Biplanar Low-Dose Radiograph Is Suitable for Cephalometric Analysis in Patients Requiring 3D Evaluation of the Whole Skeleton

Background: The biplanar 2D/3D X-ray technology (BPXR) is a 2D/3D imaging system allowing simultaneous stereo-corresponding posteroanterior (PA) and lateral 2D views of the whole body. The aim of our study was to assess the feasibility of cephalometric analysis based on the BPXR lateral skull view to accurately characterize facial morphology. Method: A total of 17 landmarks and 11 angles were placed and/or calculated on lateral BPXR and lateral cephalograms of 13 patients by three investigators. Five methods of angle identification were performed: the direct construction of straight lines on lateral cephalograms (LC-A) and on BPXR (BPXR-A), as well as the calculation of angles based on landmark identification on lateral cephalograms (LA-L) and on BPXR with the PA image (BPXR-LPA) or without (BPXR-L). Intra- and interoperator reliability of landmark identification and angle measurement of each method were calculated. To determine the most reliable method among the BPXR-based methods, their concordance with the reference method, LC-A, was evaluated. Results: Both imaging techniques had excellent intra- and interoperator reliability for landmark identification. On lateral BPXR, BPXR-A presented the best concordance with the reference method and a good intra- and interoperator reliability. Conclusion: BPXR provides a lateral view of the skull suitable for cephalometric analysis with good reliability.

Automated Motion Analysis of Bony Joint Structures from Dynamic Computer Tomography Images: A Multi-Atlas Approach

Dynamic computer tomography (CT) is an emerging modality to analyze in-vivo joint kinematics at the bone level, but it requires manual bone segmentation and, in some instances, landmark identification. The objective of this study is to present an automated workflow for the assessment of three-dimensional in vivo joint kinematics from dynamic musculoskeletal CT images. The proposed method relies on a multi-atlas, multi-label segmentation and landmark propagation framework to extract bony structures and detect anatomical landmarks on the CT dataset. The segmented structures serve as regions of interest for the subsequent motion estimation across the dynamic sequence. The landmarks are propagated across the dynamic sequence for the construction of bone embedded reference frames from which kinematic parameters are estimated. We applied our workflow on dynamic CT images obtained from 15 healthy subjects on two different joints: thumb base (n = 5) and knee (n = 10). The proposed method resulted in segmentation accuracies of 0.90 ± 0.01 for the thumb dataset and 0.94 ± 0.02 for the knee as measured by the Dice score coefficient. In terms of motion estimation, mean differences in cardan angles between the automated algorithm and manual segmentation, and landmark identification performed by an expert were below 1°. Intraclass correlation (ICC) between cardan angles from the algorithm and results from expert manual landmarks ranged from 0.72 to 0.99 for all joints across all axes. The proposed automated method resulted in reproducible and reliable measurements, enabling the assessment of joint kinematics using 4DCT in clinical routine.

Transopercular Insular Approach, Overcoming the Training Curve Using a Cadaveric Simulation Model: 2-Dimensional Operative Video

Transopercular approach to the insula is indicated for resection of insular low-grade gliomas, particularly for Yasargil's 3B, 5A, and 5B types. Nevertheless, the infrequent location and its challenging approach make it difficult to master the surgery. Consequently, a realistic laboratory training model might help to acquire key surgical skills. In this video, we describe a cadaveric-based model simulating the resection of a temporo-insular low-grade glioma. Kingler's fixation technique was used to fix the cadaver head before injecting red and blue colorants for a realistic vascular appearance. Hemisphere was frozen for white matter tract dissection. Tractography and intraoperative eloquent areas were extrapolated from a glioma patient by using a neuronavigation system. Then, a fronto-temporal craniotomy was performed through a question mark incision, exposing from inferior temporal gyrus up to middle frontal gyrus. After cortical anatomic landmark identification, eloquent areas were extrapolated creating a simulated functional cortical map. Then, transopercular noneloquent frontal and temporal corticectomies were performed, followed by subpial resection. Detailed identification of Sylvian vessels and insular cortex was demonstrated. Anatomic resection limits were exposed, and implicated white matter bundles, uncinate and fronto-occipital fascicles, were identified running through the temporal isthmus. Finally, a temporo-mesial resection was performed. In summary, this model provides a simple, cost-effective, and very realistic simulation of a transopercular approach to the insula, allowing the development of surgical skills needed to treat insular tumors in a safe environment. Besides, the integration of simulated navigation has proven useful in better understanding the complex white matter anatomy involved. Cadaver donation, subject or relatives, includes full consent for publication of the images. For the purpose of this video, no ethics committee approval was needed. Images correspond to a cadaver head donation. Cadaver donation, subject or relatives, includes full consent for any scientific purposes involving the corpse. The consent includes image or video recording. Regarding the intraoperative surgical video and tractography, the patient gave written consent for scientific divulgation prior to surgery.

Accuracy of Automatic Cephalometric Analysis Programs on Lateral Cephalograms of Preadolescent Children

The aim of this study was to evaluate the accuracy of 3 different automatic landmark identification programs on lateral cephalgrams and the clinical acceptability in pediatric dentistry. Sixty digital cephalometric radiographs of 7 to 12 years old healthy children were randomly selected. Fourteen landmarks were chosen for assessment and the mean of 3 measurements of each landmark by a single examiner was defined as the baseline landmarks. The mean difference between an automatically identified landmark and the baseline landmark was measured for each landmark on each image.The total mean difference of 3 automatic programs compared to the baseline landmarks were 2.53 ± 1.63 mm. Errors among 3 programs were not significantly different for 12 of 14 landmarks except Orbitale and Gonion. The automatic landmark identification programs showed significant higher mean detection errors than the manual method. The programs couldn’t be used as the 1st tool to replace human examiners. But considering short consuming time, these results indicate that all 3 programs have sufficient validity to be used in pediatric dental clinic.

Accuracy of Artificial Intelligence-Assisted Landmark Identification in Serial Lateral Cephalograms of Class III Patients Who Underwent Two-Jaw Orthognathic Surgery

To compare the accuracy of artificial intelligence-assisted landmark identification in serial lateral cephalograms of Class III patients who underwent two-jaw orthognathic surgery using a convolutional neural network (CNN) algorithm. 3,188 lateral cephalograms of Class III patients were allocated into the training and validation sets (3,004 cephalograms of 751 patients) and test set (184 cephalograms of 46 patients; subdivided into the genioplasty and non-genioplasty groups, n=23 per group)]. Each patient in the test set had four cephalograms: initial (T0), pre-surgery [T1, presence of orthodontic brackets (OBs)], post-surgery [T2, presence of OBs and surgical plates and screws (S-PS)], and debonding [T3, presence of S-PS and fixed retainers (FR)]. Statistical analysis was performed using mean errors of 20 landmarks between human gold standard and the CNN model. The total mean error was 1.17 mm without significant difference among four time-points. Before and after surgery, ANS, A point, and B point showed an increased error, while Mx6D and Md6D showed a decreased error. No difference in errors existed at B point, Pogonion, Menton, Md1C, and Md1R between the genioplasty and non-genioplasty groups. The CNN model can be used for landmark identification in serial cephalograms despite presence of OB, S-PS, FR, genioplasty, and bone remodeling.