Dynam.AI clients have one thing in common, an innovation mindset and the desire to make their image data smarter. The following use cases are a glimpse of what is possible.
Deep Learning applied to image-recognition functions helps with recognizing complex patterns in the images and provides assessments of medical characteristics.
AI models can be an effective tool for analyzing medical ailments like cardiovascular abnormalities, lung diseases like pneumonia, the development of tumors and melanoma, and checking for fractures from high-resolution medical imagery. This helps to provide timely treatments to patients. Computer vision solutions can help identify coronary calcium scores, detect cardiovascular disease, blood glucose levels, and the flow of blood through the body. Medical imagery personalizes medical plans, decision making, and long-term care. Computer vision helps doctors with image annotation, image segmentation, multimodal image fusion, tumor detection, and other innovations helping doctors create more effective treatment plans. Overall, medical imaging covers such disciplines as X-ray radiography, magnetic resonance imaging (MRI), ultrasound, endoscopy, thermography, medical photography in general, and more. The main goal of medical image analysis is to increase the efficiency of clinical examination and medical intervention.Medical Devices Brochure
As manufacturing facilities are transitioning towards fully automated manufacturing, the requirement for more intelligent systems to monitor industrial processes and outcomes is increasing.
While the Internet of Things (IoT) is revolutionizing the manufacturing sector and making industrial operations more autonomous, machine vision is being used for quality inspection of manufactured products for the detection of non-conformities and defects. Significant advancements in computer vision and deep learning make robotics more powerful. Deep learning-based machine vision allows robots to make complex decisions about parts classification, allowing for even less human interference and more accuracy. Embedded algorithms also allow machines to accurately identify defects, which is invaluable for cost-efficiency and overall production effectiveness. Industrial IoT coupled with computer vision is the next transformative power coming to the manufacturing sector. IIoT improves businesses’ visibility over their operations, allowing for remote control and faster decision-making. Instead of sending unsorted, raw data to other locations for further analysis, machine vision-powered robots can now make decisions on the spot.
With the amount of data available to scientists worldwide, it becomes crucial to rely on artificial intelligence and machine learning to sort through the huge data lakes, carry out the data analysis tasks, and progress at a faster pace with drug discovery.
AI can use existing patient data and computer vision to simulate chemical interactions and predict behavior based on genomics. Deep generative models create realistic samples from training data, getting around medical data silos. Early studies showed AI’s accuracy in detecting a disease state to be one percentage point higher than medical professionals and two percentage points higher in accuracy for all-clear declarations. Biotechnology can be categorized into a few types like agricultural biotechnology, medical biotechnology, animal biotechnology, industrial biotechnology, and bioinformatics.
Computer vision plays a significant role in a wide range of security applications. Computer vision systems attempt to construct meaningful and explicit descriptions of the environment or scene captured in an image. Below are a few examples of how our team can apply AI to computer vision security applications to help you keep people, property, and the spaces where we live and work safe and secure.
- Cybersecurity – with deep learning, new applications of computer vision techniques have been introduced and are now becoming part of our everyday lives
- Face recognition, indexing, and photo stylization
- Machine vision in self-driving cars
- Port security and cargo inspection
- Facility security for governments, power plants, banks, hospitals, offices, etc…
- Video surveillance for offices, ports, parking lots, parks, stadiums, malls, train stations, etc…
The challenge is not in acquiring surveillance data from these video cameras, but in identifying what is valuable, what can be ignored, and what demands immediate attention.
According to the 29th Annual Retail Technology Study by RIS, only 3% of retailers have already implemented computer vision technology, with 40% planning to implement it within the next two years. The customer experience can be redefined by making store layout improvement decisions based on real data rather than guesswork. Consumers expect as much personalization and convenience in retail stores as they experience online. Applications enabled by computer vision such as product scanning, virtual mirrors, OCR-based inventory management, and more will drive the future of successful brick and mortar retailers.
The potential for AI and machine learning to enable and improve computer vision systems is almost without limit. Reach out today to learn how Dynam.AI can help you get the most out of your image and video data.