Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans.

Craniofacial cephalometric analysis is a diagnostic tool used for the assessment of the relationship of various bones and soft tissues in the head and face. Cephalometric analysis has been traditionally conducted with the use of 2D radiographs and landmark sets and restricted to size, linear and angular measurements, and 2D relationships. The increasing use of 3D cone beam computed tomography (CBCT) scans in the dental field dictates the need for the evolution to 3D cephalometric analysis, which incorporates shape and a more realistic analysis of longitudinal development in all three planes.

Relational reasoning network for anatomical landmarking.

Purpose: We perform anatomical landmarking for craniomaxillofacial (CMF) bones without explicitly segmenting them. Toward this, we propose a simple, yet efficient, deep network architecture, called relational reasoning network (RRN), to accurately learn the local and the global relations among the landmarks in CMF bones; specifically, mandible, maxilla, and nasal bones.

Approach: The proposed RRN works in an end-to-end manner, utilizing learned relations of the landmarks based on dense-block units.

A prospective longitudinal study of clinical outcome and quality of life assessment in patients with chronic rhinosinusitis after functional endoscopic sinus surgery using sino nasal outcome test-22.

Chronic rhinosinusitis (CRS) significantly affect the quality of life (QoL) of patients. The study was conducted in CRS patients who were treated with functional endoscopic sinus surgery (FESS) after failure of medical treatment to analyze clinical outcome using prospectively collected data through a symptom-based rhinosinusitis outcome measure, the Sino-nasal Outcome Test-22 (SNOT-22). The aim of the study was to evaluate and compare the QoL in patients of chronic rhinosinusitis pre-operative and after FESS by SNOT-22.

Craniofacial Analysis May Indicate Co-Occurrence of Skeletal Malocclusions and Associated Risks in Development of Cleft Lip and Palate.

Non-syndromic orofacial clefts encompass a range of morphological changes affecting the oral cavity and the craniofacial skeleton, of which the genetic and epigenetic etiologic factors remain largely unknown. The objective of this study is to explore the contribution of underlying dentofacial deformities (also known as skeletal malocclusions) in the craniofacial morphology of non-syndromic cleft lip and palate patients (nsCLP). For that purpose, geometric morphometric analysis was performed using full skull cone beam computed tomography (CBCT) images of patients with nsCLP ( = 30), normocephalic controls ( = 60), as well as to sex- and ethnicity- matched patients with an equivalent dentofacial deformity ( = 30).

Data-driven method to enhance craniofacial and oral phenotype vocabularies.

Background: A significant amount of clinical information captured as free-text narratives could be better used for several applications, such as clinical decision support, ontology development, evidence-based practice, and research. The Human Phenotype Ontology (HPO) is specifically used for semantic comparisons for diagnostic purposes. All these functions require quality coverage of the domain of interest.

Development and Validation of Novel Three-Dimensional Craniofacial Landmarks on Cone-Beam Computed Tomography Scans.

As cone-beam computed tomography (CBCT) scans become increasingly common, it is vital to have reliable 3-dimensional (3D) landmarks for quantitative analysis of craniofacial skeletal morphology. While some studies have developed and used 3D landmarks, these landmark sets are generally small and derived primarily from previous 2-dimensional (2D) cephalometric landmarks. These derived landmarks lack information in parts of the skull such as the cranial base, which is an important feature for cranial growth and development.

Deep Geodesic Learning for Segmentation and Anatomical Landmarking.

In this paper, we propose a novel deep learning framework for anatomy segmentation and automatic landmarking. Specifically, we focus on the challenging problem of mandible segmentation from cone-beam computed tomography (CBCT) scans and identification of 9 anatomical landmarks of the mandible on the geodesic space. The overall approach employs three inter-related steps.

Determinants of outcomes after resection of renal cell carcinoma with venous involvement.

Purpose: To determine the outcomes and to identify prognostic variables determining mortality and recurrence after surgery for renal cell cancer (RCC) with venous involvement.

Methods: Retrospective evaluation of the medical records of 132 patients with RCC and tumor thrombi treated at Johns Hopkins Hospital (1997-2008) was done. Kaplan-Meier analysis was used to determine survivals.