Intraoperative accuracy of non-invasive registration in computer assisted cranio–maxillofacial surgery

Abstract Background Computer-assisted surgery optimises accuracy and serves to improve precise surgical procedures. We validated oral splints with fiducial markers by testing them against rigid bone markers. Methods We screwed twenty bone anchors as fiducial markers into different regions of a dried skull and measured the distances. After computed tomography (CT) scanning, the accuracy was evaluated by determining the markers’ position using frameless stereotaxy on a dry cadaver and indicated on the CT scan. We compared the accuracy of chairside fabricated oral splints to standard registration with bone markers immediately after fabrication and after a ten-time use. Accuracy was calculated as deviation (mean ± standard deviation). For statistical analysis, t test, Kruskal-Wallis, Tukey's, and various linear regression models, such as the Pearson's product–moment correlation coefficient, were used. Results Oral splints showed an accuracy of 0.90 mm ± 0.27 for viscerocranium, 1.10 mm ± 0.39 for skull base, and 1.45 mm ± 0.59 for neurocranium. We found an accuracy of less than 2 mm for both splints for a distance of up to 152 mm. The accuracy persisted even after ten times removing and reattaching the splints. Conclusions Oral splints offer a non-invasive indicator to improve the accuracy of image-guided surgery. The precision is dependent on the distance to the target. Up to 150-mm distance, a precision of fewer than 2 mm is possible. Dental splints provide sufficient accuracy than bone markers and may opt for higher precision combined with other non-invasive registration methods.

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Automatic planning of paediatric craniofacial deformities: new virtual facial-symmetry operative detection

Journal of Pediatric and Adolescent Surgery ◽

10.46831/jpas.v1i2.55 ◽

2021 ◽

Vol 1 (2) ◽

Author(s):

Giuditta Mannelli ◽

Antonio Marzola ◽

Francesco Buonamici ◽

Yari Volpe ◽

Francesca Uccheddu ◽

...

Keyword(s):

Preoperative Planning ◽

Maxillofacial Surgery ◽

Central Element ◽

Patient Specific ◽

Surgical Approaches ◽

Computer Assisted ◽

Case Presentation ◽

Non Invasive ◽

Automatic Planning ◽

Orbital Region

Background: The correction of craniofacial deformities is an ongoing challenge in maxillofacial surgery. However, conventional measurement methods for treatment planning are not appropriate for craniofacial surgery. Computer-assisted approaches can improve surgical outcomes. A new, non-invasive, patient-specific automatic method, proposed here, has been tested for assisting the surgeon in preoperative planning. Case Presentation: In the case reported, the described method allows effective surgery planning that led to a significant decrease in asymmetries in the orbital region. Conclusion: The multidisciplinary collaborative approach is a central element for the construction of effective personalized procedures and for the conception of new surgical approaches. The here proposed technology offers a good level of feasibility and has an achievable potential for breakthroughs in the improvement of facial deformities surgical treatment, thus representing an overwhelmingly useful tool in a clinical setting.

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Computer-Assisted Navigation Surgery in Oral and Maxillofacial Surgery

Oral and Maxillofacial Surgery for the Clinician ◽

10.1007/978-981-15-1346-6_41 ◽

2021 ◽

pp. 841-862

Author(s):

Shintaro Sukegawa ◽

Takahiro Kanno

Keyword(s):

Maxillofacial Surgery ◽

Computer Assisted Surgery ◽

Oral And Maxillofacial Surgery ◽

Computer Assisted ◽

Navigation Systems ◽

Navigation Surgery ◽

Assisted Navigation ◽

Surgical Options ◽

Application Of Cas ◽

Patient Stress

AbstractComputer-assisted surgery (CAS) and navigation offers significant improvements in patient orientation and safety in every facet of our specialty of maxillofacial surgery. Ranging from precisely planned orthognathic procedures to the removal of foreign bodies requiring extremely flexible surgical options, and from minimally invasive dental implantology procedures to radical tumor resections of the skull base, they have made their mark for improving the procedure safety, predictability, and accuracy of surgery and options for intraoperative adaptations. In the future, the application of CAS is expected to further reduce operative risks and surgery time, accompanied by a considerable decrease in patient stress.Navigation systems are effective for delicate and accurate oral and maxillofacial surgery, neurosurgery, otolaryngology, and orthopedic surgery.This section presents an overview of available navigation systems and their applications with a focus on clinical utility and the solutions they offer for problems/challenges in the field of oral and maxillofacial surgery.

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ICA as Pattern Recognition Technique for Gesture Identification

Image Processing ◽

10.4018/978-1-4666-3994-2.ch027 ◽

2013 ◽

pp. 530-549

Author(s):

Ganesh Naik ◽

Dinesh Kant Kumar ◽

Sridhar Arjunan

Keyword(s):

Pattern Recognition ◽

Marked Improvement ◽

Mitigation Strategy ◽

The Other ◽

Computer Assisted ◽

Pattern Recognition Techniques ◽

Non Invasive ◽

Robust System ◽

Network Weight ◽

Recognition Technique

In recent times there is an urgent need for a simple yet robust system to identify natural hand actions and gestures for controlling prostheses and other computer assisted devices. Surface Electromyogram (sEMG) is a non-invasive measure of the muscle activities but is not reliable because there are multiple simultaneously active muscles. This research first establishes the conditions for the applicability of Independent Component Analysis (ICA) pattern recognition techniques for sEMG. Shortcomings related to order and magnitude ambiguity have been identified and a mitigation strategy has been developed by using a set of unmixing matrix and neural network weight matrix corresponding to the specific user. The experimental results demonstrate a marked improvement in the accuracy. The other advantages of this system are that it is suitable for real time operations and it is easy to train by a lay user.

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Application of Bio-Feedback in Neurodevelopmental Disorders

Emerging Trends in the Diagnosis and Intervention of Neurodevelopmental Disorders - Advances in Medical Technologies and Clinical Practice ◽

10.4018/978-1-5225-7004-2.ch011 ◽

2019 ◽

pp. 211-235

Author(s):

Srinivasan Venkatesan ◽

Hariharan Venkataraman

Keyword(s):

Cost Effectiveness ◽

Neurodevelopmental Disorders ◽

Conclusive Evidence ◽

Self Control ◽

Computer Assisted ◽

Evidence Based ◽

Confounding Factors ◽

Placebo Effects ◽

Non Invasive ◽

Bodily Function

Biofeedback is a non-invasive process to electronically monitor normal automatic bodily function to acquire its voluntary control. Traditional medical models place the onus on the physician to “cure” the illness. Biofeedback places responsibility on the patient to gain self-control. Its application as evidence-based practice in neurodevelopmental disorders is a nascent, unexplored, and debated area of study. This chapter outlines the meaning, nature, types, protocols, procedure, practices, challenges, benefits, and limitations in its use. Its history is traced for efficacy vis-à-vis other treatments, and other issues like cost-effectiveness, certification of professionals, gadget-enabled, and computer-assisted variants. Studies have attempted, albeit with methodological limitations, to validate its utility for neurodevelopmental disorders without any definitive or conclusive evidence for or against its use given the inability to replicate results, control or exclude confounding factors, placebo effects, and/or bias. An agenda for prospective research is given.

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ICA as Pattern Recognition Technique for Gesture Identification

Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-61350-429-1.ch020 ◽

2012 ◽

pp. 367-387

Author(s):

Ganesh Naik ◽

Dinesh Kant Kumar ◽

Sridhar Arjunan

Keyword(s):

Pattern Recognition ◽

Marked Improvement ◽

Mitigation Strategy ◽

The Other ◽

Computer Assisted ◽

Pattern Recognition Techniques ◽

Non Invasive ◽

Robust System ◽

Network Weight ◽

Recognition Technique

In recent times there is an urgent need for a simple yet robust system to identify natural hand actions and gestures for controlling prostheses and other computer assisted devices. Surface Electromyogram (sEMG) is a non-invasive measure of the muscle activities but is not reliable because there are multiple simultaneously active muscles. This research first establishes the conditions for the applicability of Independent Component Analysis (ICA) pattern recognition techniques for sEMG. Shortcomings related to order and magnitude ambiguity have been identified and a mitigation strategy has been developed by using a set of unmixing matrix and neural network weight matrix corresponding to the specific user. The experimental results demonstrate a marked improvement in the accuracy. The other advantages of this system are that it is suitable for real time operations and it is easy to train by a lay user.

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