Implant radiography and radiology

The practitioner placing dental implants has many options with respect to pre-implant radiographic assessment of the jaws. The advantages and disadvantages of the imaging modalities currently available for pre-implant imaging are discussed in some detail. Intra-oral and extra-oral radiographs are generally low dose but the information provided is limited as the images are not three-dimensional. Tomography is three-dimensional, but the image quality is highly variable. Computed tomography (CT) has been the gold standard for many years as the information provided is three-dimensional and generally very accurate. However, CT examinations are expensive and deliver a relatively high radiation dose to the patient. The latest imaging modality introduced is cone beam volumetric tomography (CBVT) and this technology is very promising with regard to pre-implant imaging. CBVT generally delivers a lower dose to the patient than CT and provides reasonably sharp images with three-dimensional information. A comparison between CT and CBVT is provided. Magnetic resonance imaging is showing some promise, but the examinations are not readily available, generally expensive and bone is not well imaged. Magnetic resonance imaging is excellent for demonstrating soft tissues and therefore may be of great use in identifying the inferior dental nerve and vessels. All of the above technology is of little value if the information required is not obtained and so information is also provided on imaging of some of the vital structures. Of particular interest is the inferior dental canal, incisive canals of the mandible, genial foramina and canals, maxillary sinus and the incisive canal and foramen of the maxilla

Detecting and influencing driver emotions using psycho-physiological sensors and ambient light

Driving is a sensitive task that is strongly affected by the driver's emotions. Negative emotions, such as anger, can evidently lead to more driving errors. In this work, we introduce a concept of detecting and influencing driver emotions using psycho-physiological sensing for emotion classification and ambient light for feedback. We detect arousal and valence of emotional responses from wearable bio-electric sensors, namely brain-computer interfaces and heart rate sensors. We evaluated our concept in a static driving simulator with a fully equipped car with 12 participants. Before the rides, we elicit negative emotions and evaluate driving performance and physiological data while driving under stressful conditions. We use three ambient lighting conditions (no light, blue, orange). Using a subject-dependent random forests classifier with 40 features collected from physiological data we achieve an average accuracy of 78.9% for classifying valence and 68.7% for arousal. Driving performance was enhanced in conditions where ambient lighting was introduced. Both blue and orange light helped drivers to improve lane keeping. We discuss insights from our study and provide design recommendations for designing emotion sensing and feedback systems in the car