Using Peripheral Venous Pressure Waveforms to Predict Key Hemodynamic Parameters

Analysis of peripheral venous pressure (PVP) waveforms is a novel method of monitoring intravascular volume. Two cohorts were used to study the hemodynamics change of the body state and its influence on the PVP using (1) dehydration setting with infants suffering from pyloric stenosis and (2) hemorrhage setting during a craniosynostosis elective surgery. The goal of this research is to develop a minimally invasive method of analyzing the PVP waveforms and find correlations with volume loss. Twenty-three pyloric stenosis patients PVP were acquired at five stages and were divided into euvolemic, normal fluid volume, and hypovolemic, significant fluid loss. Seven craniosynostosis patients were enrolled and the PVP was acquired at the intervention to explore if the isoflurane dosage influences the PVP. A multivariate analysis of variances (MANOVA) was used to test if the PVP was influenced by the volume change and the anesthetic drugs effect. Prediction algorithms based on Fast Fourier Transform were utilized at the two cohort patients analyses to classify an arbitrary PVP into its correct classification. Our research found that PVP signal is influenced by the different hemodynamics states of the body. Based on MANOVA outcomes, we built prediction systems and they were able to categorize an arbitrary PVP signal into its correct classification. The k-nearest neighbor (k-NN) model correctly predicted 77% of the data in the euvolemic and hypovolemic groups. The k-NN models of the anesthetic drugs were able to correctly predict correctly at least 85% of the preoperative and intraoperative signals of the pyloric stenosis patients and the different isoflurane dosages of the craniosynostosis patients. Analyzing the PVP signal is a promising tool for measuring the dehydration level in acute settings. Our results imply that the subsequent changes in vascular resistance due to inhaled and infused anesthetics are reflected in the peripheral veins. A technology that would accurately assess the volume status of a patient to guide triage and treatment would be a significant improvement in various care settings. This minimally invasive technology utilizes a standard peripheral intravenous line and a commercial pressure-monitoring transducer, which exist today and requires no new clinical skills

Data-Driven Classification Methods for Craniosynostosis Using 3D Surface Scans

Diese Arbeit befasst sich mit strahlungsfreier Klassifizierung von Kraniosynostose mit zusätzlichem Schwerpunkt auf Datenaugmentierung und auf die Verwendung synthetischer Daten als Ersatz für klinische Daten. Motivation: Kraniosynostose ist eine Erkrankung, die Säuglinge betrifft und zu Kopfdeformitäten führt. Diagnose mittels strahlungsfreier 3D Oberflächenscans ist eine vielversprechende Alternative zu traditioneller computertomographischer Bildgebung. Aufgrund der niedrigen Prävalenz und schwieriger Anonymisierbarkeit sind klinische Daten nur spärlich vorhanden. Diese Arbeit adressiert diese Herausforderungen, indem sie neue Klassifizierungsalgorithmen vorschlägt, synthetische Daten für die wissenschaftliche Gemeinschaft erstellt und zeigt, dass es möglich ist, klinische Daten vollständig durch synthetische Daten zu ersetzen, ohne die Klassifikationsleistung zu beeinträchtigen. Methoden: Ein Statistisches Shape Modell (SSM) von Kraniosynostosepatienten wird erstellt und öffentlich zugänglich gemacht. Es wird eine 3D-2D-Konvertierung von der 3D-Gittergeometrie in ein 2D-Bild vorgeschlagen, die die Verwendung von Convolutional Neural Networks (CNNs) und Datenaugmentierung im Bildbereich ermöglicht. Drei Klassifizierungsansätze (basierend auf cephalometrischen Messungen, basierend auf dem SSM, und basierend auf den 2D Bildern mit einem CNN) zur Unterscheidung zwischen drei Pathologien und einer Kontrollgruppe werden vorgeschlagen und bewertet. Schließlich werden die klinischen Trainingsdaten vollständig durch synthetische Daten aus einem SSM und einem generativen adversarialen Netz (GAN) ersetzt. Ergebnisse: Die vorgeschlagene CNN-Klassifikation übertraf konkurrierende Ansätze in einem klinischen Datensatz von 496 Probanden und erreichte einen F1-Score von 0,964. Datenaugmentierung erhöhte den F1-Score auf 0,975. Zuschreibungen der Klassifizierungsentscheidung zeigten hohe Amplituden an Teilen des Kopfes, die mit Kraniosynostose in Verbindung stehen. Das Ersetzen der klinischen Daten durch synthetische Daten, die mit einem SSM und einem GAN erstellt wurden, ergab noch immer einen F1-Score von über 0,95, ohne dass das Modell ein einziges klinisches Subjekt gesehen hatte. Schlussfolgerung: Die vorgeschlagene Umwandlung von 3D-Geometrie in ein 2D-kodiertes Bild verbesserte die Leistung bestehender Klassifikatoren und ermöglichte eine Datenaugmentierung während des Trainings. Unter Verwendung eines SSM und eines GANs konnten klinische Trainingsdaten durch synthetische Daten ersetzt werden. Diese Arbeit verbessert bestehende diagnostische Ansätze auf strahlungsfreien Aufnahmen und demonstriert die Verwendbarkeit von synthetischen Daten, was klinische Anwendungen objektiver, interpretierbarer, und weniger kostspielig machen

Apert syndrome and repercussions in Dental Medicine

Apert’s syndrome is a craniosynostosis syndrome caused by mutations in the gene coding for the fibroblast growthfactor receptor 2 (FGFR2), characterized by craniosynostosis, midface hypoplasia and syndactyly of the hands and feet. It has several oral manifestations, such as ogival palate, maxillary transverse and sagittal hypoplasia, dental crowding, eruptive delay and ectopic position of the teeth. The diagnosis of Apert’s syndrome is established in a proband with classic clinical characteristics, and genetic tests can also be performed. Patients with this syndrome often require craniofacial team care and dental, orthodontic and orthognathic surgical management because of their esthetic and functional problems such as Class III malocclusion and midface hypoplasia. The aim of this study is to present a literature review on oral manifestations of Apert’s syndrome and their impact on dental medicine.info:eu-repo/semantics/publishedVersio

Automated surgical planning in spring-assisted sagittal craniosynostosis correction using finite element analysis and machine learning

Sagittal synostosis is a condition caused by the fused sagittal suture and results in a narrowed skull in infants. Spring-assisted cranioplasty is a correction technique used to expand skulls with sagittal craniosynostosis by placing compressed springs on the skull before six months of age. Proposed methods for surgical planning in spring-assisted sagittal craniosynostosis correction provide information only about the skull anatomy or require iterative finite element simulations. Therefore, the selection of surgical parameters such as spring dimensions and osteotomy sizes may remain unclear and spring-assisted cranioplasty may yield sub-optimal surgical results. The aim of this study is to develop the architectural structure of an automated tool to predict post-operative surgical outcomes in sagittal craniosynostosis correction with spring-assisted cranioplasty using machine learning and finite element analyses. Six different machine learning algorithms were tested using a finite element model which simulated a combination of various mechanical and geometric properties of the calvarium, osteotomy sizes, spring characteristics, and spring implantation positions. Also, a statistical shape model representing an average sagittal craniosynostosis calvarium in 5-month-old patients was used to assess the machine learning algorithms. XGBoost algorithm predicted post-operative cephalic index in spring-assisted sagittal craniosynostosis correction with high accuracy. Finite element simulations confirmed the prediction of the XGBoost algorithm. The presented architectural structure can be used to develop a tool to predict the post-operative cephalic index in spring-assisted cranioplasty in patients with sagittal craniosynostosis can be used to automate surgical planning and improve post-operative surgical outcomes in spring-assisted cranioplasty

Relation between Metopic Suture Persistence and Frontal Sinus Development

The frontal bone develops as two halves, which further unite in a single bone by the closure of the mid-sagittal metopic suture, typically by the end of the first postnatal year. The frontal sinus begins to expand into the orbital and vertical plates of the frontal bone postnatally and reaches the level of the nasion by the fourth year of age. At this time, the metopic suture is usually entirely closed. However, in the cases of failed closure of the metopic suture, its relationship to the frontal sinus development is still obscure. Here, we review the relevant literature and discuss the frontal bone development and maturation, from the viewpoint of the frontal sinus pneumatization in relation to the metopic craniosynostosis and failed closure of the metopic suture. The peculiar to the metopic skulls frontal bone configuration is rather an expression of the underlying neural mass demands than a consequence of the metopic suture persistence. Furthermore, the persistent metopic suture is frequently associated with a frontal sinus underdevelopment. It seems that the metopic suture does not inhibit the frontal sinus pneumatization itself, but rather both traits are an expression or an aftereffect of a certain condition during the early development

Advances in Craniofacial Surgery

Calvaria development initiates by growth from primary ossification centers meeting each other to form suture sites. The term craniosynostosis describes premature fusion of one or more of the calvarial sutures. Deformities are usually observable during the first few months of the newborn’s life. The premature fusion of sutures could produce intracranial pressure elevation and consequently lead to abnormal neurocognitive = neurologic development. Patients with craniosynostosis require surgical plans containing multiple surgical staging. In the following chapter, we present our experience in surgical treatment of children with various craniosynostosis syndromes