Solution for the External Contour Changes in Cone Beam Computed Tomography-Guided On-demand Online Adaptive Radiotherapy for a Patient With Very Advanced Head and Neck Cancer: A Technical Case Report.

This article presents a case of a patient with advanced head and neck cancer, characterized by a large and protruding tumor. The patient was treated with an innovative on-demand online adaptive radiotherapy (ART) technology, guided by cone beam computed tomography (CBCT), on the Ethos adaptive radiotherapy platform (version 1.0, Varian Medical Systems, Palo Alto, CA). A solution was provided for this special case to address the issue where part of the target volume could not participate in the optimization due to exceeding the external contour boundary during online adaptive radiotherapy. The treatment outcome was satisfactory in terms of tumor regression, while only grade 1 radiodermatitis and grade 2 oral mucositis at the end of radiotherapy. This article discusses the clinical diagnosis, treatment process, and follow-up of this case, aiming to provide clinical references for a broader application of this technology.

Effect of body contour changes on the setup and dosimetric accuracy of radiotherapy after cervical cancer surgery.

Purpose: The body contour of patients with cervical cancer is prone to change between radiotherapy sessions. This study aimed to investigate the effect of body contour changes on the setup and dosimetric accuracy of radiotherapy. Methods: 15 patients with cervical cancer after surgery were randomly selected for retrospective analysis. The body contours on the once-per-week cone-beam computed tomography (CBCT) were registered to the planning CT (pCT) for subsequent evaluation. A body contour conformity index (CIbody) was defined to quantify the variation of body changes. The body volume measured by CBCT was collected, and its relative difference in reference with the first CBCT was calculated and denoted by ΔVn. The relative setup errors, denoted by ΔSELR, ΔSEAP, ΔSESI, and ΔSEvec for left-right, anterior-posterior, superior-inferior, and vectorial shifts, respectively, were defined as the difference in measured setup errors between the reference and following CBCTs. The planned dose was calculated on the basis of virtual CT generated from CBCT and pCT by altering the CT body contour to fit the body on CBCT without deformable registration. The correlations between body contour changes and relative setup errors as well as dosimetric parameters were evaluated using Spearman's correlation coefficient rs . Results: CIbody was found to be negatively correlated with the superior-inferior and vectorial relative setup errors ΔSESI (rs = -0.448, p = 0.001) and ΔSEvec (rs = -0.387, p = 0.002), and no significant correlation was found between relative setup errors and ΔVn. Moreover, ΔVn was negatively correlated with ΔD2 (rs = -0.829, p < 0.001), ΔD98 (rs = -0.797, p < 0.001), and ΔTVPIV (rs = -0.819, p < 0.001). ΔD2, ΔD98, and ΔTVPIV were negatively correlated with ΔVn (p < 0.005). No correlation was found for other examined dosimetric parameters. Conclusion: The body contour change of patients could be associated with the setup variability. The effect of body contour changes on dose distribution is minimal. The extent of body change could be used as a metric for radiation therapists to estimate the setup errors.

CA-ViT: Contour-Guided and Augmented Vision Transformers to Enhance Glaucoma Classification Using Fundus Images.

Glaucoma, a predominant cause of visual impairment on a global scale, poses notable challenges in diagnosis owing to its initially asymptomatic presentation. Early identification is vital to prevent irreversible vision impairment. Cutting-edge deep learning techniques, such as vision transformers (ViTs), have been employed to tackle the challenge of early glaucoma detection. Nevertheless, limited approaches have been suggested to improve glaucoma classification due to issues like inadequate training data, variations in feature distribution, and the overall quality of samples. Furthermore, fundus images display significant similarities and slight discrepancies in lesion sizes, complicating glaucoma classification when utilizing ViTs. To address these obstacles, we introduce the contour-guided and augmented vision transformer (CA-ViT) for enhanced glaucoma classification using fundus images. We employ a Conditional Variational Generative Adversarial Network (CVGAN) to enhance and diversify the training dataset by incorporating conditional sample generation and reconstruction. Subsequently, a contour-guided approach is integrated to offer crucial insights into the disease, particularly concerning the optic disc and optic cup regions. Both the original images and extracted contours are given to the ViT backbone; then, feature alignment is performed with a weighted cross-entropy loss. Finally, in the inference phase, the ViT backbone, trained on the original fundus images and augmented data, is used for multi-class glaucoma categorization. By utilizing the Standardized Multi-Channel Dataset for Glaucoma (SMDG), which encompasses various datasets (e.g., EYEPACS, DRISHTI-GS, RIM-ONE, REFUGE), we conducted thorough testing. The results indicate that the proposed CA-ViT model significantly outperforms current methods, achieving a precision of 93.0%, a recall of 93.08%, an F1 score of 92.9%, and an accuracy of 93.0%. Therefore, the integration of augmentation with the CVGAN and contour guidance can effectively enhance glaucoma classification tasks.

Diaphragm Tracking System That Superimposes Planned Diaphragm Contours on Kilovolt Cine Images During Multiple Breath-Hold Volumetric Modulated Arc Therapy for Abdominal Tumors.

We recently published a phantom validation of our diaphragm tracking system, DiaTrak, on an Elekta linear accelerator with an integrated cone-beam computed tomography (CBCT) unit for multiple breath-hold volumetric modulated arc therapy of abdominal tumors, where the diaphragm position was compared between digitally reconstructed radiography (DRR) and kilovolt (kV) projection streaming images by template matching. In the present report, the visual feedback of the diaphragm position was added to the reported system. DICOM-RT diaphragm contour data were additionally exported from a treatment planning system to the DiaTrak PC. Following phantom localization by registering the CBCT to the planning CT images, a projected diaphragm contour was overlaid on each DRR image, whereas another two projected diaphragm contours were superimposed on each kV projection cine image every 180 ms after shifting ±5 mm (set as breath-hold tolerance) in the craniocaudal direction during gantry rotation. It was visually confirmed that the projected diaphragm surface was observed within the two contour lines on the kV cine window. The diaphragm registration errors of the localized phantom were also calculated based on image cross-correlation between the DRR and the projection cine images every 180 ms. It was found that the mean diaphragm registration error was -0.29 mm with a standard deviation of 0.32 mm during the gantry rotation. In conclusion, a new interface for the 5 mm tolerance check was proposed to provide direct visual feedback, thereby giving a sense of assurance to the attending radiotherapy technologists. The calculated diaphragm registration errors were relatively small compared to the tolerance of 5 mm, and therefore it is considered clinically acceptable.

Vascular liver segmentation: a narrative review on methods and new insights brought by artificial intelligence.

Liver vessel segmentation from routinely performed medical imaging is a useful tool for diagnosis, treatment planning and delivery, and prognosis evaluation for many diseases, particularly liver cancer. A precise representation of liver anatomy is crucial to define the extent of the disease and, when suitable, the consequent resective or ablative procedure, in order to guarantee a radical treatment without sacrificing an excessive volume of healthy liver. Once mainly performed manually, with notable cost in terms of time and human energies, vessel segmentation is currently realized through the application of artificial intelligence (AI), which has gained increased interest and development of the field. Many different AI-driven models adopted for this aim have been described and can be grouped into different categories: thresholding methods, edge- and region-based methods, model-based methods, and machine learning models. The latter includes neural network and deep learning models that now represent the principal algorithms exploited for vessel segmentation. The present narrative review describes how liver vessel segmentation can be realized through AI models, with a summary of model results in terms of accuracy, and an overview on the future progress of this topic.

The separation between mRNA-ends is more variable than expected.

Effective circularization of mRNA molecules is a key step for the efficient initiation of translation. Research has shown that the intrinsic separation of the ends of mRNA molecules is rather small, suggesting that intramolecular arrangements could provide this effective circularization. Considering that the innate proximity of RNA ends might have important unknown biological implications, we aimed to determine whether the close proximity of the ends of mRNA molecules is a conserved feature across organisms and gain further insights into the functional effects of the proximity of RNA ends. To do so, we studied the secondary structure of 274 full native mRNA molecules from 17 different organisms to calculate the contour length (CL) of the external loop as an index of their end-to-end separation. Our computational predictions show bigger variations (from 0.59 to 31.8 nm) than previously reported and also than those observed in random sequences. Our results suggest that separations larger than 18.5 nm are not favored, whereas short separations could be related to phenotypical stability. Overall, our work implies the existence of a biological mechanism responsible for the increase in the observed variability, suggesting that the CL features of the exterior loop could be relevant for the initiation of translation and that a short CL could contribute to the stability of phenotypes.

Non-invasive follow-up for intracranial aneurysms treated with contour neurovascular system-comparison of digital subtraction angiography (DSA) to magnetic resonance imaging (MRI) and spectral computed tomography angiography (CTA) in vitro.

PURPOSE: The contour neurovascular system (CNS) is an intrasaccular flow-disrupting device designed for the treatment of intracranial wide-necked bifurcation aneurysms. Metal artifacts limit magnetic resonance imaging (MRI) accessibility after implantation. The purpose of this in vitro study was to evaluate non-invasive imaging alternatives to digital subtraction angiography (DSA). MATERIAL AND METHODS: Three aneurysms of patients originally treated with CNS were three-dimensional (3D)-printed (one at the basilar tip and two at the middle cerebral artery bifurcation). CNS devices were implanted under fluoroscopic control into the 3D models. Post-implantation two-dimensional-DSA, flat panel computed tomography angiography (CTA), MRI, and spectral CTA were performed. RESULTS: Time of flight angiography and T1 weighted sequences showed large susceptibility artifacts at the detachment zone of the devices. A thin-sliced T2 weighted sequence in cross-sectional orientation to the aneurysm allowed visualization of the aneurysm dome, but the aneurysm neck and parent vessel could not be assessed. Focused spectral CTA, especially a 40 keV reconstruction with a metal artifact reduction algorithm (orthopedic metal artifact reduction (OMAR)), showed only minor artifacts at the detachment zone. This approach achieved a very similar result to DSA and flat panel computed tomography, enabling the assessment of the device structure, aneurysm perfusion, and parent vessel perfusion. DISCUSSION AND CONCLUSION: For non-invasive follow-up of CNS, focused 40 keV CTA with OMAR seems to be a valuable option. MRI can be valuable for larger aneurysms to assess the aneurysm dome, but was not suitable for evaluating the parent vessels and aneurysm neck after CNS implantation in this study.

Geometric and dosimetric evaluation for breast and regional nodal auto-segmentation structures.

The accuracy of artificial intelligence (AI) generated contours for intact-breast and post-mastectomy radiotherapy plans was evaluated. Geometric and dosimetric comparisons were performed between auto-contours (ACs) and manual-contours (MCs) produced by physicians for target structures. Breast and regional nodal structures were manually delineated on 66 breast cancer patients. ACs were retrospectively generated. The characteristics of the breast/post-mastectomy chestwall (CW) and regional nodal structures (axillary [AxN], supraclavicular [SC], internal mammary [IM]) were geometrically evaluated by Dice similarity coefficient (DSC), mean surface distance, and Hausdorff Distance. The structures were also evaluated dosimetrically by superimposing the MC clinically delivered plans onto the ACs to assess the impact of utilizing ACs with target dose (Vx%) evaluation. Positive geometric correlations between volume and DSC for intact-breast, AxN, and CW were observed. Little or anti correlations between volume and DSC for IM and SC were shown. For intact-breast plans, insignificant dosimetric differences between ACs and MCs were observed for AxNV95% (p = 0.17) and SCV95% (p = 0.16), while IMNV90% ACs and MCs were significantly different. The average V95% for intact-breast MCs (98.4%) and ACs (97.1%) were comparable but statistically different (p = 0.02). For post-mastectomy plans, AxNV95% (p = 0.35) and SCV95% (p = 0.08) were consistent between ACs and MCs, while IMNV90% was significantly different. Additionally, 94.1% of AC-breasts met ΔV95% variation <5% when DSC > 0.7. However, only 62.5% AC-CWs achieved the same metrics, despite AC-CWV95% (p = 0.43) being statistically insignificant. The AC intact-breast structure was dosimetrically similar to MCs. The AC AxN and SC may require manual adjustments. Careful review should be performed for AC post-mastectomy CW and IMN before treatment planning. The findings of this study may guide the clinical decision-making process for the utilization of AI-driven ACs for intact-breast and post-mastectomy plans. Before clinical implementation of this auto-segmentation software, an in-depth assessment of agreement with each local facilities MCs is needed.

Excessive posterior placement of upper instrumented vertebra relative to lower instrumented vertebra as a predictor of proximal junction kyphosis after selective spinal fusion for adolescent idiopathic scoliosis Lenke type 5C curves.

PURPOSE: We defined sagittal S-line tilt (SSLT) as the tilt of the line connecting the upper instrumented vertebra and the lower instrumented vertebra. This study aimed to: (1) examine the correlation between SSLT and proximal junctional angle (PJA) change values, and (2) determine the cut-off value of SSLT with respect to proximal junctional kyphosis (PJK) occurrence. METHODS: Eighty-six consecutive patients (81 female and 5 male; mean age: 15.8 years) with Lenke 5C AIS who underwent posterior selective spinal fusion. Pearson's correlation coefficients were used to examine the relationship between preoperative SSLT and changes in PJA from preoperative to 2 years postoperative. The impact of SSLT on PJK at 2 years after surgery was assessed using a receiver operating characteristic (ROC) curve. RESULTS: We observed a moderate positive correlation between preoperative SSLT and change in PJA (R = 0.541, P < 0.001). We identified 18 patients (21%) with PJK at 2 years postoperative. Mean preoperative SSLT in the PJK group and the non-PJK group differed significantly at 23.3 ± 4.1° and 16.1 ± 5.0°, respectively (P < 0.001). The cut-off value of preoperative SSLT for PJK at 2 years postoperative was 18° in ROC curve analysis, with a sensitivity of 94%, specificity of 68%, and area under the ROC curve of 0.868. CONCLUSION: In selective lumbar fusion for AIS Lenke type 5C curves, preoperative SSLT was significantly correlated with PJA change from preoperative to 2 years postoperative. SSLT was a predictor of PJK occurrence, with a cut-off value of 18°.

GC Snakes: An Efficient and Robust Segmentation Model for Hot Forging Images.

Machine vision is a desirable non-contact measurement method for hot forgings, as image segmentation has been a challenging issue in performance and robustness resulting from the diversity of working conditions for hot forgings. Thus, this paper proposes an efficient and robust active contour model and corresponding image segmentation approach for forging images, by which verification experiments are conducted to prove the performance of the segmentation method by measuring geometric parameters for forging parts. Specifically, three types of continuity parameters are defined based on the geometric continuity of equivalent grayscale surfaces for forging images; hence, a new image force and external energy functional are proposed to form a new active contour model, Geometric Continuity Snakes (GC Snakes), which is more percipient to the grayscale distribution characteristics of forging images to improve the convergence for active contour robustly; additionally, a generating strategy for initial control points for GC Snakes is proposed to compose an efficient and robust image segmentation approach. The experimental results show that the proposed GC Snakes has better segmentation performance compared with existing active contour models for forging images of different temperatures and sizes, which provides better performance and efficiency in geometric parameter measurement for hot forgings. The maximum positioning and dimension errors by GC Snakes are 0.5525 mm and 0.3868 mm, respectively, compared with errors of 0.7873 mm and 0.6868 mm by the Snakes model.

A Hands-on Guide to AmoePy - a Python-Based Software Package to Analyze Cell Migration Data.

Amoeboid cell motility is fundamental for a multitude of biological processes such as embryogenesis, immune responses, wound healing, and cancer metastasis. It is characterized by specific cell shape changes: the extension and retraction of membrane protrusions, known as pseudopodia. A common approach to investigate the mechanisms underlying this type of cell motility is to study phenotypic differences in the locomotion of mutant cell lines. To characterize such differences, methods are required to quantify the contour dynamics of migrating cells. AmoePy is a Python-based software package that provides tools for cell segmentation, contour detection as well as analyzing and simulating contour dynamics. First, a digital representation of the cell contour as a chain of nodes is extracted from each frame of a time-lapse microscopy recording of a moving cell. Then, the dynamics of these nodes-referred to as virtual markers-are tracked as the cell contour evolves over time. From these data, various quantities can be calculated that characterize the contour dynamics, such as the displacement of the virtual markers or the local stretching rate of the marker chain. Their dynamics is typically visualized in space-time plots, the so-called kymographs, where the temporal evolution is displayed for the different locations along the cell contour. Using AmoePy, you can straightforwardly create kymograph plots and videos from stacks of experimental bright-field or fluorescent images of motile cells. A hands-on guide on how to install and use AmoePy is provided in this chapter.

Efficient model-informed co-segmentation of tumors on PET/CT driven by clustering and classification information.

Automatic tumor segmentation via positron emission tomography (PET) and computed tomography (CT) images plays a critical role in the prevention, diagnosis, and treatment of this disease via radiation oncology. However, segmenting these tumors is challenging due to the heterogeneity of grayscale levels and fuzzy boundaries. To address these issues, in this paper, an efficient model-informed PET/CT tumor co-segmentation method that combines fuzzy C-means clustering and Bayesian classification information is proposed. To alleviate the grayscale heterogeneity of multi-modal images, in this method, a novel grayscale similar region term is designed based on the background region information of PET and the foreground region information of CT. An edge stop function is innovatively presented to enhance the localization of fuzzy edges by incorporating the fuzzy C-means clustering strategy. To improve the segmentation accuracy further, a unique data fidelity term is introduced based on PET images by combining the distribution characteristics of pixel points in PET images. Finally, experimental validation on datasets of head and neck tumor (HECKTOR) and non-small cell lung cancer (NSCLC) demonstrated impressive values for three key evaluation metrics, including DSC, RVD and HD5, achieved impressive values of 0.85, 5.32, and 0.17, respectively. These compelling results indicate that image segmentation methods based on mathematical models exhibit outstanding performance in handling grayscale heterogeneity and fuzzy boundaries in multi-modal images.

Deep learning with uncertainty estimation for automatic tumor segmentation in PET/CT of head and neck cancers: impact of model complexity, image processing and augmentation.

Objective.Target volumes for radiotherapy are usually contoured manually, which can be time-consuming and prone to inter- and intra-observer variability. Automatic contouring by convolutional neural networks (CNN) can be fast and consistent but may produce unrealistic contours or miss relevant structures. We evaluate approaches for increasing the quality and assessing the uncertainty of CNN-generated contours of head and neck cancers with PET/CT as input.Approach.Two patient cohorts with head and neck squamous cell carcinoma and baseline18F-fluorodeoxyglucose positron emission tomography and computed tomography images (FDG-PET/CT) were collected retrospectively from two centers. The union of manual contours of the gross primary tumor and involved nodes was used to train CNN models for generating automatic contours. The impact of image preprocessing, image augmentation, transfer learning and CNN complexity, architecture, and dimension (2D or 3D) on model performance and generalizability across centers was evaluated. A Monte Carlo dropout technique was used to quantify and visualize the uncertainty of the automatic contours.Main results. CNN models provided contours with good overlap with the manually contoured ground truth (median Dice Similarity Coefficient: 0.75-0.77), consistent with reported inter-observer variations and previous auto-contouring studies. Image augmentation and model dimension, rather than model complexity, architecture, or advanced image preprocessing, had the largest impact on model performance and cross-center generalizability. Transfer learning on a limited number of patients from a separate center increased model generalizability without decreasing model performance on the original training cohort. High model uncertainty was associated with false positive and false negative voxels as well as low Dice coefficients.Significance.High quality automatic contours can be obtained using deep learning architectures that are not overly complex. Uncertainty estimation of the predicted contours shows potential for highlighting regions of the contour requiring manual revision or flagging segmentations requiring manual inspection and intervention.

The effect of shape on visual size perception.

The Delboeuf illusion occurs when two circles (test figures) of equal radius are placed side by side and surrounded by concentric circles (inducers) of varying radii, resulting in the test figure being misestimated depending on the size of the surrounding inducer. This study conducted three experiments to explore the impact of shape and the contour attraction and parallel attraction on the Delboeuf illusion for different shapes. In Experiment 1 (n = 64), the test figures remained as circles while the inducers varied in shape. Experiment 2 (n = 64) involved simultaneous changes in the shape of both the test figures and the inducers. Experiment 3 (n = 64) replicated Experiment 2, with the exception that the areas of the inducers were equal and the distances between the inducers and the test figures were also equal. We conclude that the shape of the inducer and the test figure had an impact on the visual size perception, and in the magnitude of the Delboeuf illusion, varied depending on contour attraction. Configurations with circles or shapes resembling circles exhibit contour attraction, while configurations with shapes possessing longer parallel lines shift toward parallel attraction, both attractions enhance the perceived magnitude of the Delboeuf illusion.

Mandibular Gender Dimorphism: The Utility of Artificial Intelligence and Statistical Shape Modeling in Skeletal Facial Analysis.

BACKGROUND: In gender-affirming surgery, facial skeletal dimorphism is an important topic for every craniofacial surgeon. Few cephalometric studies have assessed this topic; however, they fall short to provide skeletal contour insights that direct surgical planning. Herein, we propose statistical shape modeling (SSM) as a novel tool for investigating mandibular dimorphism for young white individuals. METHODS: A single-center, retrospective study was performed using computed tomography (CT) scans of white individuals, aged 20 to 39 years old. AI-assisted, three-dimensional (3D) mandibles were reconstructed in Materialise Mimics v25.0. We used SSM to generate average 3D models for both genders. Relevant manual anthropometric measurements were taken for the SSMs and individual mandibles. Contour disparities were then represented using 3D overlays and heatmaps. Statistical analyses were performed using unpaired student t testing or Wilcoxon signed rank testing with 95% confidence interval as deemed appropriate by population-level normality assessment. RESULTS: Ninety-eight patients (53 females, 45 males) were included. Male mandibles showed greater bigonial width, intercondylar width, ramus height, and body length [p<0.005]. There was no statistically significant difference in the gonial angle measurements [p=0.62]. All relevant manual individual measurements demonstrated excellent concordance to their SSM counterparts. The 3D overlays of SSMs revealed squarer male chins with more lateral but less anterior projection than their female counterparts. Also, the female mandibles showed smoother transition at the gonial angle. CONCLUSIONS: SSM provides a novel tool to objectively evaluate volumetric and contour dimorphisms between genders. Moreover, this method can be automated, allowing for expedited comparisons between populations of interest compared to manual assessment. LEVEL OF EVIDENCE III: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors   www.springer.com/00266 . Bullet points about the importance of this work: Advancing Anthropometric Assessment: Statistical shape modeling (SSM) offers a cutting-edge approach to visualizing gender-specific skeletal anatomic differences for aesthetic and gender-affirming facial surgery. Expediting Comparative Analysis: The workflow established in this paper streamlines the evaluative process, enabling rapid morphologic comparisons between populations. Patient-Centered Care: This study establishes a foundation for the development of SSMs in individualized operative planning.

Ultrasound Features in Gout: An Overview.

The accurate diagnosis of gout frequently constitutes a challenge in clinical practice, as it bears a close resemblance to other rheumatologic conditions. An undelayed diagnosis and an early therapeutic intervention using uric acid lowering therapy (ULT) is of the utmost importance for preventing bone destruction, the main point of managing gout patients. Advanced and less invasive imaging techniques are employed to diagnose the pathology and ultrasonography (US) stands out as a non-invasive, widely accessible and easily reproducible method with high patient acceptability, enabling the evaluation of the full clinical spectrum in gout. The 2023 EULAR recommendations for imaging in diagnosis and management of crystal-induced arthropathies in clinical practice state that US is a fundamental imagistic modality. The guidelines underline its effectiveness in detecting crystal deposition, particularly for identifying tophi and the double contour sign (DCS). Its utility also arises in the early stages, consequent to synovitis detection. US measures of monosodium urate (MSU) deposits are valuable indicators, sensitive to change consequent to even short-term administration of ULT treatment, and can be feasibly used both in current daily practice and clinical trials. This paper aimed to provide an overview of the main US features observed in gout patients with reference to standardized imaging guidelines, as well as the clinical applicability both for diagnosis accuracy and treatment follow-up. Our research focused on summarizing the current knowledge on the topic, highlighting key data that emphasize gout as one of the few rheumatological conditions where US is recognized as a fundamental diagnostic and monitoring tool, as reflected in the most recent classification criteria.

Association between prosthesis contour and peri-implantitis in patients compliant with supportive periodontal therapy: A retrospective cohort study.

PURPOSE: Poor contour of the implant restoration causes plaque accumulation and increases the risk of peri-implantitis. This study aimed to investigate whether the prosthodontic components of dental implants were associated with the prevalence of peri-implantitis. METHODS: We enrolled 185 patients with 348 implants who underwent at least 1-year follow-up after the delivery of the prosthesis from February 2010 to January 2021. Demographic data of the patients and implants and the follow-up period were recorded. The emergence angle, type of cervical crown contour, and contour angle were analyzed using annual bite-wing radiographs. Peri-implantitis in this study was diagnosed if the peri-implant bone loss was greater than 2 mm between the bite-wing radiographs taken at baseline and the latest. Chi-square test, two-sample t-test, and multivariate logistic regression were used to investigate the differences and odds ratios between the peri-implantitis and non-peri-implantitis groups. RESULTS: The incidence of peri-implantitis was 14.9% during a follow-up period of 1509 days after the delivery of the prosthesis for at least 1-year. Based on the prevalence of non-peri-implantitis and after adjusting for confounding factors, the risk factors identified were bone types for implants (native bone vs. alveolar ridge preservation: adjusted odds ratio = 2.43, P = 0.04). Sex, arch, and guided bone regeneration vs. alveolar ridge preservation have the potential for a statistical difference. CONCLUSIONS: Compared with implants at alveolar ridge preservation sites, implants in the native bone were more prone to peri-implantitis. Further randomized controlled trials are required to determine these associations.

Improving Fire Detection Accuracy through Enhanced Convolutional Neural Networks and Contour Techniques.

In this study, a novel method combining contour analysis with deep CNN is applied for fire detection. The method was made for fire detection using two main algorithms: one which detects the color properties of the fires, and another which analyzes the shape through contour detection. To overcome the disadvantages of previous methods, we generate a new labeled dataset, which consists of small fire instances and complex scenarios. We elaborated the dataset by selecting regions of interest (ROI) for enhanced fictional small fires and complex environment traits extracted through color characteristics and contour analysis, to better train our model regarding those more intricate features. Results of the experiment showed that our improved CNN model outperformed other networks. The accuracy, precision, recall and F1 score were 99.4%, 99.3%, 99.4% and 99.5%, respectively. The performance of our new approach is enhanced in all metrics compared to the previous CNN model with an accuracy of 99.4%. In addition, our approach beats many other state-of-the-art methods as well: Dilated CNNs (98.1% accuracy), Faster R-CNN (97.8% accuracy) and ResNet (94.3%). This result suggests that the approach can be beneficial for a variety of safety and security applications ranging from home, business to industrial and outdoor settings.

Mass-selective instability and resonance ejection modes for DIT with rectangular asymmetric wave shape.

We consider the operation of a digital linear ion trap with resonance radial ejection and mass selective instability modes. Periodic wave shape has a positive part with amplitude V+=V0 and duration 0.8T and negative part with amplitude V-=-4V0 and duration 0.2T, where T is the period. The mapping of the stability diagram, calculations of the well's depth and ion oscillations spectra are presented. The process of resonant excitation of ion oscillations by a dipole sinusoidal signal is studied, as well as ion ejection at the stability boundary. The trajectory method is used for this purpose. It is shown that the mass selectivity of dipole excitation is twice as large for rectangular wave shape compared to sinusoidal wave shape. Increasing the diameter of the round rods of the linear trap gives an increase in the resolving power. The possibility of DIT operation in mass-selective instability mode at the boundary point qb=0.39 is discussed.

Upper Eyelid Contour Changes After Müller's Muscle Conjunctiva Resection.

PURPOSE: To analyze the upper eyelid contour after Müller's muscle conjunctiva resection (MMCR) performed by four different surgeons. METHODS: Comparative cross-sectional analysis of the pre- and postoperative contours of a control group and four groups of upper lids (n = 88) of 65 patients who underwent MMCR at four international centers. The procedure employed was essentially the same as described by Putterman but performed with different instruments to entrap the posterior lamella. Multiple medial and lateral margin lid distances were measured on Bézier lines expressing the pre- and postoperative lid contours. RESULTS: Preoperatively, two groups had significant lateral and medial ptosis. After MMCR, the lateral segment of the lid's contour was corrected in all groups. In the two groups with more pronounced ptosis, the nasal lid contour was undercorrected. CONCLUSIONS: In MMCR, regardless of the instrument used to entrap the posterior lamella, the amount of medial tissue resection is essential to avoid postoperative nasal undercorrection. LEVEL OF EVIDENCE III: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .