Quantifying the Severity of Metopic Craniosynostosis: A Pilot Study Application of Machine Learning in Craniofacial Surgery

Bhalodia, Riddhish; Dvoracek, Lucas A.; Ayyash, Ali; Kavan, Ladislav; Whitaker, Ross T.; Goldstein, Jesse A.

doi:10.1097/scs.0000000000006215

Cited by 51 publications

(47 citation statements)

References 21 publications

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“…Both our study and the study of Meulstee et al underline the feasibility of diagnosing craniosynostosis using 3D stereophotogrammetry, a non-invasive technique without the risk of exposing the patient to ionizing radiation. Other machine learning techniques have also shown the potential in the classification of (parts of) craniosynostosis on 3D stereophotographs with additional statistical analysis or preprocessing 19 – 21 .…”

Section: Discussionmentioning

confidence: 99%

Combining deep learning with 3D stereophotogrammetry for craniosynostosis diagnosis

Jong

Bijlsma

Meulstee

et al. 2020

Sci Rep

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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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Section: Discussionmentioning

confidence: 99%

Combining deep learning with 3D stereophotogrammetry for craniosynostosis diagnosis

Jong

Bijlsma

Meulstee

et al. 2020

Sci Rep

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“…The IFA (Fig. 3D) was the most frequently used angle (it was used in 12 studies, 11,17,19,21,[25][26][27][28][29][30][31][32] Supplementary Digital Content, Table 2, http://links.lww.com/SCS/D285). The IFA was introduced in 2012 by Kellogg et al 11 as the angle formed between the anteriormost point of the cranium and the supraorbital notches.…”

Section: Interfrontal Anglementioning

confidence: 99%

Anthropometric Cranial Measurements in Metopic Craniosynostosis/Trigonocephaly: Diagnostic Criteria, Classification of Severity and Indications for Surgery

Sisti

Gharb

Papay

et al. 2021

Journal of Craniofacial Surgery

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Metopic craniosynostosis is the second most frequent type of craniosynostosis. When the phenotypic presentation has been deemed severe the treatment is surgical in nature and is performed in infancy with fronto-orbital advancement and cranial vault remodeling. At the time of this writing, there is no consensus regarding an objective evaluation system for severity, diagnostic criteria, or indications for surgery. This study aims to review the anthropometric cranial measurements and the relative diagnostic criteria/classification of severity/surgical indications proposed so far for this skull malformation, and to investigate if there is any scientific support for their utility.

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“…Finally, a leave-one-out cross-validation model was performed to determine the accuracy of using CMD to predict raters' decision to operate. 3 Significance was set at α= 0.05 for all statistical analysis.…”

Section: Discussionmentioning

confidence: 99%

Quantifying the Severity of Metopic Craniosynostosis Using Unsupervised Machine Learning

Anstadt

Tao

Guo

et al. 2022

Plastic &Amp; Reconstructive Surgery

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Metopic craniosynostosis (MCS) refers to the premature fusion of the metopic cranial suture resulting in trigonocephaly, characterized by a keel-shaped forehead, suture ridging, orbital hypotelorism, retrusion and upsloping of the lateral supraorbital rim, and bitemporal narrowing. 1 Although severe trigonocephaly is pathognomonic for MCS, mild to moderate phenotypes are more difficult to diagnose and classify by severity. There is currently no standard for classification of severity, and variability in management protocols exists. Surgical intervention for mild to moderate phenotypes remains controversial as objective methods to delineate which patients require surgery are lacking. 1 Although operative intervention effectively preserves normal neurocognition, studies have demonstrated a worsening aesthetic result with Background: Quantifying the severity of head shape deformity and establishing a threshold for operative intervention remains challenging in patients with metopic craniosynostosis (MCS). This study combines three-dimensional skull shape analysis with an unsupervised machine-learning algorithm to generate a quantitative shape severity score (cranial morphology deviation) and provide an operative threshold score. Methods: Head computed tomography scans from subjects with MCS and normal controls (5 to 15 months of age) were used for objective three-dimensional shape analysis using ShapeWorks software and in a survey for craniofacial surgeons to rate head-shape deformity and report whether they would offer surgical correction based on head shape alone. An unsupervised machine-learning algorithm was developed to quantify the degree of shape abnormality of MCS skulls compared to controls. Results: One hundred twenty-four computed tomography scans were used to develop the model; 50 (24% MCS, 76% controls) were rated by 36 craniofacial surgeons, with an average of 20.8 ratings per skull. The interrater reliability was high (intraclass correlation coefficient, 0.988). The algorithm performed accurately and correlates closely with the surgeons assigned severity ratings (Spearman correlation coefficient, r = 0.817). The median cranial morphology deviation for affected skulls was 155.0 (interquartile range, 136.4 to 194.6; maximum, 231.3). Skulls with ratings of 150.2 or higher were very likely to be offered surgery by the experts in this study. Conclusions: This study describes a novel metric to quantify the head shape deformity associated with MCS and contextualizes the results using clinical assessments of head shapes by craniofacial experts. This metric may be useful in supporting clinical decision making around operative intervention and in describing outcomes and comparing patient population across centers. (Plast.

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Quantifying the Severity of Metopic Craniosynostosis: A Pilot Study Application of Machine Learning in Craniofacial Surgery

Cited by 51 publications

References 21 publications

Combining deep learning with 3D stereophotogrammetry for craniosynostosis diagnosis

Combining deep learning with 3D stereophotogrammetry for craniosynostosis diagnosis

Anthropometric Cranial Measurements in Metopic Craniosynostosis/Trigonocephaly: Diagnostic Criteria, Classification of Severity and Indications for Surgery

Quantifying the Severity of Metopic Craniosynostosis Using Unsupervised Machine Learning

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