A Novel Ear Impression-Taking Method Using Structured Light Imaging and Machine Learning: A Pilot Proof of Concept Study with Patients' Feedback on Prototype.

INTRODUCTION: Taking an ear impression is a minimally invasive procedure. A review of existing literature suggests that contactless methods of scanning the ear have not been developed. We proposed to establish a correlation between external ear features with the ear canal and with this proof of concept to develop a prototype and an algorithm for capturing and predicting ear canal information. METHODS: We developed a novel prototype using structured light imaging to capture external images of the ear. Using a large database of existing ear impression images obtained by traditional methods, correlation analyses were carried out and established. A deep neural network was devised to build a predictive algorithm. Patients undergoing hearing aid evaluation undertook both methods of ear impression-taking. We evaluated their subjective feedback and determined if there was a close enough objective match between the images obtained from the impression techniques. RESULTS: A prototype was developed and deployed for trial, and most participants were comfortable with this novel method of ear impression-taking. Partial matching of the ear canal could be obtained from the images taken, and the predictive algorithm applied for a few sample images was within good standard of error with proof of concept established. DISCUSSION: Further studies are warranted to strengthen the predictive capabilities of the algorithm and determine optimal prototype imaging positions so that sufficient ear canal information can be obtained for three-dimensional printing. Ear impression-taking may then have the potential to be automated, with the possibility of same-day three-dimensional printing of the earmold to provide timely access.

Classification, registration and segmentation of ear canal impressions using convolutional neural networks.

Today, fitting bespoke hearing aids involves injecting silicone into patients' ears to produce ear canal molds. These are subsequently 3D scanned to create digital ear canal impressions. However, before digital impressions can be used they require a substantial amount of effort in manual 3D editing. In this article, we present computational methods to pre-process ear canal impressions. The aim is to create automation tools to assist the hearing aid design, manufacturing and fitting processes as well as normalizing anatomical data to assist the study of the outer ear canal's morphology. The methods include classifying the handedness of the impression into left and right ear types, orienting the geometries onto the same coordinate system sense, and removing extraneous artifacts introduced by the silicone mold. We investigate the use of convolutional neural networks for performing these semantic tasks and evaluate their accuracy using a dataset of 3000 ear canal impressions. The neural networks proved highly effective at performing these tasks with 95.8% adjusted accuracy in classification, 92.3% within 20° angular error in registration and 93.4% intersection over union in segmentation.

Anisotropy-Driven Crystallization of Dimensionally Resolved Quasi-1D Van der Waals Nanostructures.

Unusual behavior in solids emerges from the complex interplay between crystalline order, composition, and dimensionality. In crystals comprising weakly bound one-dimensional (1D) or quasi-1D (q-1D) chains, properties such as charge density waves, topologically protected states, and indirect-to-direct band gap crossovers have been predicted to arise. However, the experimental demonstration of many of these nascent physics in 1D or q-1D van der Waals (vdW) crystals is obscured by the highly anisotropic bonding between the chains, stochasticity of top-down exfoliation, and the lack of synthetic strategies to control bottom-up growth. Herein, we report the directed crystallization of a model q-1D vdW phase, Sb2S3, into dimensionally resolved nanostructures. We demonstrate the uncatalyzed growth of highly crystalline Sb2S3 nanowires, nanoribbons, and quasi-2D nanosheets with thicknesses in the range of 10 to 100 nm from the bottom-up crystallization of [Sb4S6]n chains. We found that dimensionally resolved nanostructures emerge from two distinct chemical vapor growth pathways defined by diverse covalent intrachain and anisotropic vdW interchain interactions and controlled precursor ratios in the vapor phase. At sub-100 nm nanostructure thicknesses, we observe the hardening of phonon modes, blue-shifting of optical band gaps, and the emergence of a new high-energy photoluminescence peak. The directional growth of weakly bound 1D ribbons or chains into well-resolved nanocrystalline morphologies provides opportunities to develop ordered nanostructures and hierarchical assemblies that are suitable for a wide range of optoelectronic and quantum devices.

Lexicon for classifying ear-canal shapes.

The ear canal is usually described as an S-shaped funnel. In attempting to classify ear-canal shapes obtained from point clouds digitized from molds of 300 ears, the problem of designing criteria for distinguishing and organizing the canal shapes arose. In this work, we extracted features inspired by the S-shape characteristic (critical point, maximum, minimum, twist, writhe, translation, rotation) and, through them, introduced 14 types of ear-canal shapes. This classification allowed comparison of ears within a type and of ears between different types. It expanded our range of descriptors of canal shapes and unlocked perspectives for applications.

The prevalence of isolated otolith dysfunction in a local tertiary hospital.

OBJECTIVE: Patients with dizziness may present with symptoms of tilting, swaying, rocking, floating or with disequilibrium. This may be suggestive of an isolated otolithic dysfunction yet, there is little emphasis on this emerging clinical entity. To characterize and describe the prevalence of isolated otolith dysfunction in a local tertiary hospital and correlate them with clinical diagnosis. METHODOLOGY: Retrospective medical chart review of patients who presented with dizziness to the specialist outpatient Otolaryngology clinic, who required vestibular laboratory investigation. RESULTS: Of the 206 patients, more than half of them (52.4%) fulfilled the criteria for either probable or definite isolated otolith dysfunction. When there are clinical symptoms of otolith dysfunction reported, there is a 1.62 odds of a remarkable laboratory otolith finding. The most common clinical finding was "no clear diagnosis" (65.5%) followed by Vestibular Migraine (13.6%). CONCLUSION: The prevalence of isolated otolith dysfunction is quite high. Laboratory tests of otolith function should be performed more routinely. This can be done in a sequential way to optimize cost effectiveness in countries with no insurance reimbursement. Prospective cohort studies on isolated otolith dysfunction, will lay the groundwork for achieving diagnostic consensus and formulating rehabilitation plans to aid this group of patients.

A Preliminary Study: Central Vestibular Sensitivity Affects Motion Sickness Susceptibility through the Efficacy of the Velocity Storage Mechanism.

Background/Objective: Slow-Phase Eye Velocity Time constant (SPEV TC) and Perceived Rotational Duration (PRD) are measurable objective outcomes of rotational chair step-velocity test. These two variables are dependent on the efficacy of the central velocity storage. If sensory conflict from the step-velocity of the rotational chair elicits motion sickness, the SPEV TC and PRD in individuals with varying susceptibility to motion sickness should be affected. We determined if Central Vestibular Sensitivity (CVS) characteristics differ among individuals with a range of Motion Sickness Susceptibility (MSS). Methods: Participants were allocated to two groups based on MSS (low and high) as identified on the short version of the Motion Sick Susceptibility Questionnaire (MSSQ-S). We evaluated the specific relationship between MSS and the characteristics of CVS through the SPEV TC and PRD from the step-velocity test. Results: Results showed significant differences in the PRD between these two groups. 180°/s Per-rotatory PRD is most significantly different (p = 0.005) followed by 50°/s post-rotatory PRD (CCW, p = 0.007; CW, p = 0.021) and log of 180°/s post-rotatory PRD (p = 0.042). Multiple regression analysis indicated that CCW post-rotatory PRD at 50°/s was a strong predictor of MSS. Conclusions: High MSS individuals were observed with elevated PRD in general, indirectly suggesting greater velocity storage efficiency, hence, greater CVS; CVS is therefore positively correlated with MSS. PRD could be a reliable clinical indicator of motion sick susceptibility and may help with the selection of personnel working in motion sick environments and with the verification of motion sickness therapeutic interventions.