We rapidly build custom Computer Vision models trained against your unique data set, that you fully own.
We can take a year's worth of normal Data Science work and and turn it around in 30 days thanks to our in-house Computer Vision toolsets.
AI computer vision systems typically require training on a vast collection of labelled images in order to function effectively. This training process allows the algorithms to learn the characteristics of different objects and scenes, and to develop a model that can be used to recognize and classify them.
After completing the training process, the system can then be utilized for the analysis of new images and videos. The AI algorithms will process the visual data and use the trained model to identify and classify objects and scenes in the images. This analysis can then be used by other systems or applications, such as facial recognition systems or image search engines.
AI computer vision works by using machine learning algorithms to analyse and understand the content of images and videos. It is trained on a large dataset of labelled images, and then uses this knowledge to identify and classify objects and scenes in new images and videos.
After we have built your solution we deploy it securely to the cloud and monitor its performance over time, providing you with the peace of mind of Deeper Insights handling the end-to-end production of your critical business computer vision product
In data poor environments we can synthesise datasets automatically, to create enough training data to train a computer vision model.
We provide complete automation from data ingest to model deployment to model monitoring in the cloud.
Open source models can be custom trained on the clients data set by one of our Applied AI Data Scientists.
As there is a lot of complex motion and granular detail, surgery image data is very complex to analyse. Our consortium, which is comprised of Smith&Nephew Ltd, Deeper Insights and Imperial College London, won Innovate-UK funding to tackle this problem.
We developed custom Computer Vision algorithms using NN's - Deep Learning to identify body parts in medical images. This leads to Markerless Navigation - the ability to detect where to cut bone on a knee for a knee replacement in Robotic surgery.
In the UK, the total number of Total Knee Replacements (TKA's) per year has increased from 13,546 in 2003 to 98,147 in 2019 costing the NHS an estimated £585m per year. The average cost of a TKA in the UK is £12,000, however, post-surgical complications, e.g surgical site infection, increases this cost by between £1618 and £2398 per patient.
Our ambitious and innovative project focussed on developing markerless and automated registration to track the patient's limbs. This was tailored for robotic-assisted orthopedic procedures using structured light technology assisted by deep learning to continuously capture the patient's anatomy during surgery.
This new platform will be integrated within S&N's commercially available robotic platform 'NAVIO', which was previously supported by I-UK funding, and will obviate the need for percutaneous markers reducing set-up time, cost and complexity during surgery.
This results in faster than real-time image segmentation, with above 90% accuracy, of the human anatomy.SPEAK TO A SPECIALIST
We are data science experts recognised globally with over 500+ citations and patents.
We specialise in Computational Linguistics, Natural Language Processing (NLP), Machine Learning, Deep Learning and Data Analytics.
We use the latest AI technology, tools and techniques to enable businesses to onboard data quickly and at scale and harness a variety of models and AI applications to gain fresh insights that support more informed decisions.
We work in partnership with our clients to ensure we find the best solution to solve their impossible problem and drive positive business outcomes that make a measurable impact on performance, innovation and efficiency.
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Smith & Nephew