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Aydin AC, Karakol P, Sulhan A, Erk H, Bozkurt M. Comparison of Anthropometric and Cephalometric Measurements Obtained by Stereophotogrammetry and 3D Computed Tomography of the Nose Before Septorhinoplasty. Aesthetic Plast Surg 2024:10.1007/s00266-024-04097-9. [PMID: 38755496 DOI: 10.1007/s00266-024-04097-9] [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: 09/15/2023] [Accepted: 04/15/2024] [Indexed: 05/18/2024]
Abstract
INTRODUCTION Computed tomography (CT) is normally used in evaluation of patients with esthetic and functional nasal deformities. Stereophotogrammetry (SPG) is a measurement device that is an alternative to CT and does not harm human health. In this single-center retrospective study, we aimed to evaluate measurements obtained with CT and SPG. METHODS The measurements of 18 patients who applied to our clinic between January 2022 and August 2022 and planned for septorhinoplasty were performed on both 3D images obtained with paranasal sinus CT and SPG device (SLR type Vectra H1 system). Measurements included that dorsocolumellar length, columella-filtral length, nasal tip projection ratio (dorsocolumellar length/columella-filtral length), columella-labial angle, nasofrontal angle, tip deviation direction, tip deviation angle, tip deviation distance and dorsal nasal hump. RESULTS Most of patients were male (61.1%). Mean age was 24.5 years. Only columella-labial angle measurements showed a low level of significant difference (p < 0.05). However, there was no significance difference in other measurements (p > 0.05). A significant strong correlation was observed between all Vectra and CT measurements (p = 0.000). CONCLUSION SPG device can be applied quickly in polyclinic without giving radiation to patient. Measurements can be taken automatically using a software. Its use in postoperative period does not carry any risk. Disadvantage of SPG is lack of information about internal nasal passage. However, there is a strong correlation between measurements obtained from both measurement devices. Therefore, SPG can be considered as an alternative to CT imaging in operation planning. LEVEL OF EVIDENCE IV This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
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Affiliation(s)
- Ali Can Aydin
- Department of Plastic, Reconstructive, and Aesthetic Surgery, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Percin Karakol
- Department of Plastic, Reconstructive, and Aesthetic Surgery, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey.
| | - Agit Sulhan
- Department of Plastic, Reconstructive, and Aesthetic Surgery, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Hamdullah Erk
- Department of Radiology, Cemil Tascioglu City Hospital, Istanbul, Turkey
| | - Mehmet Bozkurt
- Department of Plastic, Reconstructive and Aesthetic Surgery, Health Science University Bagcilar Education and Training Hospital, Istanbul, Turkey
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Johnsen SG. Computational Rhinology: Unraveling Discrepancies between In Silico and In Vivo Nasal Airflow Assessments for Enhanced Clinical Decision Support. Bioengineering (Basel) 2024; 11:239. [PMID: 38534513 DOI: 10.3390/bioengineering11030239] [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: 01/09/2024] [Revised: 02/09/2024] [Accepted: 02/17/2024] [Indexed: 03/28/2024] Open
Abstract
Computational rhinology is a specialized branch of biomechanics leveraging engineering techniques for mathematical modelling and simulation to complement the medical field of rhinology. Computational rhinology has already contributed significantly to advancing our understanding of the nasal function, including airflow patterns, mucosal cooling, particle deposition, and drug delivery, and is foreseen as a crucial element in, e.g., the development of virtual surgery as a clinical, patient-specific decision support tool. The current paper delves into the field of computational rhinology from a nasal airflow perspective, highlighting the use of computational fluid dynamics to enhance diagnostics and treatment of breathing disorders. This paper consists of three distinct parts-an introduction to and review of the field of computational rhinology, a review of the published literature on in vitro and in silico studies of nasal airflow, and the presentation and analysis of previously unpublished high-fidelity CFD simulation data of in silico rhinomanometry. While the two first parts of this paper summarize the current status and challenges in the application of computational tools in rhinology, the last part addresses the gross disagreement commonly observed when comparing in silico and in vivo rhinomanometry results. It is concluded that this discrepancy cannot readily be explained by CFD model deficiencies caused by poor choice of turbulence model, insufficient spatial or temporal resolution, or neglecting transient effects. Hence, alternative explanations such as nasal cavity compliance or drag effects due to nasal hair should be investigated.
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Steybe D, Poxleitner P, Metzger MC, Brandenburg LS, Schmelzeisen R, Bamberg F, Tran PH, Kellner E, Reisert M, Russe MF. Automated segmentation of head CT scans for computer-assisted craniomaxillofacial surgery applying a hierarchical patch-based stack of convolutional neural networks. Int J Comput Assist Radiol Surg 2022; 17:2093-2101. [PMID: 35665881 PMCID: PMC9515026 DOI: 10.1007/s11548-022-02673-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 05/03/2022] [Indexed: 11/25/2022]
Abstract
Purpose Computer-assisted techniques play an important role in craniomaxillofacial surgery. As segmentation of three-dimensional medical imaging represents a cornerstone for these procedures, the present study was aiming at investigating a deep learning approach for automated segmentation of head CT scans. Methods The deep learning approach of this study was based on the patchwork toolbox, using a multiscale stack of 3D convolutional neural networks. The images were split into nested patches using a fixed 3D matrix size with decreasing physical size in a pyramid format of four scale depths. Manual segmentation of 18 craniomaxillofacial structures was performed in 20 CT scans, of which 15 were used for the training of the deep learning network and five were used for validation of the results of automated segmentation. Segmentation accuracy was evaluated by Dice similarity coefficient (DSC), surface DSC, 95% Hausdorff distance (95HD) and average symmetric surface distance (ASSD). Results Mean for DSC was 0.81 ± 0.13 (range: 0.61 [mental foramen] – 0.98 [mandible]). Mean Surface DSC was 0.94 ± 0.06 (range: 0.87 [mental foramen] – 0.99 [mandible]), with values > 0.9 for all structures but the mental foramen. Mean 95HD was 1.93 ± 2.05 mm (range: 1.00 [mandible] – 4.12 mm [maxillary sinus]) and for ASSD, a mean of 0.42 ± 0.44 mm (range: 0.09 [mandible] – 1.19 mm [mental foramen]) was found, with values < 1 mm for all structures but the mental foramen. Conclusion In this study, high accuracy of automated segmentation of a variety of craniomaxillofacial structures could be demonstrated, suggesting this approach to be suitable for the incorporation into a computer-assisted craniomaxillofacial surgery workflow. The small amount of training data required and the flexibility of an open source-based network architecture enable a broad variety of clinical and research applications. Supplementary Information The online version contains supplementary material available at 10.1007/s11548-022-02673-5.
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Affiliation(s)
- David Steybe
- Department of Oral and Maxillofacial Surgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Str. 55, 79106, Freiburg, Germany.
| | - Philipp Poxleitner
- Department of Oral and Maxillofacial Surgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Str. 55, 79106, Freiburg, Germany.,Berta-Ottenstein-Programme for Clinician Scientists, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Marc Christian Metzger
- Department of Oral and Maxillofacial Surgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Str. 55, 79106, Freiburg, Germany
| | - Leonard Simon Brandenburg
- Department of Oral and Maxillofacial Surgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Str. 55, 79106, Freiburg, Germany
| | - Rainer Schmelzeisen
- Department of Oral and Maxillofacial Surgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Str. 55, 79106, Freiburg, Germany
| | - Fabian Bamberg
- Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Phuong Hien Tran
- Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Elias Kellner
- Department of Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Marco Reisert
- Department of Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Maximilian Frederik Russe
- Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
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