ArticleScientific reports2020
Combining deep learning with 3D stereophotogrammetry for craniosynostosis diagnosis.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
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Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it, 56 citations in OpenAlex.
- Developments in diagnostic and surgical techniques in children with sagittal suture craniosynostosis: a systematic review spanning the last 30 years.Orphanet journal of rare diseases · 2025Pooled it
- Pooled it
- Assessing Medical Student Attentional Bias in Unicoronal Craniosynostosis: An Eye-Tracking Study.The Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association · 2026Article
- Quantifying Sagittal Craniosynostosis Severity: A Machine Learning Approach With CranioRate.The Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association · 2026Article
- Extended Reality in Neurosurgery : Surgical Planning, Navigation, Education, and Patient Communication.Journal of Korean Neurosurgical Society · 2026Article
- Smartphone 3D Scanning Technology and 3D Semi-Synthetic Data for Processing Infant Head Deformities Using Artificial Intelligence.Sensors (Basel, Switzerland) · 2026Article
- Imaging Modalities in Craniosynostosis: A Systematic Review and Proposal of the ARCANA Protocol for Multimodal Radiation-Free Assessment.Diagnostics (Basel, Switzerland) · 2025Review
- Artificial Intelligence Applications in Pediatric Craniofacial Surgery.Diagnostics (Basel, Switzerland) · 2025Review
- Quantifying dysmorphologies of the neurocranium using artificial neural networks.Journal of anatomy · 2024Article
- Convolutional neural network-based classification of craniosynostosis and suture lines from multi-view cranial X-rays.Scientific reports · 2024Article
- Automated condylar seating assessment using a deep learning-based three-step approach.Clinical oral investigations · 2024Article
- Article
- The Use of Artificial Intelligence for the Classification of Craniofacial Deformities.Journal of clinical medicine · 2023Article
- Geometric learning and statistical modeling for surgical outcomes evaluation in craniosynostosis using 3D photogrammetry.Computer methods and programs in biomedicine · 2023Article
- Can Craniosynostosis be Diagnosed on Physical Examination? A Retrospective Review.The Journal of craniofacial surgery · 2023Article
- UCSQ Method Applied on 3D Photogrammetry: Non-Invasive Objective Differentiation Between Synostotic and Positional Plagiocephaly.The Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association · 2023Article
- Review
- Review
- 3D Photography to Quantify the Severity of Metopic Craniosynostosis.The Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association · 2023Article
- Evaluation of the impact of technical physicians on improving individual patient care with technology.BMC medical education · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Craniosynostosis is a condition in which cranial sutures fuse prematurely, causing problems in normal brain and skull growth in infants. To limit the extent of cosmetic and functional problems, swift diagnosis is needed. The goal of this study is to investigate if a deep learning algorithm is capable of correctly classifying the head shape of infants as either healthy controls, or as one of the following three craniosynostosis subtypes; scaphocephaly, trigonocephaly or anterior plagiocephaly. In order to acquire cranial shape data, 3D stereophotographs were made during routine pre-operative appointments of scaphocephaly (n = 76), trigonocephaly (n = 40) and anterior plagiocephaly (n = 27) patients. 3D Stereophotographs of healthy infants (n = 53) were made between the age of 3-6 months. The cranial shape data was sampled and a deep learning network was used to classify the cranial shape data as either: healthy control, scaphocephaly patient, trigonocephaly patient or anterior plagiocephaly patient. For the training and testing of the deep learning network, a stratified tenfold cross validation was used. During testing 195 out of 196 3D stereophotographs (99.5%) were correctly classified. This study shows that trained deep learning algorithms, based on 3D stereophotographs, can discriminate between craniosynostosis subtypes and healthy controls with high accuracy.
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