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Artificial Intelligence Methods for the Argenta Classification of Deformational Plagiocephaly to Predict Severity and Treatment Recommendation.
Nguyen, Huan T; Obinero, Chioma G; Wang, Ellen; Boyd, Alexandra K; Cepeda, Alfredo; Talanker, Michael; Mumford, Danielle; Littlefield, Tim; Greives, Matthew R; Nguyen, Phuong D.
Afiliación
  • Nguyen HT; Division of Plastic and Reconstructive Surgery, University of Missouri School of Medicine, Columbia, MO.
  • Obinero CG; Division of Plastic and Reconstructive Surgery, McGovern Medical School at UT Health Houston & Children's Memorial Hermann Hospital.
  • Wang E; Division of Plastic and Reconstructive Surgery, McGovern Medical School at UT Health Houston & Children's Memorial Hermann Hospital.
  • Boyd AK; Division of Plastic and Reconstructive Surgery, McGovern Medical School at UT Health Houston & Children's Memorial Hermann Hospital.
  • Cepeda A; Division of Plastic and Reconstructive Surgery, McGovern Medical School at UT Health Houston & Children's Memorial Hermann Hospital.
  • Talanker M; Division of Plastic and Reconstructive Surgery, McGovern Medical School at UT Health Houston & Children's Memorial Hermann Hospital.
  • Mumford D; Department of Surgery, McGovern Medical School at UT Health Houston, Houston, TX.
  • Littlefield T; Cranial Technologies, Chandler, AZ.
  • Greives MR; Division of Plastic and Reconstructive Surgery, McGovern Medical School at UT Health Houston & Children's Memorial Hermann Hospital.
  • Nguyen PD; Department of Surgery, Division of Plastic and Reconstructive Surgery, University of Colorado School of Medicine.
J Craniofac Surg ; 2024 Jun 28.
Article en En | MEDLINE | ID: mdl-38940555
ABSTRACT

INTRODUCTION:

Deformational plagiocephaly (DP) can be classified into 5 severity types using the Argenta scale (AS). Patients with type III or higher require referral to craniofacial surgery for management. Primary care pediatricians (PCPs) are often the first to encounter patients with DP, but current screening methods are subjective, increasing the risk of bias, especially for clinicians with little exposure to this population. The authors propose the use of artificial intelligence (AI) to classify patients with DP using the AS and to make recommendations for referral to craniofacial surgery.

METHODS:

Vertex photographs were obtained for patients diagnosed with unilateral DP from 2019 to 2020. Using the photographs, an AI program was created to characterize the head contour of these infants into 3 groups based on the AS. The program was trained using photographs from patients whose DP severity was confirmed clinically by craniofacial surgeons. To assess the accuracy of the software, the AS predicted by the program was compared with the clinical diagnosis.

RESULTS:

Nineteen patients were assessed by the AI software. All 3 patients with type I DP were correctly classified by the program (100%). In addition, 4 patients with type II were correctly identified (67%), and 7 were correctly classified as type III or greater (70%).

CONCLUSIONS:

Using vertex photographs and AI, the authors were able to objectively classify patients with DP based on the AS. If converted into a smartphone application, the program could be helpful to PCPs in remote or low-resource settings, allowing them to objectively determine which patients require referral to craniofacial surgery.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Craniofac Surg Asunto de la revista: ODONTOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Macao Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Craniofac Surg Asunto de la revista: ODONTOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Macao Pais de publicación: Estados Unidos