Novel Interactive BRAINTEASER Tools for Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS) Management

The BRAINTEASER Project (BRinging Artificial INTElligence home for a better care of Amyotrophic lateral Sclerosis and multiple sclERosis), funded from the European Commission Horizon 2020 programme grant number GA101017598 until 2024, aims to integrate societal, environmental and health data to develop patient stratification and disease progression models for Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS). ALS and MS are both very complex chronic progressively degenerative neurological diseases significantly disrupting the quality of life of the patients and their families, but with very different clinical picture, evolution, prognosis and therapies.

BRAINTEASER integrates detailed retrospective and prospective clinical datasets with comprehensive heterogeneous personal health, activity, lifestyle, habitual/behavioural, and environmental data collected using commonly available sensing/IoT devices and the demonstrated first release of developed interactive tools for disease monitoring and management.

The collected data drive the development of Artificial Intelligence (AI) models able to address the needs of precision medicine, enabling early risk prediction of disease fast progression and adverse events, and the next planned releases of the tools currently in development will focus on the integration of the AI models and their outputs into generated specific highly complex feedbacks to the users (digital gamified coaching and recommendations to improve the patient’s and caregiver’s daily routines and overall well-being, and the analytics suite for full patient cohorts management according to stratifications output of the AI models and for interactive visual data exploration and detailed understanding of the disease progress and evolution of patient’s conditions).

The demonstrated tools are being devised and developed embracing an agile user-centered multidisciplinary co-design approach, accounting for the specific technical, medical (including psychological/cognitive) and societal needs of the users. Proof-of-concept of their validated use in real clinical settings in 4 clinical study validation sites (Lisbon, Madrid, Pavia & Turin) aims to provide quantitative evidence of benefits and effectiveness of leveraging AI models in healthcare pathways for dire neurodegenerative diseases, and a set of recommendations for the health authorities to support the transition of the current approach to healthcare from reactive to predictive, paving the path towards a healthier and more fulfilling life as long as possible.

The presented tools are primary end-user-oriented components of the overall BRAINTEASER platform ecosystem, supported by the underlying unified Data Platform, Semantic Cloud, middle tier of integration service APIs, and others as presented on Fig. 1 above with relevant main data streams and the involved external relations, including Project Open Science and Evaluation Challenge actions already underway. All together in turn provide support for the identified processes enhancing the management of ALS and MS both throughout the Project demonstration study and the care provision practice beyond, differentiated and structured through comprehensive analysis and co-design with relevant stakeholders as presented on Fig. 2 below, and progressing beyond the thoroughly analyzed key features of current relevant state-of-the-art ALS & MS management tools and applications like Emilyn¹, Cleo², and over 50 others relevant analyzed and compared in terms of specific features and functionalities.

Some of the platform functionalities and architecture build and extend upon preceding and parallel related projects and initiatives tackling common issues and exploiting applicable methods in neurodegenerative progression management [7], well-being [8], functional decline prevention [9], and public health [4, 5], such as PULSE³, City4Age⁴, NEVERMIND⁵, or VERITAS⁶.

One of the set out goals of BRAINTEASER is also to analyze the possible effects and influences of environmental and daily living ambient factors (primarily air pollution) on the aetiology and progression of neurodegenerative sclerotic diseases like MS, as has been intensely studied in recent years [1‐3] along with water and soil pollution, and the role of these factors on progression and relapse of MS and ALS, feeding the relevant collected environmental data into the dedicated developed AI models for patient exposure to the ambiental factors, complementing the models for disease progression and stratification. The cities with clinical trial sites involved in the project (Lisbon, Madrid, Pavia, Torino) have established extensive environmental sensing and measuring infrastructure, ingesting data both from public environmental measuring stations wherever open or available, as well as from connected IoT sensing devices deployed by BRAINTEASER and previous or parallel relevant projects like (again) PULSE, eMOTIONAL Cities⁷, Sharing Cities⁸, ACROSSING⁹ and others [4‐6], and complemented also with data from portable/wearable compact sensing devices (carried on or with patients) deployed by the project, mostly Atmotube PRO monitors¹⁰.

Three main interactive tools are being developed to support the processes illustrated on Fig. 2 above, differentiated and tailored towards specific target end-user roles and distinctly siloed functionality groups required for each group of roles:

In this initial release, the mobile app. for patients/caregivers and the Clinical web Tool are primary, to support the key required functionalities of the Project study, enrolling the patients and:

as diagrammed on Fig. 3 below.

Next subsequent version in current development is focused on key “intervention-related” features listed on bottom left and bottom right of Fig. 3, providing support to patients, caregivers and clinicians to fully manage, prevent and mitigate disease progression, from basic advisory guidance and instructions to advanced cognitive games and exercises, or voice/speech assessment in evolution of bulbar symptoms.

Example screen form for support service configuration in the MS-specific variant of the Clinical Tool is provided on Fig. 4 below and the detailed breakdown of UML use cases notation of basic functionalities of the Patient App. on Fig. 5 below.

The third listed Interactive Data Management and Exploration (dashboard-based) Tool is in initial version development as it significantly depends on and is intended to integrate substantial output from the AI models which are still in development and testing, but at this stage provides at least rich interactive visual exploration of arbitrary selection of collected temporal and heterogeneous data variables (including categorized and continuous), with values evaluated or compared combined together in multimodal and multidimensional composite 2D and 3D visualizations, as exemplified on Fig. 6 below.

The tools are designed to be modular, dynamically adaptable, personalized and supporting alternative user interaction modalities (with significantly higher accessibility than common for “ordinary” apps, adapting to the increasing manual dexterity impairments of the patients), with simple and scalable navigation structure, optimal balance between menu structure and dynamic interaction flows, and designed for long-term usage, in the UI/UX aspects.

In the overall architecture of the underlying supporting BRAINTEASER data collection, management, and provision ecosystem (Fig. 7 above), similar microservice modularity methodological principles have been followed, including the separation between domain-specific and infrastructural/utility orthogonal logic, and taking into account the system and service performance and security aspects built into the design and architecture from the start, employing loose coupling and service orchestration patterns like the baseline API Gateway facade pattern that may evolve into more comprehensive orchestration pattern (like ESB) if complexity or performance criticality increases in the ongoing iterative further advancement of the architecture.

The presented demonstrated working tools in the initial version constitute the foundation of the novel ALS and MS management and monitoring leveraging extended IoT sensing and emerging instruments infrastructure, and a basis for integration of more advanced and effective AI models (in development) for disease progression prediction, patient stratification and ambiental exposure assessment.