The medical devices industry continues to be a hotbed of innovation, with activity driven by increased need for homecare, preventative treatments, early diagnosis, reducing patient recovery times and improving outcomes, as well as a growing importance in technologies such as machine learning, augmented reality, 5G and digitalisation. In the last three years alone, there have been over 450,000 patents filed and granted in the medical devices industry, according to GlobalData’s report on Artificial Intelligence in Medical Devices: 3D Blood Vessels Image Analysis.
However, not all innovations are equal and nor do they follow a constant upward trend. Instead, their evolution takes the form of an S-shaped curve that reflects their typical lifecycle from early emergence to accelerating adoption, before finally stabilising and reaching maturity.
Identifying where a particular innovation is on this journey, especially those that are in the emerging and accelerating stages, is essential for understanding their current level of adoption and the likely future trajectory and impact they will have.
150+ innovations will shape the medical devices industry
According to GlobalData’s Technology Foresights, which plots the S-curve for the medical devices industry using innovation intensity models built on over 550,000 patents, there are 150+ innovation areas that will shape the future of the industry.
Within the emerging innovation stage, AI-assisted radiology, motion artefact analysis, and treatment evaluation models are disruptive technologies that are in the early stages of application and should be tracked closely. MRI image smoothing, AI-assisted EHR/EMR and AI-assisted CT imaging are some of the accelerating innovation areas, where adoption has been steadily increasing. Among maturing innovation areas are computer-assisted surgeries and 3D endoscopy, which are now well established in the industry.
Innovation S-curve for artificial intelligence in the medical devices industry
3D blood vessels image analysis is a key innovation area in artificial intelligence
This technique utilises 3D imaging and reconstruction to analyse blood vessels, to provide anatomical information such as blood-vessel orientation, cross-sectional area and volume which can them be used by cardiologists to diagnose, prognose and plan treatments for cardiovascular disease.
GlobalData’s analysis also uncovers the companies at the forefront of each innovation area and assesses the potential reach and impact of their patenting activity across different applications and geographies. According to GlobalData, there are 40 companies, spanning technology vendors, established medical devices companies, and up-and-coming start-ups engaged in the development and application of 3D blood vessels image analysis.
Key players in 3D blood vessels image analysis – a disruptive innovation in the medical devices industry
‘Application diversity’ measures the number of different applications identified for each relevant patent and broadly splits companies into either ‘niche’ or ‘diversified’ innovators.
‘Geographic reach’ refers to the number of different countries each relevant patent is registered in and reflects the breadth of geographic application intended, ranging from ‘global’ to ‘local’.
Patent volumes related to 3D blood vessels image analysis
Source: GlobalData Patent Analytics
Heartflow is one of the leading innovators of 3D image analysis within the Cardiovascular Sector, based on the number of patents published since 2010. Some other key patent filers looking at cardiovascular 3D-Image analysis include Koninklijke Philips, Subtle Medical, Samsung Group, Envista Holdings and Caption Health.
In terms of Geographical Reach, Circle Cardiovascular Imaging leads the pack, followed by Synaptic Medical and Toshiba Medical Systems in second and third spots respectively. By means of application diversity, Enlitic. held the top position followed by International Business Machines and SymphonyAI Group.
3D image analysis in the cardiovascular space has become the de facto modality of choice in recent years among cardiologists for non-clinical applications, largely displacing the traditional model-based and atlas-based methodologies. Traditional techniques have required significant prior therapeutic knowledge to gain accuracy. However, the AI-driven (or deep learning) techniques have facilitated automatic discovery of clinically significant intricate features for cardiac image segmentation and object identification. 3D image analysis still has some way to go before full real-world implementation to relieve clinician workload, but it is expected that this technique will become the dominant clinical modality in the coming years.
To further understand the key themes and technologies disrupting the medical devices industry, access GlobalData’s latest thematic research report on Medical Devices.