Deep learning for orbital fracture detection and reconstruction: A systematic review on diagnostic accuracy and surgical planning.

Journal: Journal of cranio-maxillo-facial surgery : official publication of the European Association for Cranio-Maxillo-Facial Surgery
Published Date: Jun 17, 2025

Abstract

OBJECTIVE: To systematically review the efficacy of deep learning (DL) models in detecting and reconstructing orbital fractures based on computed tomography (CT) imaging, assessing their diagnostic accuracy, processing time, and role in surgical planning.

Authors

  • Tania Camila Niño-Sandoval
    Universidad Nacional de Colombia - Bogotá. Faculty of Dentistry, Oral Health Department. Master of Dentistry. Craniofacial Growth and Development Research Group. Genetics Institute, Cll 53 - Cra. 37 Ed. 426 Of. 213. Bogotá Colombia. Electronic address: kotc2578@gmail.com.
  • Fabrício Souza Landim
    Department of Oral and Maxillofacial Surgery and Traumatology, Universidade de Pernambuco - School of Dentistry (UPE/FOP), Arcoverde, Brazil.
  • Belmiro C E Vasconcelos
    Department of Oral and Maxillofacial Surgery and Traumatology, Coordinator of the Postgraduate Program in Oral and Maxillofacial Surgery and Traumatology, University of Pernambuco - School of Dentistry (UPE/FOP), University Hospital Oswaldo Cruz, Rua Arnóbio Marquês, 310 - Santo Amaro, CEP: 50.100-130, Recife, PE, Brazil. belmiro.vasconcelos@upe.br.

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