Scandicode Oy (Ltd.) is a young Finnish start-up established in 2016 and funded in seed-investment round by Nestholma, Oulu Innovation and ELY-center. Developing AI (Artificial Intelligence) in startups is similar to jumping off the cliff and building own wings on the way down. AI is also rapidly in changing at breakneck speed. To a startup, the preoccupation is concerned much healthier not only with short-term earnings – as long as anything brought in cash – but also to its long-term ambition doable under such cash-flow. We believe our ambition can be much more defensible if as an open evolution together with all business clients to gain or share continuous intelligence from WHAT-WE-DO, like a big diamond willing to get cut:

Global Growth Markets

Senior population and the proportion with cognitive issues are increasing rapidly while healthcare/eldercare is lacking resources. The cost of Alzheimer’s and dementia can reach $1 trillion as the status in 2018, including healthcare, long-term care and hospice. As the digitalisation rewriting every industry, technology innovation is vital to offering operational optimisation or opening-up of new opportunities as follows:

  • Only 25% of people with Alzheimer’s disease have been diagnosed, so that the rest are in unknown or unidentified risks as a huge uncertainty to their life. An app-based tool as digital pre-screening is thus needed to provide rapid assessment and detect cognitive disorders, at least better than using pen / paper traditionally in a time-consuming / expensive way.
  • To seniors’ welfare or wellness sectors, questionnaires and self-reports are still mostly applied by using checklists to do the assessment of assisted living, skilled nursing, and/or in-home care assistance. New breakthrough is also required for those purposes to continue there even further: as a matter of choice preferably not only as a digital-assessment but also with AI (Artificial Intelligence) for front-line interacting.
  • Finland is one of the pioneer countries aiming to create huge value for elderly independent living and successful aging. Beyond human-machine interacting or learning, the innovation can be expected for giving the seniors more time in good health if all the levels of elder care and/or potential care needs are handled smoothly in changing.

Global market size/growth is even more excited if eventually expanding the business across those relevant industries:

  1. AI software is estimated to grow from $643.7 million in 2016 to $36.8 billion by 2025 as compound annual growth rate (CAGR) of 56.8%.
  2. Global Digital Health Market is accounted for $182.63 billion in 2017 and is expected to reach $665.36 billion by 2026 growing at a CAGR of 15.4%.
  3. Over the period of 2018-2026, the US$ 999 Mn Global Elder Care Services Market is likely to thrive at 8.5% CAGR – to touch US$ 1,919.8 Bn by 2026.

These markets are for Scandicode Oy when building new things locally in the present, as well as growing to succeed in the future to a global scale.

 

SIMO Cognitive Assessment (Medtech Version – Finland and/or EU markets)

Cognition is how a person understands the world and acts in it, for tasks ranging from the simplest to the most complex. We assess cognition for elderly people such as learning, remembering, problem-solving, and paying attention. Our SIMO Cognitive-Assessment Product is an instrument of neurocognitive assessment with simulation-based software developed by Scandicode Oy (Ltd.) and installed to a multimedia device: such as Samsung Galaxy View tablet with or without a stylus pen (as an option for some of elder users to improve accuracy).

INTENDED USES IN SHORT OR LONG TERM

The goal was initiated as a digitized version of neurocognitive assessment in comparable or contrast with traditional pen/paper-based method, such as Mini–Mental State Examination (MMSE) and The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD), as well as growth beyond healthcare also discussed with other sectors:

  • The particular technical characteristics of our product are intended to digitize, visualize and assess older people’s neurocognitive conditions as an alternative tool. A deviation from normal i.e. an abnormal status of the user can be qualified, analysed and tracked with feasible repetitions of the assessment.
  • During clinical evaluation, the product as a reusable tool must also show its excellent safety / usability to elderly end-users for neurocognitive status recognition in healthcare purposes.
  • When applying AI technologies fully in the future, such cognitive condition will be able revealed ideally as the data in clinical form, stage, severity, symptoms or aspects with the evidences from multiple sources (such as eldercare or elders’ home).

DEVELOPMENT PROCESS KNOW-HOW

In Scandicode Oy (Ltd.), the particular technical characteristics of our solution are explored at first as a MVP (Minimum Viable Product) demo version, and then productized by intended uses or customer needs accordingly to clinical-trial or market-release version with the deliverables as follows:

  • Based on the principles of design control for medical device, as well as popular agile or lean methodologies, own “3in1” KANBAN System has been applied in order to support new Innovation properly as a startup.
  • It is reasonable to leave the milestones away due to progresses of development work much faster and more dynamic in start-up environment. Or, it could also mean those virtual milestones are going through the process much faster and more often.
  • All of the work with relevant deliverables (medical devices and/or non-medical devices) should be still kept, or checked interactively and dynamically, to ensure same quality required by market needs and regulatory guidance.

KANBAN

It is business model innovation applicable for developing medical devices and/or non-medical devices similarly or simultaneously via such a “Leagile = Lean + Agile” process as Health-Tech startup. Transparency or translucency of our R&D – design control illustrates the novelty of being new and unusual with all modular parts easily reusable, aiming and delivering to different markets / end-users for multiple purposes.

 

SIMO Cognitive Assessment (Wellness Version – In R&D prototype to productisation)

It should be emphasised about this R&D project still on-going to learn or update new knowledge continuously. The best cognitive assessment should be from AI-enabled solution to fight or repair age-related problems ultimately, not just above product of Medtech Version as an interface of simulation testing. But intended not as medical device now, we are aiming to a digital reinvention for distinguishing between “normal” cognitive decline with increasing age and the decline preceding the onset of dementia. With such additional factors from cognitive know-how, new innovative solutions and applications can be oriented by the merge knowledge of human cognition and artificial intelligence:

  1. In further distinction now beyond the sub-tests, extra data out of MedTech field will be increased and learned obviously for elderly wellness instead, even supporting prevention and intervention as main purpose.
  2. A hybrid supervised/unsupervised ML (Machine Learning) approach is selected with both task-driven (to predict next value) / data-driven (to identify clusters) functions to apply deep reinforcement ML, which is as bio-inspired design or engineering based on those scientific references:

 

Reference 1: “Major Report Shows What Works, What Doesn’t, for Better Brain Health”

 

Reference 2: “Enhancing cognitive capacities over the life-span”

Deep reinforcement learning from such scientific preferences can solve complex tasks not yet via the reward function, but by teaching a little percent of the feedback instead in our design thinking. A prototype of neural network has been testified virtually for human teaching and interacting as the kick-off to this direction. R&D exploration is mainly for finding unrealized patterns or underlying structure of new data in or out of clinical environment. The feasibilities can be studied for deep learning algorithms, multiple processing layers, real-time optimal control or adjustment, performing parallel and series tasks dynamically in the future.

Without any disposing of confidential information, the triggers and outcomes can be briefly discussed here with some details focused just on AI technical characteristics. For example to get hands-on experience, MS Excel was used to reorganise and analyse clinical-trial data in a spreadsheet (such as sub-test scores / measurements of elderly cognitive assessment). It means as human learning input and output possible from hand labeled data to be machine-readable formats. Both real-world and synthetic datasets can be involved across cognitive domains to seek or increase more of multiple evidences at the end. Here are about inputs and outputs from our studying the prototype of neural network based on unsupervised machine learning in comparison with human teaching as follows.

Step 1 – A case for human learning at first:

Step 2 – Human teaching to create AI prototype:

 

Step 3 – The outcomes of neural network comparable to human learning / teaching:

Besides, these 3 steps just utilised 80% of clinical trial data for creating the neural network and kept 20% of other “clear” cases of those sub-test scores / measurements typical to each cognotive domain, possible for further verifying of unsupervised machine learning. Clinical trial data can have many permutations and/or combinations for above cases’ selecting into these two groups. Data amount can be also further doubled up virtually if “sensitivity” and “specificity” of elderly cognitive assessment will be able also studied from real-world inputs to synthetic datasets.

 

R&D partnership offer as our “eXploration-Service”

On the list of Top 20 Reasons Startups Fail, the 1st (42%) and 2nd (29%) percentage are “NO MARKET NEED” and “RAN OUT OF CASH”. In medical device industry, new startups need several years to approach the status for a marketable product with CE mark or FDA compliance for those 2 practical questions to be answered:

  1. Is it meaning almost equal to these two categories when taking so long time to go through such a phase without big seed-investment? For example, we have experienced CE marking process for class I of medical device under MDD 93/42/EEC, but with smaller value if not to take decisions alone with diagnosis or therapeutic purposes. If the product will become to future class IIa or higher in 2020 under MDR 2017/745, the value will become larger but with huge cost / time for our products to be clinical decision support or physiological monitoring software (to allow direct diagnosis or monitoring of vital physiological processes).
  2. How to survive the valley of death by dealing with short term earnings pressure – as long as anything brought in cash? According to Gartner research, 2018 has marked the beginning of AI democratization. “85% of CIOs will be piloting artificial intelligence programs through a combination of buy, build, and outsource efforts” by 2020. For MedTech and/or any industry that truly need AI soon for strategic experimentation, it can be necessary to hire more experts and/or purchase specialized services. This is the opportunity for our “eXploration-Service” able to solve own challenges as a must and also create win-win outcome for exponential growth together in B2B partnership.

The reasons to seek R&D-acceleration beyond MedTech or Health-Tech:

  • Due to low-cost limitation to startup business, it has been challenged not easy to achieve large enough value for an affordable risk / benefit profile, if just determined to Class I of medical device.
  • Via refreshing overall-care of the elderly, non-medical devices can be as a speed-up option comprehensively to apply health technologies, but still requiring undoubtedly safe, effective, and fit for intended uses.
  • To collect big-data from scratch in both fields, a long journey is very likely required by developing AI to break the boundaries of medical devices and/or non-medical devices before further success comes to us.

R&D-acceleration as a service provider OK to other use cases:

With our deep insight from own AI-strategies (similar to intellectual property strategy), open disruptive innovation is thus to seek other use cases for short term earnings and develop Reinforcement Learning (RL) know-how faster. At least, the key is to serve our B2B customers if with big data available already in or even beyond MedTech or HealthTech, instead of collecting big-data from scratch as what if possible to avoid.

Our “eXploration-Service” is offered as a paid or free service only in Reinforcement Learning (RL) field, including as IT blue-collar workers to parse and catalog “raw” data. For instance, same AI can be applied to other industrial sectors without losing the focus of equivalent knowledge growth from more scheduled-tasks, as well as better cash-flow to startups’ survival / earlier value-in-use to customers. In principle, it can be as marketing-interactions steadily to any interests (cognitive science, geriatrics, healthcare or health, medical device, regulatory compliance, ICT, new market-studies, to formulate AI strategies or modularize R&D processes, PoC or fast-prototyping, PR or sales, etc.). But among them, Business Intelligence or Intelligent Automation can be for Reinforcement Learning (RL) applied to Industry 4.0 optimisation by human preferences effectively to solve complex tasks without access to the reward function. Our “eXploration-Service” is a new way not just limited by the startups’ challenges but challenging the limits instead!

Example:

The development path of the OEE (Overall Equipment Effectiveness) has been also as using Microsoft Excel, to IT or BI tool, and now AI – very similar to what to do for MedTech AI. If the manufacturing process – Availability, Performance and Quality can be as human preferences, will OEE data be more available and suitable to develop RL (Reinforcement Learning) solution itself faster in comparision with collecting health/healthcare data from scratch? 

It can at least create cash flow in startups to provide such a service but focused still on a same AI and solve OEE local-optimisation issue for the clients (if involving extra data from Availability, Performance and Quality as AI for better optimisation than normal BI tool). There will be a lot of new opportunities leading to biologically-inspired engineering and/or newest business biomimetics: AI to learn Availability, Performance and Quality in business or industrial context as a new type of “cognitive assessment” and implement PwC’s Concept ofThe Bionic Company at both sides!

Due to own product or project development in MedTech or Health sectors, our RL interests have a limit of changing from low-level as trial-and-error learning to high-level as deliberative planning. With such a focus, it can help “eXploration Service” of conceptualisation, PoC prototype or final products undoubtedly safe, effective, and fit for intended uses as our special reason.

 

Interesting to our SIMO product (MedTech Version), on-going project (Wellness Version) or “eXploration-Service”?

 

Welcome to contact us as business customer, partner or investor if for client-specified details!

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