Clinical results of implant placement in resorbed ridges using simultaneous guided bone regeneration: a multicenter case series.

OBJECTIVES: The purpose of this case series was to evaluate the new bone formation following guided bone regeneration (GBR) with a calcium phosphosilicate (CPS), alloplastic bone putty at peri-implant dehiscence defects and to assess survival rate of implants placed in the augmented sites after 12 months of function. MATERIALS AND METHODS: Implants were placed in patients exhibiting Seibert class I ridge defects resulting in peri-implant dehiscence defects. The defects were treated following GBR principles with the use of a CPS alloplastic bone graft putty in combination either with a collagen membrane or a titanium mesh. The height of each bony dehiscence was clinically measured at the time of implant placement and again during second-stage surgery. The percentage of complete defect coverage, frequency of adverse events, and risk factors for residual defect were determined. RESULTS: Thirty-six implants were placed in 26 patients. Twenty-seven of the 36 sites employed a collagen membrane in conjunction with the CPS while the remaining nine sites utilized a titanium membrane. Mean gain in bone height was 3.23 ± 2.04 mm, with 75 % of the peri-implant defects achieving complete regeneration. A negative correlation was identified between patient age and complete coverage of the peri-implant defect (p = 0.026). The implant survival rate at 12 months was 97.22 %. CONCLUSION: Use of CPS bone putty during delayed implant placement at peri-implant dehiscence sites either in combination with a collagen membrane or a titanium mesh results in predictable defect coverage. CLINICAL RELEVANCE: The handling characteristics of CPS putty may simplify GBR protocol. Implants placed in conjunction with GBR have a very good survival rate after 1 year of follow-up.

Implants placed simultaneously with lateral window sinus augmentation using a putty alloplastic bone substitute for increased primary implant stability: a retrospective study.

INTRODUCTION: The aim of this retrospective study was to evaluate the primary stability of implants placed in significantly pneumatized maxillary sinuses with minimum residual bone height. MATERIALS AND METHODS: Seventeen patients who had been treated with simultaneous implant placement in sites with <5 mm of vertical bone height using a modified direct sinus lift technique were included. Implants placed in adjacent sites with at least 5 mm of bone height were included as quasi-controls. RESULTS: A total of 30 implants were inserted with a maximum insertion torque number >20 N/cm. Logistic regression analysis failed to show any association between residual bone height and primary implant stability. Implant survival was 96.67% (29/30) during a mean follow-up of 15.74 months postloading. CONCLUSIONS: The diminished preoperative vertical dimensions of the residual ridges did not seem to negatively influence the osseointegration of implants placed in this study. The prerequisite for simultaneous sinus augmentation and implant placement is an adequate primary stability of the implant and not a fixed minimum bone height level.

Learning Log-Determinant Divergences for Positive Definite Matrices.

Representations in the form of Symmetric Positive Definite (SPD) matrices have been popularized in a variety of visual learning applications due to their demonstrated ability to capture rich second-order statistics of visual data. There exist several similarity measures for comparing SPD matrices with documented benefits. However, selecting an appropriate measure for a given problem remains a challenge and in most cases, is the result of a trial-and-error process. In this paper, we propose to learn similarity measures in a data-driven manner. To this end, we capitalize on the αß-log-det divergence, which is a meta-divergence parametrized by scalars α and ß, subsuming a wide family of popular information divergences on SPD matrices for distinct and discrete values of these parameters. Our key idea is to cast these parameters in a continuum and learn them from data. We systematically extend this idea to learn vector-valued parameters, thereby increasing the expressiveness of the underlying non-linear measure. We conjoin the divergence learning problem with several standard tasks in machine learning, including supervised discriminative dictionary learning and unsupervised SPD matrix clustering. We present Riemannian gradient descent schemes for optimizing our formulations efficiently, and show the usefulness of our method on eight standard computer vision tasks.

Image Descriptors for Weakly Annotated Histopathological Breast Cancer Data.

INTRODUCTION: Cancerous Tissue Recognition (CTR) methodologies are continuously integrating advancements at the forefront of machine learning and computer vision, providing a variety of inference schemes for histopathological data. Histopathological data, in most cases, come in the form of high-resolution images, and thus methodologies operating at the patch level are more computationally attractive. Such methodologies capitalize on pixel level annotations (tissue delineations) from expert pathologists, which are then used to derive labels at the patch level. In this work, we envision a digital connected health system that augments the capabilities of the clinicians by providing powerful feature descriptors that may describe malignant regions. MATERIAL AND METHODS: We start with a patch level descriptor, termed Covariance-Kernel Descriptor (CKD), capable of compactly describing tissue architectures associated with carcinomas. To leverage the recognition capability of the CKDs to larger slide regions, we resort to a multiple instance learning framework. In that direction, we derive the Weakly Annotated Image Descriptor (WAID) as the parameters of classifier decision boundaries in a Multiple Instance Learning framework. The WAID is computed on bags of patches corresponding to larger image regions for which binary labels (malignant vs. benign) are provided, thus obviating the necessity for tissue delineations. RESULTS: The CKD was seen to outperform all the considered descriptors, reaching classification accuracy (ACC) of 92.83%. and area under the curve (AUC) of 0.98. The CKD captures higher order correlations between features and was shown to achieve superior performance against a large collection of computer vision features on a private breast cancer dataset. The WAID outperform all other descriptors on the Breast Cancer Histopathological database (BreakHis) where correctly classified malignant (CCM) instances reached 91.27 and 92.00% at the patient and image level, respectively, without resorting to a deep learning scheme achieves state-of-the-art performance. DISCUSSION: Our proposed derivation of the CKD and WAID can help medical experts accomplish their work accurately and faster than the current state-of-the-art.