial features of MISSLA and FA patients are. We show that MISSLA has a specific facial gestalt. Notably, we find that also FA patients feature facial characteristics recognizable by computer-aided image analysis. We conclude that computer-assisted image analysis improves diagnostic precision in both MISSLA and FA.
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22
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Dzobo K, Adotey S, Thomford NE, Dzobo W. Integrating Artificial and Human Intelligence: A Partnership for Responsible Innovation in Biomedical Engineering and Medicine. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2019; 24:247-263. [PMID: 31313972 DOI: 10.1089/omi.2019.0038] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Historically, the term "artificial intelligence" dates to 1956 when it was first used in a conference at Dartmouth College in the US. Since then, the development of artificial intelligence has in part been shaped by the field of neuroscience. By understanding the human brain, scientists have attempted to build new intelligent machines capable of performing complex tasks akin to humans. Indeed, future research into artificial intelligence will continue to benefit from the study of the human brain. While the development of artificial intelligence algorithms has been fast paced, the actual use of most artificial intelligence (AI) algorithms in biomedical engineering and clinical practice is still markedly below its conceivably broader potentials. This is partly because for any algorithm to be incorporated into existing workflows it has to stand the test of scientific validation, clinical and personal utility, application context, and is equitable as well. In this context, there is much to be gained by combining AI and human intelligence (HI). Harnessing Big Data, computing power and storage capacities, and addressing societal issues emergent from algorithm applications, demand deploying HI in tandem with AI. Very few countries, even economically developed states, lack adequate and critical governance frames to best understand and steer the AI innovation trajectories in health care. Drug discovery and translational pharmaceutical research stand to gain from AI technology provided they are also informed by HI. In this expert review, we analyze the ways in which AI applications are likely to traverse the continuum of life from birth to death, and encompassing not only humans but also all animal, plant, and other living organisms that are increasingly touched by AI. Examples of AI applications include digital health, diagnosis of diseases in newborns, remote monitoring of health by smart devices, real-time Big Data analytics for prompt diagnosis of heart attacks, and facial analysis software with consequences on civil liberties. While we underscore the need for integration of AI and HI, we note that AI technology does not have to replace medical specialists or scientists and rather, is in need of such expert HI. Altogether, AI and HI offer synergy for responsible innovation and veritable prospects for improving health care from prevention to diagnosis to therapeutics while unintended consequences of automation emergent from AI and algorithms should be borne in mind on scientific cultures, work force, and society at large.
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Affiliation(s)
- Kevin Dzobo
- International Centre for Genetic Engineering and Biotechnology (ICGEB), Cape Town Component, Wernher and Beit Building (South), UCT Medical Campus, Anzio Road, Observatory 7925, Cape Town, South Africa.,Division of Medical Biochemistry and Institute of Infectious Disease and Molecular Medicine, Department of Integrative Biomedical Sciences, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Sampson Adotey
- International Development Innovation Network, D-Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Nicholas E Thomford
- Pharmacogenetics Research Group, Division of Human Genetics, Department of Pathology and Institute of Infectious Diseases and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Observatory 7925, Cape Town, South Africa
| | - Witness Dzobo
- Pathology and Immunology Department, University Hospital Southampton, Mail Point B, Tremona Road, Southampton, UK.,University of Portsmouth, Faculty of Science, St Michael's Building, White Swan Road, Portsmouth, UK
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23
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Bosio M, Drechsel O, Rahman R, Muyas F, Rabionet R, Bezdan D, Domenech Salgado L, Hor H, Schott JJ, Munell F, Colobran R, Macaya A, Estivill X, Ossowski S. eDiVA-Classification and prioritization of pathogenic variants for clinical diagnostics. Hum Mutat 2019; 40:865-878. [PMID: 31026367 PMCID: PMC6767450 DOI: 10.1002/humu.23772] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Revised: 04/17/2019] [Accepted: 04/24/2019] [Indexed: 01/06/2023]
Abstract
Mendelian diseases have shown to be an and efficient model for connecting genotypes to phenotypes and for elucidating the function of genes. Whole‐exome sequencing (WES) accelerated the study of rare Mendelian diseases in families, allowing for directly pinpointing rare causal mutations in genic regions without the need for linkage analysis. However, the low diagnostic rates of 20–30% reported for multiple WES disease studies point to the need for improved variant pathogenicity classification and causal variant prioritization methods. Here, we present the exome Disease Variant Analysis (eDiVA; http://ediva.crg.eu), an automated computational framework for identification of causal genetic variants (coding/splicing single‐nucleotide variants and small insertions and deletions) for rare diseases using WES of families or parent–child trios. eDiVA combines next‐generation sequencing data analysis, comprehensive functional annotation, and causal variant prioritization optimized for familial genetic disease studies. eDiVA features a machine learning‐based variant pathogenicity predictor combining various genomic and evolutionary signatures. Clinical information, such as disease phenotype or mode of inheritance, is incorporated to improve the precision of the prioritization algorithm. Benchmarking against state‐of‐the‐art competitors demonstrates that eDiVA consistently performed as a good or better than existing approach in terms of detection rate and precision. Moreover, we applied eDiVA to several familial disease cases to demonstrate its clinical applicability.
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Affiliation(s)
- Mattia Bosio
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain.,Universitat Pompeu Fabra (UPF), Barcelona, Spain.,Barcelona Supercomputing Center (BSC), Barcelona, Spain
| | - Oliver Drechsel
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
| | | | - Francesc Muyas
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain.,Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Raquel Rabionet
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain.,Universitat Pompeu Fabra (UPF), Barcelona, Spain.,Institut de Recerca Sant Joan de Déu, University of Barcelona, Barcelona, Spain
| | - Daniela Bezdan
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain.,Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Laura Domenech Salgado
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain.,Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Hyun Hor
- Department of Neurology, University Hospital Zurich, Zurich, Switzerland
| | - Jean-Jacques Schott
- L'Institut du Thorax, INSERM, CNRS, Univ Nantes, Nantes, France.,Service de Cardiologie, L'institut du thorax, CHU Nantes, Nantes, France
| | | | - Roger Colobran
- Vall d'Hebron Institut de Recerca (VHIR), Barcelona, Spain
| | - Alfons Macaya
- Vall d'Hebron Institut de Recerca (VHIR), Barcelona, Spain
| | - Xavier Estivill
- Sidra Medicine, Doha, Qatar.,Women's Health Dexeus, Barcelona, Spain
| | - Stephan Ossowski
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain.,Universitat Pompeu Fabra (UPF), Barcelona, Spain.,Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
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24
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Pascolini G, Fleischer N, Ferraris A, Majore S, Grammatico P. The facial dysmorphology analysis technology in intellectual disability syndromes related to defects in the histones modifiers. J Hum Genet 2019; 64:721-728. [PMID: 31086247 DOI: 10.1038/s10038-019-0598-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Revised: 03/22/2019] [Accepted: 03/25/2019] [Indexed: 12/16/2022]
Abstract
Genetic syndromes are frequently associated with Intellectual Disability (ID), as well as craniofacial dysmorphisms. A group of ID syndromes with typical abnormal face related to chromatin remodeling defects, have been recognized, coining the term chromatinopathies. This is a molecular heterogeneous subset of congenital disorders caused by mutations of the various components of the Chromatin-Marking System (CMS), including modifiers of DNA and chromatin remodelers. We performed a phenotypic study on a sample of 120 individuals harboring variants in genes codifying for the histones enzymes, using the DeepGestalt technology. Three experiments (two multiclass comparison experiments and a frontal face-crop analysis) were conducted, analyzing respectively a total of 181 pediatric images in the first comparison experiment and 180 in the second, all individuals belonging predominantly to Caucasian population. The classification results were expressed in terms of the area under the curve (AUC) of the receiver-operating-characteristic curve (ROC). Significant values of AUC and low p-values were registered for all syndromes in the three experiments, in comparison with each other, with other ID syndromes characterized by recognizable craniofacial dysmorphisms and with unaffected controls. Final findings indicated that this group of diseases is characterized by distinctive dysmorphisms, which result pathognomonic. A correct interrogation and use of adequate informatics aids, could become a valid support for clinicians.
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Affiliation(s)
- Giulia Pascolini
- Medical Genetics Laboratory, Department of Molecular Medicine, Sapienza University, San Camillo-Forlanini Hospital, Rome, Italy.
| | | | - Alessandro Ferraris
- Medical Genetics Laboratory, Department of Molecular Medicine, Sapienza University, San Camillo-Forlanini Hospital, Rome, Italy
| | - Silvia Majore
- Medical Genetics Laboratory, Department of Molecular Medicine, Sapienza University, San Camillo-Forlanini Hospital, Rome, Italy
| | - Paola Grammatico
- Medical Genetics Laboratory, Department of Molecular Medicine, Sapienza University, San Camillo-Forlanini Hospital, Rome, Italy
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25
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Identifying facial phenotypes of genetic disorders using deep learning. Nat Med 2019; 25:60-64. [PMID: 30617323 DOI: 10.1038/s41591-018-0279-0] [Citation(s) in RCA: 341] [Impact Index Per Article: 68.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2017] [Accepted: 10/29/2018] [Indexed: 01/18/2023]
Abstract
Syndromic genetic conditions, in aggregate, affect 8% of the population1. Many syndromes have recognizable facial features2 that are highly informative to clinical geneticists3-5. Recent studies show that facial analysis technologies measured up to the capabilities of expert clinicians in syndrome identification6-9. However, these technologies identified only a few disease phenotypes, limiting their role in clinical settings, where hundreds of diagnoses must be considered. Here we present a facial image analysis framework, DeepGestalt, using computer vision and deep-learning algorithms, that quantifies similarities to hundreds of syndromes. DeepGestalt outperformed clinicians in three initial experiments, two with the goal of distinguishing subjects with a target syndrome from other syndromes, and one of separating different genetic subtypes in Noonan syndrome. On the final experiment reflecting a real clinical setting problem, DeepGestalt achieved 91% top-10 accuracy in identifying the correct syndrome on 502 different images. The model was trained on a dataset of over 17,000 images representing more than 200 syndromes, curated through a community-driven phenotyping platform. DeepGestalt potentially adds considerable value to phenotypic evaluations in clinical genetics, genetic testing, research and precision medicine.
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26
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Abstract
PURPOSE OF REVIEW The current review aims to discuss the incorporation of facial recognition software into the clinical practice of dysmorphology and medical genetics. RECENT FINDINGS Facial recognition software has improved the process of generating a differential diagnosis for rare genetic syndromes, and recent publications demonstrate utility in both research and clinical applications. Software programs are freely available to verified medical providers and can be incorporated into routine clinic encounters. SUMMARY As facial recognition software capabilities improve, two-dimensional image capture with artificial intelligence interpretation may become a useful tool within many areas of medicine. Geneticists and researchers can use such software to enhance their differential diagnoses, to study similarities and differences between patient cohorts, and to improve the interpretation of genomic data. Pediatricians and subspecialists may use tools to identify patients who may benefit from a genetic evaluation, and educators can use these tools to interest students in the study of dysmorphoplogy and genetic syndromes.
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27
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Hammersen J, Wohlfart S, Goecke TW, Köninger A, Stepan H, Gallinat R, Morris S, Bücher K, Clarke A, Wünsche S, Beckmann MW, Schneider H, Faschingbauer F. Reliability of prenatal detection of X-linked hypohidrotic ectodermal dysplasia by tooth germ sonography. Prenat Diagn 2018; 39:796-805. [PMID: 30394555 DOI: 10.1002/pd.5384] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2018] [Revised: 10/11/2018] [Accepted: 10/19/2018] [Indexed: 11/11/2022]
Abstract
OBJECTIVE In X-linked hypohidrotic ectodermal dysplasia (XLHED), dysfunction of ectodysplasin A1 (EDA1) due to EDA mutations results in malformation of hair, teeth, and sweat glands. Hypohidrosis, which can cause life-threatening hyperthermia, is amenable to intrauterine therapy with recombinant EDA1. This study aimed at evaluating tooth germ sonography as a noninvasive means to identify affected fetuses in pregnant carrier women. METHODS Sonography, performed at 10 study sites between gestational weeks 18 and 28, led to the diagnosis of XLHED if fewer than six tooth germs were detected in mandible or maxilla. The assessment was verified postnatally by EDA sequencing and/or clinical findings. Estimated fetal weights and postnatal weight gain of boys with XLHED were assessed using appropriate growth charts. RESULTS In 19 of 38 sonographic examinations (23 male and 13 female fetuses), XLHED was detected prenatally. The prenatal diagnosis proved to be correct in 37 cases; one affected male fetus was missed. Specificity and positive predictive value were both 100%. Tooth counts obtained by clinical examination corresponded well with findings on panoramic radiographs. We observed no weight deficits of subjects with XLHED in utero but occasionally during infancy. CONCLUSION Tooth germ sonography is highly specific and reliable in detecting XLHED prenatally.
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Affiliation(s)
- Johanna Hammersen
- Center for Ectodermal Dysplasias, University Hospital Erlangen, Erlangen, Germany
| | - Sigrun Wohlfart
- Center for Ectodermal Dysplasias, University Hospital Erlangen, Erlangen, Germany
| | - Tamme W Goecke
- Department of Gynecology and Obstetrics, RWTH Aachen, Aachen, Germany
| | - Angela Köninger
- Department of Gynecology and Obstetrics, University of Duisburg-Essen, Essen, Germany
| | - Holger Stepan
- Department of Obstetrics, University Hospital Leipzig, Leipzig, Germany
| | - Ralph Gallinat
- Frauenärztliche Gemeinschaftspraxis Günzburg-Ichenhausen, Günzburg, Germany
| | - Susan Morris
- Cardiff and Vale University Health Board, University Hospital of Wales, Cardiff, UK
| | - Katharina Bücher
- Department of Conservative Dentistry and Periodontology, University Hospital, Ludwig-Maximilians University, Munich, Germany
| | - Angus Clarke
- Cardiff and Vale University Health Board, University Hospital of Wales, Cardiff, UK.,Institute of Medical Genetics, School of Medicine, Cardiff University, Cardiff, UK
| | - Stephanie Wünsche
- Center for Ectodermal Dysplasias, University Hospital Erlangen, Erlangen, Germany
| | - Matthias W Beckmann
- Center for Ectodermal Dysplasias, University Hospital Erlangen, Erlangen, Germany
| | - Holm Schneider
- Center for Ectodermal Dysplasias, University Hospital Erlangen, Erlangen, Germany
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28
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Vorravanpreecha N, Lertboonnum T, Rodjanadit R, Sriplienchan P, Rojnueangnit K. Studying Down syndrome recognition probabilities in Thai children with de‐identified computer‐aided facial analysis. Am J Med Genet A 2018; 176:1935-1940. [DOI: 10.1002/ajmg.a.40483] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Revised: 06/05/2018] [Accepted: 06/28/2018] [Indexed: 01/24/2023]
Affiliation(s)
| | | | | | | | - Kitiwan Rojnueangnit
- Pediatric Department, Faculty of MedicineThammasat University Pathumthani Thailand
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