Creating a health informatics data resource for hearing health research.

BACKGROUND: The National Institute of Health and Social Care Research (NIHR) Health Informatics Collaborative (HIC) for Hearing Health has been established in the UK to curate routinely collected hearing health data to address research questions. This study defines priority research areas, outlines its aims, governance structure and demonstrates how hearing health data have been integrated into a common data model using pure tone audiometry (PTA) as a case study. METHODS: After identifying key research aims in hearing health, the governance structure for the NIHR HIC for Hearing Health is described. The Observational Medical Outcomes Partnership (OMOP) was chosen as our common data model to provide a case study example. RESULTS: The NIHR HIC Hearing Health theme have developed a data architecture outlying the flow of data from all of the various siloed electronic patient record systems to allow the effective linkage of data from electronic patient record systems to research systems. Using PTAs as an example, OMOPification of hearing health data successfully collated a rich breadth of datapoints across multiple centres. CONCLUSION: This study identified priority research areas where routinely collected hearing health data could be useful. It demonstrates integration and standardisation of such data into a common data model from multiple centres. By describing the process of data sharing across the HIC, we hope to invite more centres to contribute and utilise data to address research questions in hearing health. This national initiative has the power to transform UK hearing research and hearing care using routinely collected clinical data.

Unusual case of Reinke's oedema.

This case report describes a man in his mid 40s, with a history of chronic smoking, who presented with dysphonia. He underwent microlaryngoscopy and biopsy for a suspicious lesion on the anterior right vocal cord. Mask ventilation proved difficult on induction of general anaesthesia due to a solid lesion acting as a ball valve into the glottis. This mass was LASER debulked and sent for histopathology. This demonstrated a haematoma, likely traumatic in origin, with some polypoidal features, consistent with advanced Reinke's oedema. Reinke's oedema is a benign condition where chronic inflammation causes fluid accumulation within the vocal cords. Long-standing inflammation leads to disarrangement of the vocal cord lamina propria, causing fluid accumulation and thereby resulting oedema of the vocal cords. This process can subsequently lead to polyp formation and can cause gravelly voice. This case report describes the potential airway sequelae of this benign condition.

Deep Learning for Automated Image Segmentation of the Middle Ear: A Scoping Review.

OBJECTIVE: Convolutional neural networks (CNNs) have revolutionized medical image segmentation in recent years. This scoping review aimed to carry out a comprehensive review of the literature describing automated image segmentation of the middle ear using CNNs from computed tomography (CT) scans. DATA SOURCES: A comprehensive literature search, generated jointly with a medical librarian, was performed on Medline, Embase, Scopus, Web of Science, and Cochrane, using Medical Subject Heading terms and keywords. Databases were searched from inception to July 2023. Reference lists of included papers were also screened. REVIEW METHODS: Ten studies were included for analysis, which contained a total of 866 scans which were used in model training/testing. Thirteen different architectures were described to perform automated segmentation. The best Dice similarity coefficient (DSC) for the entire ossicular chain was 0.87 using ResNet. The highest DSC for any structure was the incus using 3D-V-Net at 0.93. The most difficult structure to segment was the stapes, with the highest DSC of 0.84 using 3D-V-Net. CONCLUSIONS: Numerous architectures have demonstrated good performance in segmenting the middle ear using CNNs. To overcome some of the difficulties in segmenting the stapes, we recommend the development of an architecture trained on cone beam CTs to provide improved spatial resolution to assist with delineating the smallest ossicle. IMPLICATIONS FOR PRACTICE: This has clinical applications for preoperative planning, diagnosis, and simulation.