Augmented reality in craniomaxillofacial surgery: added value and proposed recommendations through a systematic review of the literature

Benmahdjoub, Mohamed; Walsum, Theo van; Twisk, P. van; Wolvius, Eppo B.

doi:10.1016/j.ijom.2020.11.015

Cited by 26 publications

(19 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The application of SMMs has been reported frequently and demonstrated versatile usability for different research and clinical applications [ 38 , 39 ]; however, as of today, a shortcoming of this approach is the amount of data incorporated into a specific SSM due to limited availability of segmented image data. Last but not least, it can be expected that computer-assisted craniomaxillofacial surgery will be further promoted by the fast progress made in augmented/virtual reality applications in this field [ 40 ] and taking full advantage of these developments once more demands detailed and precise medical image segmentation.…”

Section: Discussionmentioning

confidence: 99%

Automated segmentation of head CT scans for computer-assisted craniomaxillofacial surgery applying a hierarchical patch-based stack of convolutional neural networks

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Automated segmentation of head CT scans for computer-assisted craniomaxillofacial surgery applying a hierarchical patch-based stack of convolutional neural networks

et al. 2022

View full text Add to dashboard Cite

show abstract

“…While AR has been applied for various purposes, including resections of tumors positioned at jaws, dental implant placement and root canals, cranial vault surgery, etc., [ 60 ] the application of AR/MR in jaw reconstruction is still in the pre-clinical development stage. The cadaveric study by Meng et al investigated the feasibility of using MR in mandible reconstruction with fibula flap.…”

Section: Future Perspectivesmentioning

confidence: 99%

Current Trends in the Reconstruction and Rehabilitation of Jaw following Ablative Surgery

Hakim

Melville

et al. 2022

Cancers

View full text Add to dashboard Cite

The reconstruction and rehabilitation of jaws following ablative surgery have been transformed in recent years by the development of computer-assisted surgery and virtual surgical planning. In this narrative literature review, we aim to discuss the current state-of-the-art jaw reconstruction, and to preview the potential future developments. The application of patient-specific implants and the “jaw-in-a-day technique” have made the fast restoration of jaws’ function and aesthetics possible. The improved efficiency of primary reconstructive surgery allows for the rehabilitation of neurosensory function following ablative surgery. Currently, a great deal of research has been conducted on augmented/mixed reality, artificial intelligence, virtual surgical planning for soft tissue reconstruction, and the rehabilitation of the stomatognathic system. This will lead to an even more exciting future for the functional reconstruction and rehabilitation of the jaw following ablative surgery.

show abstract

“…Unlike Virtual Reality (VR), which creates a totally computer-generated artificial environment, AR uses the real environment and overlays new virtual information on top of it, thus providing a composite view that enhances the user’s sensory perception of the world. Tools and systems employing AR have been designed and tested in the context of several medical applications [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ], including surgical navigation in neurosurgery [ 14 ], craniomaxillofacial surgery [ 1 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ], and head and neck oncology [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Preclinical Application of Augmented Reality in Pediatric Craniofacial Surgery: An Accuracy Study

Ruggiero

Cercenelli

Emiliani

et al. 2023

JCM

View full text Add to dashboard Cite

Background: Augmented reality (AR) allows the overlapping and integration of virtual information with the real environment. The camera of the AR device reads the object and integrates the virtual data. It has been widely applied to medical and surgical sciences in recent years and has the potential to enhance intraoperative navigation. Materials and methods: In this study, the authors aim to assess the accuracy of AR guidance when using the commercial HoloLens 2 head-mounted display (HMD) in pediatric craniofacial surgery. The Authors selected fronto-orbital remodeling (FOR) as the procedure to test (specifically, frontal osteotomy and nasal osteotomy were considered). Six people (three surgeons and three engineers) were recruited to perform the osteotomies on a 3D printed stereolithographic model under the guidance of AR. By means of calibrated CAD/CAM cutting guides with different grooves, the authors measured the accuracy of the osteotomies that were performed. We tested accuracy levels of ±1.5 mm, ±1 mm, and ±0.5 mm. Results: With the HoloLens 2, the majority of the individuals involved were able to successfully trace the trajectories of the frontal and nasal osteotomies with an accuracy level of ±1.5 mm. Additionally, 80% were able to achieve an accuracy level of ±1 mm when performing a nasal osteotomy, and 52% were able to achieve an accuracy level of ±1 mm when performing a frontal osteotomy, while 61% were able to achieve an accuracy level of ±0.5 mm when performing a nasal osteotomy, and 33% were able to achieve an accuracy level of ±0.5 mm when performing a frontal osteotomy. Conclusions: despite this being an in vitro study, the authors reported encouraging results for the prospective use of AR on actual patients.

show abstract

Augmented reality in craniomaxillofacial surgery: added value and proposed recommendations through a systematic review of the literature

Cited by 26 publications

References 56 publications

Automated segmentation of head CT scans for computer-assisted craniomaxillofacial surgery applying a hierarchical patch-based stack of convolutional neural networks

Automated segmentation of head CT scans for computer-assisted craniomaxillofacial surgery applying a hierarchical patch-based stack of convolutional neural networks

Current Trends in the Reconstruction and Rehabilitation of Jaw following Ablative Surgery

Preclinical Application of Augmented Reality in Pediatric Craniofacial Surgery: An Accuracy Study

Contact Info

Product

Resources

About