Cranial Ultrasound as a First-Line Imaging Examination for Craniosynostosis

Rozovsky, Katya; Udjus, K.; Wilson, Nagwa; Barrowman, Nicholas; Simanovsky, Natalia; Miller, Elka

doi:10.1542/peds.2015-2230

Cited by 44 publications

(26 citation statements)

References 33 publications

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“…3D photogrammetry is not the only radiation-free imaging modality that can be used to diagnose craniosynostosis as systematic physical examination, ultrasound and MRIcan also be utilized and have led to good results in the past 7 , 9 , 11 , 32 , 33 . It would be interesting to see if a deep learning algorithm would perform similarly when using images from another modality.…”

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 effectiveness and reliability of US examination in children with suspected craniosynostosis, in comparison with radiography, CT or clinical follow up, has been demonstrated in several studies [4][5][6][7][8][9][10][11][12][13], showing high sensitivity and specificity (Table 1). In addition, the capacity to determine complete or incomplete closure of the sutures also reaches 100% [13].…”

Section: Validity Of the Techniquementioning

confidence: 99%

“…In addition, the capacity to determine complete or incomplete closure of the sutures also reaches 100% [13]. Inconclusive US examination is very infrequent: it may be due to poor cooperation in some studies [10] or to relatively advanced age (12-18 months) [11,12]. The less conclusive results in Krimmel's study [7] can be explained by the inclusion of inconclusive US findings as false-positive and false-negative results in their statistics.…”

Section: Validity Of the Techniquementioning

confidence: 99%

“…However, clinical differentiation between isolated unilateral lambdoid and coronal synostosis can be difficult, especially for general practitioners or pediatricians. Rozovsky et al [10] recommended an algorithm for diagnosis or exclusion of non-syndromic craniosynostosis by pediatricians or general practitioners: when craniosynostosis is clinically suspected, a cranial US examination should be performed first. If negative, there is no need for further imaging studies.…”

Section: Role Of Us In Diagnosis Strategy (Fig4)mentioning

confidence: 99%

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How ultrasonography can contribute to diagnosis of craniosynostosis

Proisy¹,

Bruneau²,

Riffaud

2019

Neurochirurgie

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“…Cranial ultrasonography is used by some, and studies suggest that it is as effective as plain radiographs or CT scans in identifying a fused suture. 73 However, not all radiologists are equally experienced at identifying fused sutures on ultrasonography, so it is recommended that the provider check with the radiologist first before obtaining this study. Many craniofacial teams prefer that providers refer these children early and postpone imaging until after the child is seen by specialists.…”

Section: Surgical Management Of Craniosynostosismentioning

confidence: 99%

Identifying the Misshapen Head: Craniosynostosis and Related Disorders

Dias¹,

Samson²,

Rizk³

et al. 2020

Pediatrics

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Pediatric care providers, pediatricians, pediatric subspecialty physicians, and other health care providers should be able to recognize children with abnormal head shapes that occur as a result of both synostotic and deformational processes. The purpose of this clinical report is to review the characteristic head shape changes, as well as secondary craniofacial characteristics, that occur in the setting of the various primary craniosynostoses and deformations. As an introduction, the physiology and genetics of skull growth as well as the pathophysiology underlying craniosynostosis are reviewed. This is followed by a description of each type of primary craniosynostosis (metopic, unicoronal, bicoronal, sagittal, lambdoid, and frontosphenoidal) and their resultant head shape changes, with an emphasis on differentiating conditions that require surgical correction from those (bathrocephaly, deformational plagiocephaly/brachycephaly, and neonatal intensive care unit-associated skill deformation, known as NICUcephaly) that do not. The report ends with a brief discussion of microcephaly as it relates to craniosynostosis as well as fontanelle closure. The intent is to improve pediatric care providers' recognition and timely referral for craniosynostosis and their differentiation of synostotic from deformational and other nonoperative head shape changes.

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Cranial Ultrasound as a First-Line Imaging Examination for Craniosynostosis

Abstract: BACKGROUND: Radiography, typically the first-line imaging study for diagnosis of craniosynostosis, exposes infants to ionizing radiation. We aimed to compare the accuracy of cranial ultrasound (CUS) with radiography for the diagnosis or exclusion of craniosynostosis.

Cited by 44 publications

References 33 publications

Combining deep learning with 3D stereophotogrammetry for craniosynostosis diagnosis

Combining deep learning with 3D stereophotogrammetry for craniosynostosis diagnosis

How ultrasonography can contribute to diagnosis of craniosynostosis

Identifying the Misshapen Head: Craniosynostosis and Related Disorders

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