A multimodal database for the collection of interdisciplinary audiological research data

3. Mobile App

The app module systematically collects information on health status and quality of life through validated questionnaires, which can be personalized and remotely completed by patients on their mobile phones. It also provides access to educational information and content provided by clinical specialists. A screenshot of some of the different facilities provided by the mobile app can be seen in Figure 3.

Figure 3. Mobile app developed to collect health information and questionnaires remotely

Platform Validation

From a functional perspective, the multimodal database provides advanced capabilities that support the primary research processes present in our audiological clinical setting. Currently, the platform database is being validated by specialized clinicians and researchers by handling and collecting data from patients with hearing loss in the context of a multicentric project funded by the Spanish Ministry of Economy and Digital Transformation under project AI4HA from Misiones I+D in Artificial Intelligence 2021, which aims to develop open databases and IA algorithms to early detect and treat diseases with high prevalence in aging. At present, data from more than 200 patients and healthy controls has been collected.

Discussion

In this project, a unified framework integrating information from various audiological and health data sources relevant in both clinical and translational research fields in audiology has been developed. Within this process, a dedicated multimodal database oriented towards the integration of cross-sectional data from different health domains and devices has been implemented, thereby facilitating the customization of various add-ons and data analytics tools. The clinical suitability and applicability of the implemented tests and measures, as well as the possibilities that this platform brings to further standardization, are discussed below.

Towards the standardization and collection of interdisciplinary audiological datasets

In the field of audiology, an issue remains with comparatively small and disaggregated datasets generated at only few centers. Tonal audiometry still represents, in many cases, the clinical gold standard and the primary measure used to characterize individual hearing loss. It also serves as the basis for treatments involving hearing solutions such as hearing-aid and cochlear implant fitting. However, hearing loss is also related to poor spectral resolution and reduced intelligibility, especially in noisy real-life scenarios, where difficulties in perception and clarity are not reflected by an audiogram (Sanchez-Lopez et al., 2020). Indeed, the consequences of poor speech understanding may also lead to a decline in cognitive capabilities and quality of life. These factors are relevant variables, not only for fully understanding the impact of hearing loss on a patient’s daily life (Raymond et al., 2023; Gurgel et al., 2022), but also for guiding and improving personalized hearing aid and cochlear implant fittings (Cone-Wesson & Wunderlich, 2003; Rader et al., 2023; Van Dun et al., 2016; Van Opstal & Noordanus, 2023). However, these variables are not always comprehensively assessed in daily clinical practice, possibly due to the lack of suitable, clinically applicable tests and materials, as well as standardized protocols across centers. The issue of the coexistence of different data management systems and audiological materials persists nationally and internationally, hindering our ability to provide open and statistically significant datasets for re-use and validation (Ait Abdelouahid et al., 2023).

Initial efforts to provide comprehensive normative audiological data across five international institutions and countries were made by Van Esch et al. (2013), who proposed a battery of tests termed the “preliminary auditory profile”. This profile includes various measures of loudness perception, listening effort, speech perception, spatial hearing, spectral and temporal resolution, cognitive capabilities, and self-reported hearing disability. The battery was evaluated in an international multi-center study with two normal-hearing and hearing-impaired subgroups and showed good test-retest variability across centers, although with some differences in language-specific tests across countries. More recently, Buhl et al. (2019, 2020) proposed Common Audiological Functional Parameters (CAFPAs) as a compact but statistically meaningful representation of audiological diagnostics and treatments. This aims to provide real-world clinic databases with a universally applicable audiological datasheet based on Bayesian networks, which is partially robust against the choice of different or redundant audiological tests. A first preliminary set of ten CAFPAs representing information on hearing thresholds, supra-threshold deficits, binaural, neural and cognitive properties of the human auditory system, as well as socio-economic variables, was proposed and validated. As concluded by the authors, further collection of data from real clinical databases should be conducted to train and validate the CAFPA concept or other ML-based diagnostic tool approaches.

Motivated by these previous studies, our multimodal platform aims to integrate various health domains related to auditory function into a single dedicated database. This facilitates the systematic collection of heterogeneous data for hearing research. With this purpose, we have met two technical requirements that we considered important for automatization. Firstly, we integrated XML files containing the audiometric thresholds and raw auditory EEG signals from the Interacoustics proprietary medical devices available in our hospital into our platform. Detailed documentation of the source codes used to import and read these XML files is provided in Appendix B, with the intention of aiding other clinics and research centers using the same equipment. Another milestone was achieved through the integration of this multimodal data with clinical information derived from patients’ EHRs. This was accomplished by successfully connecting the multimodal platform into the Intranet of the Andalusian Health Service. This has allowed us to use international normalized predefined CIE 10 diagnostic and treatments codes, hopefully enabling data accessibility across primary and tertiary care centers in Andalusia and Spain. Moreover, the platform complies with the international standard ISO 13606 (ISO 13606 Standard - EHR Interoperability; Santos et al., 2010), which provides specifications for the structure of clinical information to be shared between different EHR systems. This standard aims to improve interoperability and effective data exchange (Frid et al., 2023). A previous work by Moreno-Conde et al. (2022) provides a semantic infrastructure description of certain terminology subsets and forms included in the multimodal platform developed, thus specifying its associated attributes and how they are mapped into ISO 13606.

Clinical applicability and suitability

A wide variety of tests has been implemented for subjects ranging from normal hearing to hearing impaired, including those with different hearing devices such as hearing aids or cochlear implants. In this way, different test implementations have been defined and selectively adapted to different hearing profiles. A critical factor for clinical applicability is the evaluation time. Currently, the complete battery of tests implemented in our platform takes up to three hours (divided into two sessions): one session of 60 minutes for audiological assessment (auditory thresholds, speech in noise test, auditory electrophysiological measures, etc.) and a second session of about 120 min for cognitive evaluation, including breaks to ensure that patients do not become fatigued. According to our experience, patients with bilateral severe hearing loss, low levels of education and/or advanced age, find it more difficult to complete the entire cognitive battery. Additionally, it is essential to ensure the communication and comprehension of instructions, aided by visual information (i.e. slides, dictation-writing tools) for a more fluid communication. We acknowledge that obtaining a full auditory and cognitive profile across different domains is time-consuming and probably not suitable for clinical use. Conversely, the implemented database and mobile app, allow us to partly automate the process and collect data from patients remotely, successfully integrating it into our daily clinical setup. However, we have also found that some subjects, especially older patients, often report difficulties when using their mobile phones, necessitating in-person interviews. Thus, it is important to evaluate and detect the most relevant cognitive domains and variables related to hearing loss, with the aim of developing more versatile screening tests that could be strategically useful for evaluating patients in daily clinical practice.

Ethics, privacy, and quality issues

As new AI-based clinical decision support systems are increasingly being incorporated into healthcare, remaining challenges such as data quality, bias, ethics, and privacy regulations become more evident and critical. Technical challenges exist to comply with the GDPR while still adhering to FAIR principles. In our platform, pseudonymization and anonymization are addressed at two different levels. Internally, data extraction performed by the researchers is done in a pseudo-anonymized manner using a patient identifier encryption mechanism based on NUHSA codes (Protti, 2007). The patient identifier (NUHSA) is replaced by a code generated using the SHA-512 encryption algorithm (Algredo-Badillo et al., 2012), with a specific seed unknown to the researchers. This mechanism ensures technical and functional separation between the research team and those who carry out the pseudonymization. It includes specific measures to prevent the re-identification of the subjects and unauthorized access to the data, as established in Section d) of Organic Law 3/2018, 5 December, GDPR, for the use of pseudonymized personal data for health research purposes (Ley Orgánica 3/2018, de 5 de Diciembre, de Protección de Datos Personales y Garantía de Los Derechos Digitales). Pseudonymization still allows the re-identification of patients, if necessary (only by authorized clinical and technical staff of the hospital), due to health risks or findings uncovered during the research. However, externally, open datasets available to the scientific community will be fully anonymized. For anonymization we are currently implementing hash and k-anonymization techniques in our data processing pipelines to ensure robust anonymization of data, in line with recommendations provided by the Spanish Agency on Data Protection (Anonimización y Seudonimización|AEPD). Regarding neuroimaging, further efforts should be made to improve standardization and compatibility across different clinics and centers. Currently, we are planning to implement widely accepted standards like Brain Imaging Data Structure, BIDS, (Boré et al., 2023; Gorgolewski et al., 2016), which would help in the FAIRification process of our neuroimaging dataset. Given the sensible nature of the collected information, our research group must carefully study and analyze neuroimaging privacy and usage policies, as discussed by other research groups when creating a neuroimage data repository (Jwa & Poldrack, 2022).

High-quality and non-biased datasets are crucial when used as input for IA algorithms that support clinical decisions (Vicente & Matute, 2023). Different methods have been proposed in the literature to quantify the quality of collected data (Bertossi & Geerts, 2020; Holzinger et al., 2020). Some of these methods are based on rigorous methodological queries to the database, safeguarding data consistency (Levman et al., 2023). Other relevant aspects, especially those recommended for ML models, rely on concepts such as causality or explainability (Livshits et al., 2021; Linardatos et al., 2020). Bias analysis and mitigation must be carried out during the development of ML models to guarantee the quality and accuracy of the predictions (Siddique et al., 2023). All these concepts will be properly taken into consideration to quantify the quality of our data when developing new updates for our multimodal platform.

Limitations and future developments

We provide in Supplementary Material 2 and 3 a description of the variables implemented in our platform, offering an overall view of its capability to collect multimodal data from different sources. Our objective is to make such data accessible for research purposes by the entire scientific community in audiology. Thus, we plan to provide the data collected on our platform in open repositories. Regarding dataset specifications, we have followed the template “datasheets for datasets” provided by Gebru et al. (2021) which aims to facilitate data sharing and communication between dataset creators and consumers. The platform is scalable and permits new questionnaires and data sections to be implemented according to new research goals and projects. Indeed, future developments will implement other relevant variables, such as distortion product otoacoustic emissions (DPOE) or cochlear implant fitting parameters, among others.

Regarding interoperability, we must acknowledge that our platform is still in the initial phase of development, with heterogeneity being only partially addressed. Additional efforts will be required to evolve towards the adoption of other specifications such as OpenEHR (Haarbrandt et al., 2018; openEHR website) or HL7 (HL7 Diagnostic Audiology Reporting Implementation Guide), which will hopefully make our developments more versatile and accessible. Finally, we also aim to incorporate FAIR-compliant metadata to generate research data collections, which would allow universal reusability of datasets for open science research (Wilkinson et al., 2016).

In summary, we present the multimodal platform developed as a step towards a multi-collaborative database in Audiology. Nevertheless, in addition to the need for a common framework and technical platform, support from clinicians in audiology and healthcare stakeholders is crucial. The database could be increasingly used by various agents and organizations at a national level, including researchers and industrial partners, to develop more evidence and high-quality multimodal open datasets for audiological research.

Conclusion

In this work, a multimodal digital platform and database have been developed and implemented in the Otorhinolaryngology Service of the Virgen Macarena University Hospital in Seville to systematically collect audiological and health data from different sources, including auditory thresholds, speech tests, auditory evoked potentials, medical images, quality of life, and cognitive tests, among others. A demo web version of the platform, along with all variables and sections implemented, and the coded scripts to automatically import and read audiological data, are freely available for the clinical and scientific community in audiology. Further joint efforts are still required to generate larger, comprehensive, and open datasets for hearing research.

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Appendix A

Additional materials can be found on the project’s website.