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Jolibois MI, Roohani I, Moshal T, Lasky S, Urata M, Munabi NC, Johns AL, Sader N, Durham SR, Urata MM. Sociodemographic Factors Associated with Delayed Presentation in Craniosynostosis Surgery at a Tertiary Children's Hospital. PLASTIC AND RECONSTRUCTIVE SURGERY-GLOBAL OPEN 2024; 12:e6035. [PMID: 39220750 PMCID: PMC11365692 DOI: 10.1097/gox.0000000000006035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Accepted: 06/04/2024] [Indexed: 09/04/2024]
Abstract
Background Craniosynostosis is a common diagnosis requiring early referral to a pediatric plastic surgeon; however, disparities in healthcare may influence presentation timing and affect treatment options and outcomes. This study aimed to explore sociodemographic factors contributing to delay in craniosynostosis surgical consultation. Methods A retrospective cohort study of 694 California-based craniosynostosis patients at a tertiary children's hospital was performed from 2006 to 2023. State-specific Area Deprivation Index (ADI) and distance to the hospital were calculated using ZIP codes. Multivariate linear and logistic regressions considered race, insurance type, syndromic status, suture type, and ZIP code-based socioeconomic factors. Results Median age of presentation was 4.5 [interquartile range: 2.6-7.6] months with racial/ethnic breakdown of Hispanic/Latinx (41.2%), White (23.6%), Asian (3.7%), Black/African American (2.0%), or other/unreported (29.5%) with 58.4% having public insurance and an average distance to the hospital of 48.3 km. Median ADI was 5.4 [interquartile range: 4.0-7.1]. By linear regression, public insurance (P < 0.001) and higher ADI decile (P < 0.001) independently contributed to an older age of presentation. Patients with public insurance (odds ratio 1.90; P = 0.002) were more likely to present after 4 months of age. Conclusions Patients who had public insurance or resided in more disadvantaged areas presented later for craniosynostosis surgical consultation. Eliminating disparities in these populations ensures more equitable access to surgical options and can improve patient outcomes.
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Affiliation(s)
- Marah I. Jolibois
- From the Division of Plastic and Maxillofacial Surgery, Children’s Hospital Los Angeles, Los Angeles, Calif
- Albany Medical College, Albany, N.Y
| | - Idean Roohani
- From the Division of Plastic and Maxillofacial Surgery, Children’s Hospital Los Angeles, Los Angeles, Calif
- Keck School of Medicine, University of Southern California, Los Angeles, Calif
| | - Tayla Moshal
- From the Division of Plastic and Maxillofacial Surgery, Children’s Hospital Los Angeles, Los Angeles, Calif
- Keck School of Medicine, University of Southern California, Los Angeles, Calif
| | - Sasha Lasky
- From the Division of Plastic and Maxillofacial Surgery, Children’s Hospital Los Angeles, Los Angeles, Calif
- Keck School of Medicine, University of Southern California, Los Angeles, Calif
| | - Maya Urata
- From the Division of Plastic and Maxillofacial Surgery, Children’s Hospital Los Angeles, Los Angeles, Calif
| | - Naikhoba C.O. Munabi
- From the Division of Plastic and Maxillofacial Surgery, Children’s Hospital Los Angeles, Los Angeles, Calif
| | - Alexis L. Johns
- From the Division of Plastic and Maxillofacial Surgery, Children’s Hospital Los Angeles, Los Angeles, Calif
| | - Nicholas Sader
- Division of Pediatric Neurosurgery, Children’s Hospital Los Angeles, Los Angeles, Calif
| | - Susan R. Durham
- Division of Pediatric Neurosurgery, Children’s Hospital Los Angeles, Los Angeles, Calif
| | - Mark M. Urata
- From the Division of Plastic and Maxillofacial Surgery, Children’s Hospital Los Angeles, Los Angeles, Calif
- Division of Plastic and Reconstructive Surgery, Keck School of Medicine of USC, Los Angeles, Calif
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2
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Abdel-Alim T, Tapia Chaca F, Mathijssen IMJ, Dirven CMF, Niessen WJ, Wolvius EB, van Veelen MLC, Roshchupkin GV. Quantifying dysmorphologies of the neurocranium using artificial neural networks. J Anat 2024. [PMID: 38760946 DOI: 10.1111/joa.14061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 04/29/2024] [Accepted: 04/30/2024] [Indexed: 05/20/2024] Open
Abstract
BACKGROUND Craniosynostosis, a congenital condition characterized by the premature fusion of cranial sutures, necessitates objective methods for evaluating cranial morphology to enhance patient treatment. Current subjective assessments often lead to inconsistent outcomes. This study introduces a novel, quantitative approach to classify craniosynostosis and measure its severity. METHODS An artificial neural network was trained to classify normocephalic, trigonocephalic, and scaphocephalic head shapes based on a publicly available dataset of synthetic 3D head models. Each 3D model was converted into a low-dimensional shape representation based on the distribution of normal vectors, which served as the input for the neural network, ensuring complete patient anonymity and invariance to geometric size and orientation. Explainable AI methods were utilized to highlight significant features when making predictions. Additionally, the Feature Prominence (FP) score was introduced, a novel metric that captures the prominence of distinct shape characteristics associated with a given class. Its relationship with clinical severity scores was examined using the Spearman Rank Correlation Coefficient. RESULTS The final model achieved excellent test accuracy in classifying the different cranial shapes from their low-dimensional representation. Attention maps indicated that the network's attention was predominantly directed toward the parietal and temporal regions, as well as toward the region signifying vertex depression in scaphocephaly. In trigonocephaly, features around the temples were most pronounced. The FP score showed a strong positive monotonic relationship with clinical severity scores in both scaphocephalic (ρ = 0.83, p < 0.001) and trigonocephalic (ρ = 0.64, p < 0.001) models. Visual assessments further confirmed that as FP values rose, phenotypic severity became increasingly evident. CONCLUSION This study presents an innovative and accessible AI-based method for quantifying cranial shape that mitigates the need for adjustments due to age-specific size variations or differences in the spatial orientation of the 3D images, while ensuring complete patient privacy. The proposed FP score strongly correlates with clinical severity scores and has the potential to aid in clinical decision-making and facilitate multi-center collaborations. Future work will focus on validating the model with larger patient datasets and exploring the potential of the FP score for broader applications. The publicly available source code facilitates easy implementation, aiming to advance craniofacial care and research.
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Affiliation(s)
- Tareq Abdel-Alim
- Department of Neurosurgery, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Franz Tapia Chaca
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Irene M J Mathijssen
- Department of Plastic and Reconstructive Surgery, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Clemens M F Dirven
- Department of Neurosurgery, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Wiro J Niessen
- Faculty of Medical Sciences, University of Groningen, Groningen, The Netherlands
| | - Eppo B Wolvius
- Department of Oral- and Maxillofacial Surgery, Erasmus Medical Center, Rotterdam, The Netherlands
| | | | - Gennady V Roshchupkin
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
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3
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Watson AL, Winters R. Nonsyndromic Craniofacial Disorders. Facial Plast Surg Clin North Am 2024; 32:127-139. [PMID: 37981408 DOI: 10.1016/j.fsc.2023.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2023]
Abstract
We describe the investigation and management of select pediatric craniofacial disorders their recent advances. Positional plagiocephaly: The incidence of positional plagiocephaly has increased since the institution of the "safe to sleep" campaign to reduce sudden infant death syndrome. Positional plagiocephaly may be associated with underlying developmental delay. Nonsyndromic craniosynostosis: Treatment of nonsyndromic craniosynostosis depends on the age of the patient and the suture involved. Pediatric skull lesions: Management of skull lesions depends on histologic diagnosis. Some benign skull lesions are managed conservatively, whereas erosive and malignant lesions may require surgical excision, radiotherapy, chemotherapy, or multimodality treatment.
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Affiliation(s)
- Antonia L Watson
- Department of Otolaryngology - Head & Neck Surgery, John Hunter Hospital, Lookout Road, New Lambton Heights, New South Wales 2035, Australia
| | - Ryan Winters
- Department of Otolaryngology - Head & Neck Surgery, John Hunter Hospital, Lookout Road, New Lambton Heights, New South Wales 2035, Australia; Department of Otolaryngology-Head & Neck Surgery, Tulane University, New Orleans, LA, USA; Division of Plastic & Reconstructive Surgery, Tulane University, New Orleans, LA, USA.
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4
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TerKonda SP, TerKonda AA, Sacks JM, Kinney BM, Gurtner GC, Nachbar JM, Reddy SK, Jeffers LL. Artificial Intelligence: Singularity Approaches. Plast Reconstr Surg 2024; 153:204e-217e. [PMID: 37075274 DOI: 10.1097/prs.0000000000010572] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/21/2023]
Abstract
SUMMARY Artificial intelligence (AI) has been a disruptive technology within health care, from the development of simple care algorithms to complex deep-learning models. AI has the potential to reduce the burden of administrative tasks, advance clinical decision-making, and improve patient outcomes. Unlocking the full potential of AI requires the analysis of vast quantities of clinical information. Although AI holds tremendous promise, widespread adoption within plastic surgery remains limited. Understanding the basics is essential for plastic surgeons to evaluate the potential uses of AI. This review provides an introduction of AI, including the history of AI, key concepts, applications of AI in plastic surgery, and future implications.
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Affiliation(s)
- Sarvam P TerKonda
- From the Division of Plastic and Reconstructive Surgery, Mayo Clinic Florida
| | - Anurag A TerKonda
- Division of Plastic and Reconstructive Surgery, Washington University School of Medicine in St. Louis
| | - Justin M Sacks
- Division of Plastic and Reconstructive Surgery, Washington University School of Medicine in St. Louis
| | - Brian M Kinney
- Division of Plastic Surgery, University of Southern California
| | - Geoff C Gurtner
- Division of Plastic and Reconstructive Surgery, Stanford University
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5
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Kuehle R, Ringwald F, Bouffleur F, Hagen N, Schaufelberger M, Nahm W, Hoffmann J, Freudlsperger C, Engel M, Eisenmann U. The Use of Artificial Intelligence for the Classification of Craniofacial Deformities. J Clin Med 2023; 12:7082. [PMID: 38002694 PMCID: PMC10672418 DOI: 10.3390/jcm12227082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 10/27/2023] [Accepted: 11/02/2023] [Indexed: 11/26/2023] Open
Abstract
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.
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Affiliation(s)
- Reinald Kuehle
- Department of Oral and Maxillofacial Surgery, University of Heidelberg, 69120 Heidelberg, Germany; (F.B.); (J.H.); (C.F.)
| | - Friedemann Ringwald
- Institute of Medical Informatics, University of Heidelberg, 69120 Heidelberg, Germany; (F.R.)
| | - Frederic Bouffleur
- Department of Oral and Maxillofacial Surgery, University of Heidelberg, 69120 Heidelberg, Germany; (F.B.); (J.H.); (C.F.)
| | - Niclas Hagen
- Institute of Medical Informatics, University of Heidelberg, 69120 Heidelberg, Germany; (F.R.)
| | - Matthias Schaufelberger
- Institute of Biomedical Engineering, Karlsruhe Institute for Technology, 76131 Karlsruhe, Germany
| | - Werner Nahm
- Institute of Biomedical Engineering, Karlsruhe Institute for Technology, 76131 Karlsruhe, Germany
| | - Jürgen Hoffmann
- Department of Oral and Maxillofacial Surgery, University of Heidelberg, 69120 Heidelberg, Germany; (F.B.); (J.H.); (C.F.)
| | - Christian Freudlsperger
- Department of Oral and Maxillofacial Surgery, University of Heidelberg, 69120 Heidelberg, Germany; (F.B.); (J.H.); (C.F.)
| | - Michael Engel
- Department of Oral and Maxillofacial Surgery, University of Heidelberg, 69120 Heidelberg, Germany; (F.B.); (J.H.); (C.F.)
| | - Urs Eisenmann
- Institute of Medical Informatics, University of Heidelberg, 69120 Heidelberg, Germany; (F.R.)
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Quispe-Enriquez OC, Valero-Lanzuela JJ, Lerma JL. Smartphone Photogrammetric Assessment for Head Measurements. SENSORS (BASEL, SWITZERLAND) 2023; 23:9008. [PMID: 37960704 PMCID: PMC10648760 DOI: 10.3390/s23219008] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 10/24/2023] [Accepted: 10/28/2023] [Indexed: 11/15/2023]
Abstract
The assessment of cranial deformation is relevant in the field of medicine dealing with infants, especially in paediatric neurosurgery and paediatrics. To address this demand, the smartphone-based solution PhotoMeDAS has been developed, harnessing mobile devices to create three-dimensional (3D) models of infants' heads and, from them, automatic cranial deformation reports. Therefore, it is crucial to examine the accuracy achievable with different mobile devices under similar conditions so prospective users can consider this aspect when using the smartphone-based solution. This study compares the linear accuracy obtained from three smartphone models (Samsung Galaxy S22 Ultra, S22, and S22+). Twelve measurements are taken with each mobile device using a coded cap on a head mannequin. For processing, three different bundle adjustment implementations are tested with and without self-calibration. After photogrammetric processing, the 3D coordinates are obtained. A comparison is made among spatially distributed distances across the head with PhotoMeDAS vs. ground truth established with a Creaform ACADEMIA 50 while-light 3D scanner. With a homogeneous scale factor for all the smartphones, the results showed that the average accuracy for the S22 smartphone is -1.15 ± 0.53 mm, for the S22+, 0.95 ± 0.40 mm, and for the S22 Ultra, -1.8 ± 0.45 mm. Worth noticing is that a substantial improvement is achieved regardless of whether the scale factor is introduced per device.
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Affiliation(s)
- Omar C. Quispe-Enriquez
- Photogrammetry and Laser Scanner Research Group (GIFLE), Department of Cartographic Engineering, Geodesy and Photogrammetry, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain; (J.J.V.-L.); (J.L.L.)
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Nachmani O, Saun T, Huynh M, Forrest CR, McRae M. "Facekit"-Toward an Automated Facial Analysis App Using a Machine Learning-Derived Facial Recognition Algorithm. Plast Surg (Oakv) 2023; 31:321-329. [PMID: 37915352 PMCID: PMC10617451 DOI: 10.1177/22925503211073843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Revised: 11/14/2021] [Accepted: 11/22/2021] [Indexed: 11/03/2023] Open
Abstract
Introduction: Multiple tools have been developed for facial feature measurements and analysis using facial recognition machine learning techniques. However, several challenges remain before these will be useful in the clinical context for reconstructive and aesthetic plastic surgery. Smartphone-based applications utilizing open-access machine learning tools can be rapidly developed, deployed, and tested for use in clinical settings. This research compares a smartphone-based facial recognition algorithm to direct and digital measurement performance for use in facial analysis. Methods: Facekit is a camera application developed for Android that utilizes ML Kit, an open-access computer vision Application Programing Interface developed by Google. Using the facial landmark module, we measured 4 facial proportions in 15 healthy subjects and compared them to direct surface and digital measurements using intraclass correlation (ICC) and Pearson correlation. Results: Measurement of the naso-facial proportion achieved the highest ICC of 0.321, where ICC > 0.75 is considered an excellent agreement between methods. Repeated measures analysis of variance of proportion measurements between ML Kit, direct and digital methods, were significantly different (F[2,14] = 6-26, P<<.05). Facekit measurements of orbital, orbitonasal, naso-oral, and naso-facial ratios had overall low correlation and agreement to both direct and digital measurements (R<<0.5, ICC<<0.75). Conclusion: Facekit is a smartphone camera application for rapid facial feature analysis. Agreement between Facekit's machine learning measurements and direct and digital measurements was low. We conclude that the chosen pretrained facial recognition software is not accurate enough for conducting a clinically useful facial analysis. Custom models trained on accurate and clinically relevant landmarks may provide better performance.
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Affiliation(s)
- Omri Nachmani
- Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Tomas Saun
- Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Minh Huynh
- Division of Plastic and Reconstructive Surgery, McMaster University, Hamilton, Ontario, Canada
| | | | - Mark McRae
- Division of Plastic and Reconstructive Surgery, McMaster University, Hamilton, Ontario, Canada
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Zhong NN, Wang HQ, Huang XY, Li ZZ, Cao LM, Huo FY, Liu B, Bu LL. Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives. Semin Cancer Biol 2023; 95:52-74. [PMID: 37473825 DOI: 10.1016/j.semcancer.2023.07.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 07/11/2023] [Accepted: 07/15/2023] [Indexed: 07/22/2023]
Abstract
Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate anatomical structure of these regions poses considerable challenges to efficacious treatment strategies. Despite the availability of myriad treatment modalities, the overall therapeutic efficacy for HNTs continues to remain subdued. In recent years, the deployment of artificial intelligence (AI) in healthcare practices has garnered noteworthy attention. AI modalities, inclusive of machine learning (ML), neural networks (NNs), and deep learning (DL), when amalgamated into the holistic management of HNTs, promise to augment the precision, safety, and efficacy of treatment regimens. The integration of AI within HNT management is intricately intertwined with domains such as medical imaging, bioinformatics, and medical robotics. This article intends to scrutinize the cutting-edge advancements and prospective applications of AI in the realm of HNTs, elucidating AI's indispensable role in prevention, diagnosis, treatment, prognostication, research, and inter-sectoral integration. The overarching objective is to stimulate scholarly discourse and invigorate insights among medical practitioners and researchers to propel further exploration, thereby facilitating superior therapeutic alternatives for patients.
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Affiliation(s)
- Nian-Nian Zhong
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Han-Qi Wang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Xin-Yue Huang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Zi-Zhan Li
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Lei-Ming Cao
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Fang-Yi Huo
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Bing Liu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China; Department of Oral & Maxillofacial - Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China.
| | - Lin-Lin Bu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China; Department of Oral & Maxillofacial - Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China.
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Watt A, Lee J, Toews M, Gilardino MS. Smartphone Integration of Artificial Intelligence for Automated Plagiocephaly Diagnosis. PLASTIC AND RECONSTRUCTIVE SURGERY-GLOBAL OPEN 2023; 11:e4985. [PMID: 37197011 PMCID: PMC10184988 DOI: 10.1097/gox.0000000000004985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 03/17/2023] [Indexed: 05/19/2023]
Abstract
Positional plagiocephaly is a pediatric condition with important cosmetic implications affecting ∼40% of infants under 12 months of age. Early diagnosis and treatment initiation is imperative in achieving satisfactory outcomes; improved diagnostic modalities are needed to support this goal. This study aimed to determine whether a smartphone-based artificial intelligence tool could diagnose positional plagiocephaly. Methods A prospective validation study was conducted at a large tertiary care center with two recruitment sites: (1) newborn nursery, (2) pediatric craniofacial surgery clinic. Eligible children were aged 0-12 months with no history of hydrocephalus, intracranial tumors, intracranial hemorrhage, intracranial hardware, or prior craniofacial surgery. Successful artificial intelligence diagnosis required identification of the presence and severity of positional plagiocephaly. Results A total of 89 infants were prospectively enrolled from the craniofacial surgery clinic (n = 25, 17 male infants [68%], eight female infants [32%], mean age 8.44 months) and newborn nursery (n = 64, 29 male infants [45%], 25 female infants [39%], mean age 0 months). The model obtained a diagnostic accuracy of 85.39% compared with a standard clinical examination with a disease prevalence of 48%. Sensitivity was 87.50% [95% CI, 75.94-98.42] with a specificity of 83.67% [95% CI, 72.35-94.99]. Precision was 81.40%, while likelihood ratios (positive and negative) were 5.36 and 0.15, respectively. The F1-score was 84.34%. Conclusions The smartphone-based artificial intelligence algorithm accurately diagnosed positional plagiocephaly in a clinical environment. This technology may provide value by helping guide specialist consultation and enabling longitudinal quantitative monitoring of cranial shape.
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Affiliation(s)
- Ayden Watt
- From the Department of Experimental Surgery, McGill University, Montreal, Canada
| | - James Lee
- Division of Plastic and Reconstructive Surgery, McGill University Health Center, Montreal, Canada
| | - Matthew Toews
- École de Technologie Supérieure, Department of Systems Engineering, Montréal, Canada
| | - Mirko S. Gilardino
- Division of Plastic and Reconstructive Surgery, McGill University Health Center, Montreal, Canada
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10
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Application of Deep Learning Techniques for Automated Diagnosis of Non-Syndromic Craniosynostosis Using Skull X-Ray. J Craniofac Surg 2022; 33:1843-1846. [PMID: 35261366 DOI: 10.1097/scs.0000000000008620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Accepted: 02/14/2022] [Indexed: 11/26/2022] Open
Abstract
ABSTRACT Non-syndromic craniosynostosis (NSCS) is a disease, in which a single cranial bone suture is prematurely fused. The early intervention of the disease is associated with a favorable outcome at a later age, so appropriate screening of NSCS is essential for its clinical management. The present study aims to develop a classification and detection system of NSCS using skull X-ray images and a convolutional neural network (CNN) deep learning framework. A total of 56 NSCS cases (scaphocephaly [n = 17], trigonocephaly [n = 28], anterior plagiocephaly [n = 8], and posterior plagiocephaly [n = 3]) and 25 healthy control infants were included in the study. All the cases underwent skull X-rays and computed tomography scan for diagnosis in our institution. The lateral views obtained from the patients were retrospectively examined using a CNN framework. Our CNN model classified the 4 NSCS types and control with high accuracy (100%). All the cases were correctly classified. The proposed CNN model may offer a safe and high-sensitivity screening of NSCS and facilitate early diagnosis of the disease and better neurocognitive outcome for patients.
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11
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Watt A, Zammit D, Lee J, Gilardino M. Novel Screening and Monitoring Techniques for Deformational Plagiocephaly: A Systematic Review. Pediatrics 2022; 149:184526. [PMID: 35059723 DOI: 10.1542/peds.2021-051736] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/01/2021] [Indexed: 11/24/2022] Open
Abstract
This article summarizes the current state of diagnostic modalities for infant craniofacial deformities and highlights capable diagnostic tools available currently to pediatricians.
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Affiliation(s)
- Ayden Watt
- Department of Experimental Surgery, McGill University, Montreal, QC, Canada
| | - Dino Zammit
- Division of Plastic and Reconstructive Surgery, McGill University Health Centre, Montreal, QC, Canada
| | - James Lee
- Division of Plastic and Reconstructive Surgery, McGill University Health Centre, Montreal, QC, Canada
| | - Mirko Gilardino
- Division of Plastic and Reconstructive Surgery, McGill University Health Centre, Montreal, QC, Canada
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