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Diagnostics
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Craniofacial Imaging in Clinical Practice: Techniques, Innovations and Clinical Applications
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Craniofacial Imaging in Clinical Practice: Techniques, Innovations and Clinical Applications

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 8999

Special Issue Editor

Dr. Rami R. Hallac

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Guest Editor
1. Analytical Imaging and Modeling Center, Children’s Health, Dallas, TX, 75235, USA
2. Department of Plastic Surgery, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
Interests: machine learning; craniofacial imaging; ear deformity; craniosynostosis; cleft lip and palate; vascular anomalies; 3D photogrammetry

Special Issue Information

Dear Colleagues,

The field of craniofacial imaging has experienced rapid advancements in recent years, driven by the development of new imaging modalities, algorithms, and applications. This Special Issue aims to present the latest research on and clinical applications of craniofacial imaging, encompassing diagnostic assessment, treatment planning, and postoperative evaluation. It will also discuss emerging technologies and future directions in the field. This comprehensive collection of articles will serve as an essential reference for researchers, clinicians, and students involved in craniofacial imaging.

The topics covered include, but are not limited to, the following:

  • Three-dimensional modeling and surgical simulation;
  • Postoperative evaluation;
  • Artificial intelligence and machine learning;
  • Pediatric craniofacial imaging;
  • Craniofacial trauma imaging;
  • Imaging in temporomandibular joint disorders.

Dr. Rami R. Hallac
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at mdpi.longhoe.net by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • surgical simulation

  • three-dimensional modeling

  • virtual surgical planning

  • artificial intelligence (AI)

  • machine learning

  • three-dimensional printing

  • pediatric craniofacial disorders

  • craniosynostosis

  • cleft lip and palate

  • craniofacial trauma

  • craniofacial imaging

Published Papers (5 papers)

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Research

16 pages, 4162 KiB  
Article
Nasal Airflow Dynamics following LeFort I Advancement in Cleft Nasal Deformities: A Retrospective Preliminary Study
by Daniel Charles, Lucas Harrison, Fatemeh Hassanipour and Rami R. Hallac
Diagnostics 2024, 14(12), 1294; https://doi.org/10.3390/diagnostics14121294 - 19 Jun 2024
Viewed by 369
Abstract
Unilateral cleft lip and palate (UCLP) nasal deformity impacts airflow patterns and pressure distribution, leading to nasal breathing difficulties. This study aims to create an integrated approach using computer-aided design (CAD) and computational fluid dynamics (CFD) to simulate airway function and assess outcomes [...] Read more.
Unilateral cleft lip and palate (UCLP) nasal deformity impacts airflow patterns and pressure distribution, leading to nasal breathing difficulties. This study aims to create an integrated approach using computer-aided design (CAD) and computational fluid dynamics (CFD) to simulate airway function and assess outcomes in nasal deformities associated with unilateral cleft lip and palate (UCLP) after LeFort I osteotomy advancement. Significant alterations were observed in nasal geometry, airflow velocity, pressure dynamics, volumetric flow rate, and nasal resistance postoperatively, indicating improved nasal airflow. The cross-sectional area increased by 26.6%, airflow rate by 6.53%, and nasal resistance decreased by 6.23%. The study offers quantitative insights into the functional impacts of such surgical interventions, contributing to a deeper understanding of UCLP nasal deformity treatment and providing objective metrics for assessing surgical outcome. Full article
(This article belongs to the Special Issue Craniofacial Imaging in Clinical Practice: Techniques, Innovations and Clinical Applications)
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16 pages, 3718 KiB  
Article
Three-Dimensional Craniofacial Landmark Detection in Series of CT Slices Using Multi-Phased Regression Networks
by Soh Nishimoto, Takuya Saito, Hisako Ishise, Toshihiro Fujiwara, Kenichiro Kawai and Masao Kakibuchi
Diagnostics 2023, 13(11), 1930; https://doi.org/10.3390/diagnostics13111930 - 1 Jun 2023
Cited by 1 | Viewed by 1856
Abstract
Geometrical assessments of human skulls have been conducted based on anatomical landmarks. If developed, the automatic detection of these landmarks will yield both medical and anthropological benefits. In this study, an automated system with multi-phased deep learning networks was developed to predict the [...] Read more.
Geometrical assessments of human skulls have been conducted based on anatomical landmarks. If developed, the automatic detection of these landmarks will yield both medical and anthropological benefits. In this study, an automated system with multi-phased deep learning networks was developed to predict the three-dimensional coordinate values of craniofacial landmarks. Computed tomography images of the craniofacial area were obtained from a publicly available database. They were digitally reconstructed into three-dimensional objects. Sixteen anatomical landmarks were plotted on each of the objects, and their coordinate values were recorded. Three-phased regression deep learning networks were trained using ninety training datasets. For the evaluation, 30 testing datasets were employed. The 3D error for the first phase, which tested 30 data, was 11.60 px on average (1 px = 500/512 mm). For the second phase, it was significantly improved to 4.66 px. For the third phase, it was further significantly reduced to 2.88. This was comparable to the gaps between the landmarks, as plotted by two experienced practitioners. Our proposed method of multi-phased prediction, which conducts coarse detection first and narrows down the detection area, may be a possible solution to prediction problems, taking into account the physical limitations of memory and computation. Full article
(This article belongs to the Special Issue Craniofacial Imaging in Clinical Practice: Techniques, Innovations and Clinical Applications)
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14 pages, 16370 KiB  
Article
An Automated Method of 3D Facial Soft Tissue Landmark Prediction Based on Object Detection and Deep Learning
by Yuchen Zhang, Yifei Xu, Jiamin Zhao, Tian**g Du, Dongning Li, **nyan Zhao, **xiu Wang, Chen Li, Junbo Tu and Kun Qi
Diagnostics 2023, 13(11), 1853; https://doi.org/10.3390/diagnostics13111853 - 25 May 2023
Viewed by 2850
Abstract
Background: Three-dimensional facial soft tissue landmark prediction is an important tool in dentistry, for which several methods have been developed in recent years, including a deep learning algorithm which relies on converting 3D models into 2D maps, which results in the loss of [...] Read more.
Background: Three-dimensional facial soft tissue landmark prediction is an important tool in dentistry, for which several methods have been developed in recent years, including a deep learning algorithm which relies on converting 3D models into 2D maps, which results in the loss of information and precision. Methods: This study proposes a neural network architecture capable of directly predicting landmarks from a 3D facial soft tissue model. Firstly, the range of each organ is obtained by an object detection network. Secondly, the prediction networks obtain landmarks from the 3D models of different organs. Results: The mean error of this method in local experiments is 2.62±2.39, which is lower than that in other machine learning algorithms or geometric information algorithms. Additionally, over 72% of the mean error of test data falls within ±2.5 mm, and 100% falls within 3 mm. Moreover, this method can predict 32 landmarks, which is higher than any other machine learning-based algorithm. Conclusions: According to the results, the proposed method can precisely predict a large number of 3D facial soft tissue landmarks, which gives the feasibility of directly using 3D models for prediction. Full article
(This article belongs to the Special Issue Craniofacial Imaging in Clinical Practice: Techniques, Innovations and Clinical Applications)
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14 pages, 3527 KiB  
Article
Automated Sagittal Skeletal Classification of Children Based on Deep Learning
by Lan Nan, Min Tang, Bohui Liang, Shuixue Mo, Na Kang, Shaohua Song, Xuejun Zhang and **aojuan Zeng
Diagnostics 2023, 13(10), 1719; https://doi.org/10.3390/diagnostics13101719 - 12 May 2023
Cited by 4 | Viewed by 1335
Abstract
Malocclusions are a type of cranio-maxillofacial growth and developmental deformity that occur with high incidence in children. Therefore, a simple and rapid diagnosis of malocclusions would be of great benefit to our future generation. However, the application of deep learning algorithms to the [...] Read more.
Malocclusions are a type of cranio-maxillofacial growth and developmental deformity that occur with high incidence in children. Therefore, a simple and rapid diagnosis of malocclusions would be of great benefit to our future generation. However, the application of deep learning algorithms to the automatic detection of malocclusions in children has not been reported. Therefore, the aim of this study was to develop a deep learning-based method for automatic classification of the sagittal skeletal pattern in children and to validate its performance. This would be the first step in establishing a decision support system for early orthodontic treatment. In this study, four different state-of-the-art (SOTA) models were trained and compared by using 1613 lateral cephalograms, and the best performance model, Densenet-121, was selected was further subsequent validation. Lateral cephalograms and profile photographs were used as the input for the Densenet-121 model, respectively. The models were optimized using transfer learning and data augmentation techniques, and label distribution learning was introduced during model training to address the inevitable label ambiguity between adjacent classes. Five-fold cross-validation was conducted for a comprehensive evaluation of our method. The sensitivity, specificity, and accuracy of the CNN model based on lateral cephalometric radiographs were 83.99, 92.44, and 90.33%, respectively. The accuracy of the model with profile photographs was 83.39%. The accuracy of both CNN models was improved to 91.28 and 83.98%, respectively, while the overfitting decreased after addition of label distribution learning. Previous studies have been based on adult lateral cephalograms. Therefore, our study is novel in using deep learning network architecture with lateral cephalograms and profile photographs obtained from children in order to obtain a high-precision automatic classification of the sagittal skeletal pattern in children. Full article
(This article belongs to the Special Issue Craniofacial Imaging in Clinical Practice: Techniques, Innovations and Clinical Applications)
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11 pages, 2515 KiB  
Article
Effect of Pre-Surgical Orthopedic Treatment on Hard and Soft Tissue Morphology in Infants with Cleft Lip and Palate
by Saki Ogino, Hitoshi Kawanabe, Kazunori Fukui, Ryoko Sone and Akihiko Oyama
Diagnostics 2023, 13(8), 1444; https://doi.org/10.3390/diagnostics13081444 - 17 Apr 2023
Cited by 1 | Viewed by 1443
Abstract
The frequency of cleft lip and palate births in Japan is approximately 0.146%. The study aimed to compare the effects of NAM on restoring nasal morphology and improving extraoral nasal morphology in children with cleft lip and palate in the first stage of [...] Read more.
The frequency of cleft lip and palate births in Japan is approximately 0.146%. The study aimed to compare the effects of NAM on restoring nasal morphology and improving extraoral nasal morphology in children with cleft lip and palate in the first stage of treatment using 3D imaging and oral model analysis. The subjects were five infants (37.6 ± 14.4 days old) with unilateral cleft lip and palate. The images taken with the 3D analyzer and oral model used for constructing the NAM at the first examination (baseline) and at the completion of the pre-surgical orthodontic treatment (157.8 ± 37.8 days old) were analyzed. The cleft distance was measured at the upper, middle, and lower points on the 3D images. On the model, the cleft jaw width at the maximum protrusion of the healthy and affected sides of the alveolar bone was measured. After the pre-surgical orthopedic treatment, the measured value on the model decreased significantly by a mean of 8.3 mm from baseline, and the cleft lip width narrowed by an average of 2.8 ± 2.2, 4.3 ± 2.3, and 3.0 ± 2.8 mm at the upper, middle, and lower points of the cleft, respectively. Pre-surgical orthopedic treatment using NAM can help narrow the width of the cleft jaw and lip. The sample size is stated at the study limit in the paper. Full article
(This article belongs to the Special Issue Craniofacial Imaging in Clinical Practice: Techniques, Innovations and Clinical Applications)
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