Accuracy of generic mesh conformation: the future of facial morphological analysis

Three-dimensional (3D) analysis of the face is required for the assessment of changes following surgery, to monitor the progress of pathological conditions and for the evaluation of facial growth. Sophisticated methods have been applied for the evaluation of facial morphology, the most common being dense surface correspondence. The method depends on the application of a mathematical facial mask known as the generic facial mesh for the evaluation of the characteristics of facial morphology. This study evaluated the accuracy of the conformation of generic mesh to the underlying facial morphology. The study was conducted on 10 non-patient volunteers. Thirty-four 2-mm-diameter self-adhesive, non-reflective markers were placed on each face. These were readily identifiable on the captured 3D facial image, which was captured by Di3D stereophotogrammetry. The markers helped in minimising digitisation errors during the conformation process. For each case, the face was captured six times: at rest and at the maximum movements of four facial expressions. The 3D facial image of each facial expression was analysed. Euclidean distances between the 19 corresponding landmarks on the conformed mesh and on the original 3D facial model provided a measure of the accuracy of the conformation process. For all facial expressions and all corresponding landmarks, these distances were between 0.7 and 1.7 mm. The absolute mean distances ranged from 0.73 to 1.74 mm. The mean absolute error of the conformation process was 1.13 ± 0.26 mm. The conformation of the generic facial mesh is accurate enough for clinical trial proved to be accurate enough for the analysis of the captured 3D facial images

Let’s Face It: The effect of orthognathic surgery on facial recognition algorithm analysis

Aim: To evaluate the ability of a publicly available facial recognition application program interface (API) to calculate similarity scores for pre- and post-surgical photographs of patients undergoing orthognathic surgeries. Our primary objective was to identify which surgical procedure(s) had the greatest effect(s) on similarity score. Methods: Standard treatment progress photographs for 25 retrospectively identified, orthodontic-orthognathic patients were analyzed using the API to calculate similarity scores between the pre- and post-surgical photographs. Photographs from two pre-surgical timepoints were compared as controls. Both relaxed and smiling photographs were included in the study to assess for the added impact of facial pose on similarity score. Surgical procedure(s) performed on each patient, gender, age at time of surgery, and ethnicity were recorded for statistical analysis. Nonparametric Kruskal-Wallis Rank Sum Tests were performed to univariately analyze the relationship between each categorical patient characteristic and each recognition score. Multiple comparison Wilcoxon Rank Sum Tests were performed on the subsequent statistically significant characteristics. P-Values were adjusted for using the Bonferroni correction technique. Results: Patients that had surgery on both jaws had a lower median similarity score, when comparing relaxed expressions before and after surgery, compared to those that had surgery only on the mandible (p = 0.014). It was also found that patients receiving LeFort and bilateral sagittal split osteotomies (BSSO) surgeries had a lower median similarity score compared to those that received only BSSO (p = 0.009). For the score comparing relaxed expressions before surgery versus smiling expressions after surgery, patients receiving two-jaw surgeries had lower scores than those that had surgery on only the mandible (p = 0.028). Patients that received LeFort and BSSO surgeries were also found to have lower similarity scores compared to patients that received only BSSO when comparing pre-surgical relaxed photographs to post-surgical smiling photographs (p = 0.036). Conclusions: Two-jaw surgeries were associated with a statistically significant decrease in similarity score when compared to one-jaw procedures. Pose was also found to be a factor influencing similarity scores, especially when comparing pre-surgical relaxed photographs to post-surgical smiling photographs

Physical and statistical shape modelling in craniomaxillofacial surgery: a personalised approach for outcome prediction

Orthognathic surgery involves repositioning of the jaw bones to restore face function and shape for patients who require an operation as a result of a syndrome, due to growth disturbances in childhood or after trauma. As part of the preoperative assessment, three-dimensional medical imaging and computer-assisted surgical planning help to improve outcomes, and save time and cost. Computer-assisted surgical planning involves visualisation and manipulation of the patient anatomy and can be used to aid objective diagnosis, patient communication, outcome evaluation, and surgical simulation. Despite the benefits, the adoption of three-dimensional tools has remained limited beyond specialised hospitals and traditional two-dimensional cephalometric analysis is still the gold standard. This thesis presents a multidisciplinary approach to innovative surgical simulation involving clinical patient data, medical image analysis, engineering principles, and state-of-the-art machine learning and computer vision algorithms. Two novel three-dimensional computational models were developed to overcome the limitations of current computer-assisted surgical planning tools. First, a physical modelling approach – based on a probabilistic finite element model – provided patient-specific simulations and, through training and validation, population-specific parameters. The probabilistic model was equally accurate compared to two commercial programs whilst giving additional information regarding uncertainties relating to the material properties and the mismatch in bone position between planning and surgery. Second, a statistical modelling approach was developed that presents a paradigm shift in its modelling formulation and use. Specifically, a 3D morphable model was constructed from 5,000 non-patient and orthognathic patient faces for fully-automated diagnosis and surgical planning. Contrary to traditional physical models that are limited to a finite number of tests, the statistical model employs machine learning algorithms to provide the surgeon with a goal-driven patient-specific surgical plan. The findings in this thesis provide markers for future translational research and may accelerate the adoption of the next generation surgical planning tools to further supplement the clinical decision-making process and ultimately to improve patients’ quality of life

AI-Enhanced Intensive Care Unit: Revolutionizing Patient Care with Pervasive Sensing

The intensive care unit (ICU) is a specialized hospital space where critically ill patients receive intensive care and monitoring. Comprehensive monitoring is imperative in assessing patients conditions, in particular acuity, and ultimately the quality of care. However, the extent of patient monitoring in the ICU is limited due to time constraints and the workload on healthcare providers. Currently, visual assessments for acuity, including fine details such as facial expressions, posture, and mobility, are sporadically captured, or not captured at all. These manual observations are subjective to the individual, prone to documentation errors, and overburden care providers with the additional workload. Artificial Intelligence (AI) enabled systems has the potential to augment the patient visual monitoring and assessment due to their exceptional learning capabilities. Such systems require robust annotated data to train. To this end, we have developed pervasive sensing and data processing system which collects data from multiple modalities depth images, color RGB images, accelerometry, electromyography, sound pressure, and light levels in ICU for developing intelligent monitoring systems for continuous and granular acuity, delirium risk, pain, and mobility assessment. This paper presents the Intelligent Intensive Care Unit (I2CU) system architecture we developed for real-time patient monitoring and visual assessment

A pilot study for the digital replacement of a distorted dentition acquired by Cone Beam Computed Tomography (CBCT)

Abstract Introduction: Cone beam CT (CBCT) is becoming a routine imaging modality designed for the maxillofacial region. Imaging patients with intra-oral metallic objects cause streak artefacts. Artefacts impair any virtual model by obliterating the teeth. This is a major obstacle for occlusal registration and the fabrication of orthognathic wafers to guide the surgical correction of dentofacial deformities. Aims and Objectives: To develop a method of replacing the inaccurate CBCT images of the dentition with an accurate representation and test the feasibility of the technique in the clinical environment. Materials and Method: Impressions of the teeth are acquired and acrylic baseplates constructed on dental casts incorporating radiopaque registration markers. The appliances are fitted and a preoperative CBCT is performed. Impressions are taken of the dentition with the devices in situ and subsequent dental models produced. The models are scanned to produce a virtual model. Both images of the patient and the model are imported into a virtual reality software program and aligned on the virtual markers. This allows the alignment of the dentition without relying on the teeth for superimposition. The occlusal surfaces of the dentition can be replaced with the occlusal image of the model. Results: The absolute mean distance of the mesh between the markers in the skulls was in the region of 0.09mm ± 0.03mm; the replacement dentition had an absolute mean distance of about 0.24mm ± 0.09mm. In patients the absolute mean distance between markers increased to 0.14mm ± 0.03mm. It was not possible to establish the discrepancies in the patient’s dentition, since the original image of the dentition is inherently inaccurate. Conclusion: It is possible to replace the CBCT virtual dentition of cadaveric skulls with an accurate representation to create a composite skull. The feasibility study was successful in the clinical arena. This could be a significant advancement in the accuracy of surgical prediction planning, with the ultimate goal of fabrication of a physical orthognathic wafer using reverse engineering