Performance of Oral Cavity Sensors: A Systematic Review.

Technological advancements are enabling new applications within biomedical engineering. As a connection point between the outer environment and the human system, the oral cavity offers unique opportunities for sensing technologies. This paper systematically reviews the performance of measurement systems tested in the human oral cavity. Performance was defined by metrics related to accuracy and agreement estimation. A comprehensive search identifying human studies that reported on the accuracy or agreement of intraoral sensors found 85 research papers. Most of the literature (62%) was in dentistry, followed by neurology (21%), and physical medicine and rehabilitation (12%). The remaining papers were on internal medicine, obstetrics, and aerospace medicine. Most of the studies applied force or pressure sensors (32%), while optical and image sensors were applied most widely across fields. The main challenges for future adoption include the lack of large human trials, the maturity of emerging technologies (e.g., biochemical sensors), and the absence of standardization of evaluation in specific fields. New research should aim to employ robust performance metrics to evaluate their systems and incorporate real-world evidence as part of the evaluation process. Oral cavity sensors offer the potential for applications in healthcare and wellbeing, but for many technologies, more research is needed.

Exploring the impact of pulse oximeter selection within the COVID-19 home-use pulse oximetry pathways.

BACKGROUND: During the COVID-19 pandemic, portable pulse oximeters were issued to some patients to permit home monitoring and alleviate pressure on inpatient wards. Concerns were raised about the accuracy of these devices in some patient groups. This study was conducted in response to these concerns. OBJECTIVES: To evaluate the performance characteristics of five portable pulse oximeters and their suitability for deployment on home-use pulse oximetry pathways created during the COVID-19 pandemic. This study considered the effects of different device models and patient characteristics on pulse oximeter accuracy, false negative and false positive rate. METHODS: A total of 915 oxygen saturation (spO2) measurements, paired with measurements from a hospital-standard pulse oximeter, were taken from 50 patients recruited from respiratory wards and the intensive care unit at an acute hospital in London. The effects of device model and several patient characteristics on bias, false negative and false positive likelihood were evaluated using multiple regression analyses. RESULTS AND CONCLUSIONS: All five portable pulse oximeters appeared to outperform the standard to which they were manufactured. Device model, patient spO2 and patient skin colour were significant predictors of measurement bias, false positive and false negative rate, with some variation between models. The false positive and false negative rates were 11.2% and 24.5%, respectively, with substantial variation between models.

Mechanical Safety of Embedded Electronics for In-body Wearables: A Smart Mouthguard Study.

The growing popularity of contact sports drives the requirement for better design of protective equipment, such as mouthguards. Smart mouthguards with embedded electronics provide a multitude of new ways to provide increased safety and protection to users. Characterisation of how electronic components embedded in typical mouthguard material, ethylene vinyl acetate (EVA), behave under typical sports impacts is crucial for future designs. A novel pendulum impact rig using a hockey ball disc impactor was developed to investigate impact forces and component failure. Two sets of dental models (aluminium and plastic padding chemical metal) were used to manufacture post-thermoformed mouthguards. Seven embedding conditions with varying thickness of EVA (1.5 and 3 mm) and locations of electrical components were tested. Component failures were observed in four out of seven test conditions, and the experimental failure forces at which the electrical component had a 50% chance of failure were reported for those cases. The experimental results showed that an EVA thickness of 3 mm surrounding the electrical component gives the most comprehensive protection even under extreme surface conformity. Computational models on surface conformity of EVA showed that a block of EVA with a minimum thickness of 1.5 mm was better at reducing stress concentration than a shell with an overall thickness of 1.5 mm. This study demonstrated that the thickness of a mouthguard is important when protecting electrical components from extreme dental surface conformity, furthermore the surface geometry should not be overlooked when considering electrical component safety for in-body wearables that are impact prone.

A comprehensive texture segmentation framework for segmentation of capillary non-perfusion regions in fundus fluorescein angiograms.

Capillary non-perfusion (CNP) in the retina is a characteristic feature used in the management of a wide range of retinal diseases. There is no well-established computation tool for assessing the extent of CNP. We propose a novel texture segmentation framework to address this problem. This framework comprises three major steps: pre-processing, unsupervised total variation texture segmentation, and supervised segmentation. It employs a state-of-the-art multiphase total variation texture segmentation model which is enhanced by new kernel based region terms. The model can be applied to texture and intensity-based multiphase problems. A supervised segmentation step allows the framework to take expert knowledge into account, an AdaBoost classifier with weighted cost coefficient is chosen to tackle imbalanced data classification problems. To demonstrate its effectiveness, we applied this framework to 48 images from malarial retinopathy and 10 images from ischemic diabetic maculopathy. The performance of segmentation is satisfactory when compared to a reference standard of manual delineations: accuracy, sensitivity and specificity are 89.0%, 73.0%, and 90.8% respectively for the malarial retinopathy dataset and 80.8%, 70.6%, and 82.1% respectively for the diabetic maculopathy dataset. In terms of region-wise analysis, this method achieved an accuracy of 76.3% (45 out of 59 regions) for the malarial retinopathy dataset and 73.9% (17 out of 26 regions) for the diabetic maculopathy dataset. This comprehensive segmentation framework can quantify capillary non-perfusion in retinopathy from two distinct etiologies, and has the potential to be adopted for wider applications.

Automated segmentation of the choroid in retinal optical coherence tomography images.

The choroid is a tissue layer at the back of the eye, which can be imaged by optical coherence tomography (OCT). Choroidal thickness has been proven to be correlated to several ophthalmic diseases in several studies. In this paper we proposed a novel segmentation technique to address this challenge. This technique firstly automatically segments the inner boundary of the choroid using a two-stage fast active contour model. It secondly allows a real-time human-supervised automated segmentation on the outer boundary of the choroid. Dice similarity coefficient (DSC) was used to evaluate the agreement between manual annotation and our automated measurements on 30 images captured from patients diagnosed with diabetes. The mean DSC value is 92.7% (standard deviation 3.6%) in the range of 85.5% to 98.1%. Results show that this new technique can achieve choroid segmentation with high accuracy.