Much progress has been made in recent years in identifying genes involved in the risk of developing Alzheimer’s disease (AD), the most common form of dementia. Yet despite the identification of over 20 disease associated loci, mainly through genome wide association studies (GWAS), a large proportion of the genetic component of the disorder remains unexplained. Recent evidence from the AD field, as with other complex diseases, suggests a large proportion of this “missing heritability” may be due to rare variants of moderate to large effect size, but the methodologies to detect such variants are still in their infancy. The latest studies in the field have been focused on the identification of coding variation associated with AD risk, through whole-exome or whole-genome sequencing. Such variants are expected to have larger effect sizes than GWAS loci, and are easier to functionally characterize, and develop cellular and animal models for. This review explores the issues involved in detecting rare variant associations in the context of AD, highlighting some successful approaches utilized to date.

Objective This study investigates the accuracy of an automated method to rapidly segment relevant temporal bone anatomy from cone beam computed tomography (CT) images. Implementation of this segmentation pipeline has potential to improve surgical safety and decrease operative time by augmenting preoperative planning and interfacing with image-guided robotic surgical systems. Study Design Descriptive study of predicted segmentations. Setting Academic institution. Methods We have developed a computational pipeline based on the symmetric normalization registration method that predicts segmentations of anatomic structures in temporal bone CT scans using a labeled atlas. To evaluate accuracy, we created a data set by manually labeling relevant anatomic structures (eg, ossicles, labyrinth, facial nerve, external auditory canal, dura) for 16 deidentified high-resolution cone beam temporal bone CT images. Automated segmentations from this pipeline were compared against ground-truth manual segmentations by using modified Hausdorff distances and Dice scores. Runtimes were documented to determine the computational requirements of this method. Results Modified Hausdorff distances and Dice scores between predicted and ground-truth labels were as follows: malleus (0.100 ± 0.054 mm; Dice, 0.827 ± 0.068), incus (0.100 ± 0.033 mm; Dice, 0.837 ± 0.068), stapes (0.157 ± 0.048 mm; Dice, 0.358 ± 0.100), labyrinth (0.169 ± 0.100 mm; Dice, 0.838 ± 0.060), and facial nerve (0.522 ± 0.278 mm; Dice, 0.567 ± 0.130). A quad-core 16GB RAM workstation completed this segmentation pipeline in 10 minutes. Conclusions We demonstrated submillimeter accuracy for automated segmentation of temporal bone anatomy when compared against hand-segmented ground truth using our template registration pipeline. This method is not dependent on the training data volume that plagues many complex deep learning models. Favorable runtime and low computational requirements underscore this method’s translational potential.

ObjectivePreoperative planning for otologic or neurotologic procedures often requires manual segmentation of relevant structures, which can be tedious and time‐consuming. Automated methods for segmenting multiple geometrically complex structures can not only streamline preoperative planning but also augment minimally invasive and/or robot‐assisted procedures in this space. This study evaluates a state‐of‐the‐art deep learning pipeline for semantic segmentation of temporal bone anatomy.Study DesignA descriptive study of a segmentation network.SettingAcademic institution.MethodsA total of 15 high‐resolution cone‐beam temporal bone computed tomography (CT) data sets were included in this study. All images were co‐registered, with relevant anatomical structures (eg, ossicles, inner ear, facial nerve, chorda tympani, bony labyrinth) manually segmented. Predicted segmentations from no new U‐Net (nnU‐Net), an open‐source 3‐dimensional semantic segmentation neural network, were compared against ground‐truth segmentations using modified Hausdorff distances (mHD) and Dice scores.ResultsFivefold cross‐validation with nnU‐Net between predicted and ground‐truth labels were as follows: malleus (mHD: 0.044 ± 0.024 mm, dice: 0.914 ± 0.035), incus (mHD: 0.051 ± 0.027 mm, dice: 0.916 ± 0.034), stapes (mHD: 0.147 ± 0.113 mm, dice: 0.560 ± 0.106), bony labyrinth (mHD: 0.038 ± 0.031 mm, dice: 0.952 ± 0.017), and facial nerve (mHD: 0.139 ± 0.072 mm, dice: 0.862 ± 0.039). Comparison against atlas‐based segmentation propagation showed significantly higher Dice scores for all structures (p < .05).ConclusionUsing an open‐source deep learning pipeline, we demonstrate consistently submillimeter accuracy for semantic CT segmentation of temporal bone anatomy compared to hand‐segmented labels. This pipeline has the potential to greatly improve preoperative planning workflows for a variety of otologic and neurotologic procedures and augment existing image guidance and robot‐assisted systems for the temporal bone.

Chronic thromboembolic pulmonary hypertension (CTEPH) is an underdiagnosed and undertreated sequelae of acute pulmonary embolism. In this comprehensive review, we provide an introductory overview of CTEPH, highlight recent advances in its diagnostic imaging, and describe the surgical technique for pulmonary thromboendarterectomy (PTE), the only established curative treatment for CTEPH. We also discuss the emerging role of balloon pulmonary angioplasty, both independently and combined with PTE, for patients with inoperable, residual, or refractory pulmonary hypertension post PTE. Finally, we stress the importance of a specialized multidisciplinary team approach to CTEPH patient care and share our approach to optimizing care for these patients.