دورية أكاديمية
أعرض في EDS

Convolutional mesh autoencoders for the 3-dimensional identification of FGFR-related craniosynostosis.

التفاصيل البيبلوغرافية
العنوان: Convolutional mesh autoencoders for the 3-dimensional identification of FGFR-related craniosynostosis.
المؤلفون: O' Sullivan E; UCL Great Ormond Street Institute of Child Health, London, UK.; Assistance Publique - Hôpitaux de Paris, Faculty of Medicine, University of Paris, Paris, France.; Department of Computing, Imperial College London, London, UK., van de Lande LS; UCL Great Ormond Street Institute of Child Health, London, UK. l.lande@ucl.ac.uk.; Assistance Publique - Hôpitaux de Paris, Faculty of Medicine, University of Paris, Paris, France. l.lande@ucl.ac.uk.; Department of Maxillofacial Surgery and Plastic Surgery, Necker - Enfants Malades University Hospital, Paris, France. l.lande@ucl.ac.uk., Papaioannou A; UCL Great Ormond Street Institute of Child Health, London, UK.; Assistance Publique - Hôpitaux de Paris, Faculty of Medicine, University of Paris, Paris, France.; Department of Computing, Imperial College London, London, UK., Breakey RWF; UCL Great Ormond Street Institute of Child Health, London, UK.; Assistance Publique - Hôpitaux de Paris, Faculty of Medicine, University of Paris, Paris, France., Jeelani NO; UCL Great Ormond Street Institute of Child Health, London, UK.; Assistance Publique - Hôpitaux de Paris, Faculty of Medicine, University of Paris, Paris, France., Ponniah A; Department of Plastic Surgery, Royal. Free Hospital, London, UK., Duncan C; Craniofacial Unit, Alder Hey Childrens Hospital, Liverpool, UK., Schievano S; UCL Great Ormond Street Institute of Child Health, London, UK.; Assistance Publique - Hôpitaux de Paris, Faculty of Medicine, University of Paris, Paris, France., Khonsari RH; Department of Maxillofacial Surgery and Plastic Surgery, Necker - Enfants Malades University Hospital, Paris, France., Zafeiriou S; Department of Computing, Imperial College London, London, UK., Dunaway DJ; UCL Great Ormond Street Institute of Child Health, London, UK.; Assistance Publique - Hôpitaux de Paris, Faculty of Medicine, University of Paris, Paris, France.
المصدر: Scientific reports [Sci Rep] 2022 Feb 09; Vol. 12 (1), pp. 2230. Date of Electronic Publication: 2022 Feb 09.
نوع المنشور: Journal Article; Research Support, Non-U.S. Gov't
اللغة: English
بيانات الدورية: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
أسماء مطبوعة: Original Publication: London : Nature Publishing Group, copyright 2011-
مواضيع طبية MeSH: Artificial Intelligence*, Craniosynostoses/*classification , Craniosynostoses/*diagnosis , Image Processing, Computer-Assisted/*methods , Imaging, Three-Dimensional/*methods, Computer Simulation ; Craniosynostoses/diagnostic imaging ; Face/abnormalities ; Head/abnormalities ; Humans ; Infant ; Tomography, X-Ray Computed
مستخلص: Clinical diagnosis of craniofacial anomalies requires expert knowledge. Recent studies have shown that artificial intelligence (AI) based facial analysis can match the diagnostic capabilities of expert clinicians in syndrome identification. In general, these systems use 2D images and analyse texture and colour. They are powerful tools for photographic analysis but are not suitable for use with medical imaging modalities such as ultrasound, MRI or CT, and are unable to take shape information into consideration when making a diagnostic prediction. 3D morphable models (3DMMs), and their recently proposed successors, mesh autoencoders, analyse surface topography rather than texture enabling analysis from photography and all common medical imaging modalities and present an alternative to image-based analysis. We present a craniofacial analysis framework for syndrome identification using Convolutional Mesh Autoencoders (CMAs). The models were trained using 3D photographs of the general population (LSFM and LYHM), computed tomography data (CT) scans from healthy infants and patients with 3 genetically distinct craniofacial syndromes (Muenke, Crouzon, Apert). Machine diagnosis outperformed expert clinical diagnosis with an accuracy of 99.98%, sensitivity of 99.95% and specificity of 100%. The diagnostic precision of this technique supports its potential inclusion in clinical decision support systems. Its reliance on 3D topography characterisation make it suitable for AI assisted diagnosis in medical imaging as well as photographic analysis in the clinical setting.
(© 2022. The Author(s).)
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معلومات مُعتمدة: United Kingdom DH_ Department of Health
تواريخ الأحداث: Date Created: 20220210 Date Completed: 20220304 Latest Revision: 20220913
رمز التحديث: 20231215
مُعرف محوري في PubMed: PMC8828904
DOI: 10.1038/s41598-021-02411-y
PMID: 35140239
قاعدة البيانات: MEDLINE
الوصف
تدمد:2045-2322
DOI:10.1038/s41598-021-02411-y