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.

Apolipoprotein ɛ4 is Associated with Dementia and Cognitive Impairment Predominantly Due to Alzheimer's Disease and Not with Vascular Cognitive Impairment: A Singapore-Based Cohort.

BACKGROUND AND OBJECTIVE: While the association for apolipoprotein ɛ4 allele (APOE4) with Alzheimer's disease (AD) has been consistently confirmed, the association with vascular cognitive impairment (VCI) is unclear. We therefore explored the relationship of APOE with both AD and cerebrovascular disease (CeVD) by examining the prevalence of APOE4 in AD, AD with CeVD and vascular dementia (VaD), as well as in cognitive impairment no dementia (CIND) with and without CeVD. METHODS: We performed a case-control study with subjects recruited from memory clinics and the community. All subjects underwent standardized brain neuroimaging, clinical and neuropsychological assessments, following which they were classified using research criteria. RESULTS: A total of 411 subjects; 92 controls with no cognitive impairment (NCI), 77 CIND without CeVD, 87 CIND with CeVD, 55 AD without CeVD, 68 AD with CeVD, and 32 VaD patients were recruited. Compared to NCI (16.3%), the prevalence of APOE4 carriers was significantly higher only in CIND (37.7%) and AD in the absence of CeVD (45.5%), but not in the three subgroups of VCI, namely CIND with CeVD (20.7%), AD with CeVD (27.9%) and VaD (25.0%). Logistic regression analyses also showed that APOE4 carriers were more likely to have CIND without CeVD (Odds Ratio [OR]: 3.34; 95% Confidence Interval [CI]: 1.59-7.03) and AD without CeVD (OR: 7.21; 95% CI: 2.74-18.98), but no such association was observed in the VCI subgroups. CONCLUSION: APOE4 is significantly associated with dementia and CIND due to AD pathology, but not with VCI.