The Evolution of the Role of Imaging in the Diagnosis of Craniosynostosis: A Narrative Review

Cacciaguerra, Giuseppe; Palermo, Monica; Marino, Lidia; Rapisarda, Filippo Andrea Salvatore; Pavone, Piero; Falsaperla, Raffaele; Ruggieri, Martino; Marıno, Silvia

doi:10.3390/children8090727

Cited by 11 publications

(7 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most emergency physicians also preferred to learn about RDs at academic conferences, where specialists would share vivid cases of RDs. As learning from clinical work and academic conferences are not systemic and has great uncertainty, through the years, technologies like functional imaging have changed our way of seeing and interpreting the RDs [33],enhance the ability of our emergency physicians to self-learn is very important.…”

Section: Discussionmentioning

confidence: 99%

Poor education and urgent information need for emergency physicians about rare diseases in China

Zhou

Yang

2022

Orphanet J Rare Dis

View full text Add to dashboard Cite

Background Rare diseases are serious and chronic diseases that affect no more than 1 person in 2000 (in European Union criteria). Patients suffering from RDs may come to the emergency department for life-threatening symptoms, such as acute aortic dissection, intracranial haemorrhage, and severe respiratory distress. Diagnostic delay of rare disease patients is common and often caused by low rare disease awareness among physicians. The main aim of this study was to investigate Chinese emergency physicians’ basic knowledge, information access and educational needs regarding rare diseases. An online questionnaire was completed by Chinese emergency physicians during January and March 2021. Methods and results A total of 539 emergency physicians, including 200 females and 339 males, responded to the questionnaire-based study. More than half of the respondents were from Tertiary A hospitals and had engaged in medical clinical work for more than 10 years. Only 4.27% of respondents correctly estimated the prevalence of rare diseases. A few respondents knew the exact number of RDs in the first official list of rare diseases in 2018. A total of 98.5% of respondents rated their knowledge about rare diseases as minimal or insufficient. Most emergency physicians preferred to obtain information through search engines instead of specialized websites on rare diseases. A lack of practice guidelines or consensus was considered the most important reason for the diagnostic delay of RD. Practice guidelines or consensus and professional websites on rare diseases are urgently needed for emergency physicians. Conclusion The investigation shows poor knowledge of rare diseases among emergency physicians. Practice guidelines and professional websites on rare diseases were the primary urgent needs for emergency physicians. Specialized RD courses should also be added to medical education.

show abstract

Section: Discussionmentioning

confidence: 99%

Poor education and urgent information need for emergency physicians about rare diseases in China

Zhou

Yang

2022

Orphanet J Rare Dis

View full text Add to dashboard Cite

show abstract

“…One alternative imaging method is Black Bone magnet resonance imaging [14,15], which has the notable drawback that the infant needs to be sedated during image acquisition to prevent child from moving. Sonographic examinations [16] and 3D photogrammetry are radiation-free and broadly available diagnostic options. Photogrammetric scans provide inexpensive and fast means to objectively quantify head shape without exposure to radiation or sedation.…”

Section: Introduction 1craniosynostosismentioning

confidence: 99%

“…They are often used to monitor the condition before surgery and the head development after the operation [ 17 ]. For more information about the current development, we refer to [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

A Radiation-Free Classification Pipeline for Craniosynostosis Using Statistical Shape Modeling

et al. 2022

View full text Add to dashboard Cite

Background: Craniosynostosis is a condition caused by the premature fusion of skull sutures, leading to irregular growth patterns of the head. Three-dimensional photogrammetry is a radiation-free alternative to the diagnosis using computed tomography. While statistical shape models have been proposed to quantify head shape, no shape-model-based classification approach has been presented yet. Methods: We present a classification pipeline that enables an automated diagnosis of three types of craniosynostosis. The pipeline is based on a statistical shape model built from photogrammetric surface scans. We made the model and pathology-specific submodels publicly available, making it the first publicly available craniosynostosis-related head model, as well as the first focusing on infants younger than 1.5 years. To the best of our knowledge, we performed the largest classification study for craniosynostosis to date. Results: Our classification approach yields an accuracy of 97.8 %, comparable to other state-of-the-art methods using both computed tomography scans and stereophotogrammetry. Regarding the statistical shape model, we demonstrate that our model performs similar to other statistical shape models of the human head. Conclusion: We present a state-of-the-art shape-model-based classification approach for a radiation-free diagnosis of craniosynostosis. Our publicly available shape model enables the assessment of craniosynostosis on realistic and synthetic data.

show abstract

“…Patients with craniofacial deformities typically present in early infancy and are conventionally diagnosed through clinical examination, ultrasound, or imaging modalities like Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) [8,9]. However, the use of tomographic techniques, especially in young patients, necessitates general anesthesia and carries the risk of radiation exposure in CT imaging, a concern given the increased susceptibility to malignancies during early development [10,11].…”

Section: Introductionmentioning

confidence: 99%

The Use of Artificial Intelligence for the Classification of Craniofacial Deformities

Kuehle,

Ringwald,

Bouffleur

et al. 2023

JCM

View full text Add to dashboard Cite

Positional cranial deformities are a common finding in toddlers, yet differentiation from craniosynostosis can be challenging. The aim of this study was to train convolutional neural networks (CNNs) to classify craniofacial deformities based on 2D images generated using photogrammetry as a radiation-free imaging technique. A total of 487 patients with photogrammetry scans were included in this retrospective cohort study: children with craniosynostosis (n = 227), positional deformities (n = 206), and healthy children (n = 54). Three two-dimensional images were extracted from each photogrammetry scan. The datasets were divided into training, validation, and test sets. During the training, fine-tuned ResNet-152s were utilized. The performance was quantified using tenfold cross-validation. For the detection of craniosynostosis, sensitivity was at 0.94 with a specificity of 0.85. Regarding the differentiation of the five existing classes (trigonocephaly, scaphocephaly, positional plagiocephaly left, positional plagiocephaly right, and healthy), sensitivity ranged from 0.45 (positional plagiocephaly left) to 0.95 (scaphocephaly) and specificity ranged from 0.87 (positional plagiocephaly right) to 0.97 (scaphocephaly). We present a CNN-based approach to classify craniofacial deformities on two-dimensional images with promising results. A larger dataset would be required to identify rarer forms of craniosynostosis as well. The chosen 2D approach enables future applications for digital cameras or smartphones.

show abstract

The Evolution of the Role of Imaging in the Diagnosis of Craniosynostosis: A Narrative Review

Cited by 11 publications

References 41 publications

Poor education and urgent information need for emergency physicians about rare diseases in China

Poor education and urgent information need for emergency physicians about rare diseases in China

A Radiation-Free Classification Pipeline for Craniosynostosis Using Statistical Shape Modeling

The Use of Artificial Intelligence for the Classification of Craniofacial Deformities

Contact Info

Product

Resources

About