Precision of manual landmark identification between as-received and oriented volume-rendered cone-beam computed tomography images.

INTRODUCTION: The objective of this study was to evaluate the effect of the orientation of cone-beam computed tomography (CBCT) images on the precision and reliability of 3-dimensional cephalometric landmark identification. METHODS: Ten CBCT scans were used for manual landmark identification. Volume-rendered images were oriented by aligning the Frankfort horizontal and transorbital planes horizontally, and the midsagittal plane vertically. A total of 20 CBCT images (10 as-received and 10 oriented) were anonymized, and 3 random sets were generated for manual landmark plotting by 3 expert orthodontists. Twenty-five landmarks were identified for plotting on each anonymized image independently. Hence, a total of 60 images were marked by the orthodontists. After landmark plotting, the randomized samples were decoded and regrouped into as-received and oriented data sets for analysis and comparison. Means and standard deviations of the x-, y-, and z-axis coordinates were calculated for each landmark to measure the central tendency. Intraclass correlation coefficients were calculated to analyze the interobserver reliability of landmark plotting in the 3 axes in both situations. Paired t tests were applied on the mean Euclidean distance computed separately for each landmark to evaluate the effect of 3-dimensional image orientation. RESULTS: Interobserver reliability (intraclass correlation coefficient, >0.9) was excellent for all 25 landmarks for the x-, y-, and z-axes on both before and after orientation of the images. Paired t test results showed insignificant differences for the orientation of volume-rendered images for all landmarks except 3: R1 left (P = 0.0138), sella (P = 0.0490), and frontozygomatic left (P = 0.0493). Also midline structures such as Bolton and nasion were plotted more consistently or precisely than bilateral structures. CONCLUSIONS: Orientation of the CBCT image does not enhance the precision of landmark plotting if each landmark is defined properly on multiplanar reconstruction slices and rendered images, and the clinician has sufficient training. The consistency of landmark identification is influenced by their anatomic locations on the midline, bilateral, and curved structures.

A machine learning-based approach to determine infection status in recipients of BBV152 (Covaxin) whole-virion inactivated SARS-CoV-2 vaccine for serological surveys.

Data science has been an invaluable part of the COVID-19 pandemic response with multiple applications, ranging from tracking viral evolution to understanding the vaccine effectiveness. Asymptomatic breakthrough infections have been a major problem in assessing vaccine effectiveness in populations globally. Serological discrimination of vaccine response from infection has so far been limited to Spike protein vaccines since whole virion vaccines generate antibodies against all the viral proteins. Here, we show how a statistical and machine learning (ML) based approach can be used to discriminate between SARS-CoV-2 infection and immune response to an inactivated whole virion vaccine (BBV152, Covaxin). For this, we assessed serial data on antibodies against Spike and Nucleocapsid antigens, along with age, sex, number of doses taken, and days since last dose, for 1823 Covaxin recipients. An ensemble ML model, incorporating a consensus clustering approach alongside the support vector machine model, was built on 1063 samples where reliable qualifying data existed, and then applied to the entire dataset. Of 1448 self-reported negative subjects, our ensemble ML model classified 724 to be infected. For method validation, we determined the relative ability of a random subset of samples to neutralize Delta versus wild-type strain using a surrogate neutralization assay. We worked on the premise that antibodies generated by a whole virion vaccine would neutralize wild type more efficiently than delta strain. In 100 of 156 samples, where ML prediction differed from self-reported uninfected status, neutralization against Delta strain was more effective, indicating infection. We found 71.8% subjects predicted to be infected during the surge, which is concordant with the percentage of sequences classified as Delta (75.6%-80.2%) over the same period. Our approach will help in real-world vaccine effectiveness assessments where whole virion vaccines are commonly used.

Insights from a Pan India Sero-Epidemiological survey (Phenome-India Cohort) for SARS-CoV2.

To understand the spread of SARS-CoV2, in August and September 2020, the Council of Scientific and Industrial Research (India) conducted a serosurvey across its constituent laboratories and centers across India. Of 10,427 volunteers, 1058 (10.14%) tested positive for SARS-CoV2 anti-nucleocapsid (anti-NC) antibodies, 95% of which had surrogate neutralization activity. Three-fourth of these recalled no symptoms. Repeat serology tests at 3 (n = 607) and 6 (n = 175) months showed stable anti-NC antibodies but declining neutralization activity. Local seropositivity was higher in densely populated cities and was inversely correlated with a 30-day change in regional test positivity rates (TPRs). Regional seropositivity above 10% was associated with declining TPR. Personal factors associated with higher odds of seropositivity were high-exposure work (odds ratio, 95% confidence interval, p value: 2.23, 1.92-2.59, <0.0001), use of public transport (1.79, 1.43-2.24, <0.0001), not smoking (1.52, 1.16-1.99, 0.0257), non-vegetarian diet (1.67, 1.41-1.99, <0.0001), and B blood group (1.36, 1.15-1.61, 0.001).

Automatic localization of three-dimensional cephalometric landmarks on CBCT images by extracting symmetry features of the skull.

To propose an algorithm for automatic localization of 3D cephalometric landmarks on CBCT data, those are useful for both cephalometric and upper airway volumetric analysis. 20 landmarks were targeted for automatic detection, of which 12 landmarks exist on the mid-sagittal plane. Automatic detection of mid-sagittal plane from the volume is a challenging task. Mid-sagittal plane is detected by extraction of statistical parameters of the symmetrical features of the skull. The mid-sagittal plane is partitioned into four quadrants based on the boundary definitions extracted from the human anatomy. Template matching algorithm is applied on the mid-sagittal plane to identify the region of interest ROI, further the edge features are extracted, to form contours in the individual regions. The landmarks are automatically localized by using the extracted knowledge of anatomical definitions of the landmarks. The overall mean error for detection of 20 landmarks was 1.88 mm with a standard deviation of 1.10 mm. The cephalometric land marks on CBCT data were detected automatically with in the mean error less than 2 mm.

Craniofacial and upper airway morphology in adult obstructive sleep apnea patients: A systematic review and meta-analysis of cephalometric studies.

Obstructive sleep apnea (OSA) is one of the common sleep breathing disorders in adults, characterised by frequent episodes of upper airway collapse during sleep. Craniofacial disharmony is an important risk factor for OSA. Overnight polysomnography (PSG) study is considered to be the most reliable confirmatory investigation for OSA diagnosis, whereas the precise localization of site of obstruction to the airflow cannot be detected. Identifying the cause of OSA in a particular ethnic population/individual subject helps to understand the etiological factors and effective management of OSA. The objective of the meta-analysis is to elucidate altered craniofacial anatomy on lateral cephalograms in adult subjects with established OSA. Significant weighted mean difference with insignificant heterogeneity was found for the following parameters: anterior lower facial height (ALFH: 2.48 mm), position of hyoid bone (Go-H: 5.45 mm, S-H: 6.89 mm, GoGn-H: 11.84°, GoGn-H: 7.22 mm, N-S-H: 2.14°), and pharyngeal airway space (PNS-Phw: -1.55 mm, pharyngeal space: -495.74 mm2 and oro-pharyngeal area: -151.15 mm2). Significant weighted mean difference with significant heterogeneity was found for the following parameters: cranial base (SN: -2.25 mm, S-N-Ba: -1.45°), position and length of mandible (SNB: -1.49° and Go-Me: -5.66 mm) respectively, maxillary length (ANS-PNS: -1.76 mm), tongue area (T: 366.51 mm2), soft palate area (UV: 125.02 mm2), and upper airway length (UAL: 5.39 mm). This meta-analysis supports the relationship between craniofacial disharmony and obstructive sleep apnea. There is a strong evidence for reduced pharyngeal airway space, inferiorly placed hyoid bone and increased anterior facial heights in adult OSA patients compared to control subjects. The cephalometric analysis provides insight into anatomical basis of the etiology of OSA that can influence making a choice of appropriate therapy.

A pilot study for segmentation of pharyngeal and sino-nasal airway subregions by automatic contour initialization.

PURPOSE: The objective of the present study is to put forward a novel automatic segmentation algorithm to segment pharyngeal and sino-nasal airway subregions on 3D CBCT imaging datasets. METHODS: A fully automatic segmentation of sino-nasal and pharyngeal airway subregions was implemented in MATLAB programing environment. The novelty of the algorithm is automatic initialization of contours in upper airway subregions. The algorithm is based on boundary definitions of the human anatomy along with shape constraints with an automatic initialization of contours to develop a complete algorithm which has a potential to enhance utility at clinical level. Post-initialization; five segmentation techniques: Chan-Vese level set (CVL), localized Chan-Vese level set (LCVL), Bhattacharya distance level set (BDL), Grow Cut (GC), and Sparse Field method (SFM) were used to test the robustness of automatic initialization. RESULTS: Precision and F-score were found to be greater than 80% for all the regions with all five segmentation methods. High precision and low recall were observed with BDL and GC techniques indicating an under segmentation. Low precision and high recall values were observed with CVL and SFM methods indicating an over segmentation. A Larger F-score value was observed with SFM method for all the subregions. Minimum F-score value was observed for naso-ethmoidal and sphenoidal air sinus region, whereas a maximum F-score was observed in maxillary air sinuses region. The contour initialization was more accurate for maxillary air sinuses region in comparison with sphenoidal and naso-ethmoid regions. CONCLUSION: The overall F-score was found to be greater than 80% for all the airway subregions using five segmentation techniques, indicating accurate contour initialization. Robustness of the algorithm needs to be further tested on severely deformed cases and on cases with different races and ethnicity for it to have global acceptance in Katradental radKatraiology workflow.

The reliability of different methods of manual volumetric segmentation of pharyngeal and sinonasal subregions.

OBJECTIVES: The purpose of the study was to test the intra and interobserver reliability of manual volumetric segmentation of pharyngeal and sinonasal airway subregions. STUDY DESIGN: Cone beam computed tomography data of 15 patients were collected from an orthodontic clinical database. Two experienced orthodontists independently performed manual segmentation of the airway subregions. Four performance measures were considered to test intra and interobserver reliability of manual segmentation: (1) volume correlation, (2) mean slice correlation, (3) percentage of volume difference, and (4) percentage of nonoverlapping voxels. RESULTS: Intra and interobserver reliability was observed to be greater than 0.96 for the entire pharyngeal and sinonasal airway sinus subregions by both observers using the volume correlation method. Mean slice correlation was found to be greater than 0.84, showing the existence of nonoverlapping voxels. Therefore, the percentage of nonoverlapping voxels was used as a reliability measure and was found to be less than 20% for both intra and interobserver markings. CONCLUSIONS: The mean slice correlation and percentage of nonoverlapping voxels were the most reliable performance measures of segmentation correctness. Volume correlation and the percentage of volume difference were observed to be the most reliable performance measures for volume correctness.

Accuracy of 3D cephalometric measurements based on an automatic knowledge-based landmark detection algorithm.

PURPOSE: To evaluate the accuracy of three-dimensional cephalometric measurements obtained through an automatic landmark detection algorithm compared to those obtained through manual identification. METHODS: The study demonstrates a comparison of 51 cephalometric measurements (28 linear, 16 angles and 7 ratios) on 30 CBCT (cone beam computed tomography) images. The analysis was performed to compare measurements based on 21 cephalometric landmarks detected automatically and those identified manually by three observers. RESULTS: Inter-observer ICC for each landmark was found to be excellent ([Formula: see text]) among three observers. The unpaired t-test revealed that there was no statistically significant difference in the measurements based on automatically detected and manually identified landmarks. The difference between the manual and automatic observation for each measurement was reported as an error. The highest mean error in the linear and angular measurements was found to be 2.63 mm ([Formula: see text] distance) and [Formula: see text] ([Formula: see text]-Me angle), respectively. The highest mean error in the group of distance ratios was 0.03 (for N-Me/N-ANS and [Formula: see text]). CONCLUSION: Cephalometric measurements computed from automatic detection of landmarks on 3D CBCT image were as accurate as those computed from manual identification.

A knowledge-based algorithm for automatic detection of cephalometric landmarks on CBCT images.

PURPOSE: Cone-beam computed tomography (CBCT) is now an established component for 3D evaluation and treatment planning of patients with severe malocclusion and craniofacial deformities. Precision landmark plotting on 3D images for cephalometric analysis requires considerable effort and time, notwithstanding the experience of landmark plotting, which raises a need to automate the process of 3D landmark plotting. Therefore, knowledge-based algorithm for automatic detection of landmarks on 3D CBCT images has been developed and tested. METHODS: A knowledge-based algorithm was developed in the MATLAB programming environment to detect 20 cephalometric landmarks. For the automatic detection, landmarks that are physically adjacent to each other were clustered into groups and were extracted through a volume of interest (VOI). Relevant contours were detected in the VOI and landmarks were detected using corresponding mathematical entities. The standard data for validation were generated using manual marking carried out by three orthodontists on a dataset of 30 CBCT images as a reference. RESULTS: Inter-observer ICC for manual landmark identification was found to be excellent (>0.9) amongst three observers. Euclidean distances between the coordinates of manual identification and automatic detection through the proposed algorithm of each landmark were calculated. The overall mean error for the proposed method was 2.01 mm with a standard deviation of 1.23 mm for all the 20 landmarks. The overall landmark detection accuracy was recorded at 64.67, 82.67 and 90.33 % within 2-, 3- and 4-mm error range of manual marking, respectively. CONCLUSIONS: The proposed knowledge-based algorithm for automatic detection of landmarks on 3D images was able to achieve relatively accurate results than the currently available algorithm.