A survey by the Journal of the American College of Radiology (JACR) reveals that 90% of the practicing radiologists in the United States reported an increase in their workload in the past 3 years. While around 28% admitted that the increase was more than 20%, about 78% of these 90% radiologists attributed the primary reason for the heavier workload to the increasing number of scans that need to be analyzed quickly and accurately. The power of Artificial Intelligence (AI) can help medical professionals by assisting them in skimming the scans with better precision and further raise the value of medical imaging in a manner yet to be explored.
Potential of AI in Medical Imaging
AI technologies can significantly save radiologists’ time and efforts by quickly sifting through millions of images and identifying potential abnormalities and patterns accurately. They can drastically reduce the risk of oversight as well as misdiagnosis in their analysis.
For instance, one study in 2019 reveals that AI could successfully identify and distinguish between the different diseases in chest radiographs – pneumonia, active pulmonary tuberculosis, pneumothorax, and malignant pulmonary neoplasms, including metastasis and primary lung cancers.
According to another study, AI-based computer-aided design software marked fewer false-positives per image in digital mammograms with the same sensitivity as the FDA-approved computer-aided design system.
AI algorithms can drive more efficient workflows for medical professionals with more accurate readings and significant time-savings with a capability to detect abnormalities quicker than even the most-skilled human eye. With almost 30% of the respondents already utilizing AI in their patient care imaging, according to the Definitive Healthcare survey, the Data Bridge Market Research report says that the global AI medical imaging market is expected to rise to $264.85 billion by 2026 from $21.48 billion in 2018.
AI-based medical imaging is used in a range of applications as follows:
- Diagnosis and Surgery
AI is used to engineer algorithms to categorize images of lesions in skin and tissues. A video contains around 25 times more data than that of high-resolution diagnostic images like CT and thus, offers higher data value based on resolution over time. A laparoscopic procedure video analysis has provided 92.8% accuracy in identifying all steps and detecting unexpected or missing actions.
- Deep Learning and Medical Image Recognition
AI can identify imaging pattern changes that are not easily detectable to a human reader. Brain MRI analysis by machine learning can detect tissue changes suggesting ischemic stroke within a short time window from symptom onset with greater sensitivity than humans.
Convolutional Neural Networks (CNNs) present a more promising image recognition technique. Influenced by the human visual cortex, CNNs help classify the image in the right category, similar to how a radiologist categorizes it.
- Augmented and Virtual Reality in the Healthcare Domain
Beneficial for educating experienced surgeons and medical students for a specific specialty, AR and VR is also useful for offering personal rehabilitation physiotherapy and relaxation to patients experiencing debilitating conditions.
How Clara Toolkit with NVIDIA GPU Helps Build Efficient AI
GPUs offer the essential compute capabilities to devices in the medical imaging domains, including MRI, CT, ultrasound, and x-ray. Deep learning research further focuses on developing enhanced approaches to enable AI-assisted workflows.
NVIDIA Clara offers the tools to make data annotation training and deployment seamless for medical imaging applications. It lets the researchers and developers quickly create AI-assisted high-quality annotated data using Clara SDK APIs. You can train an AI model from scratch or promptly adapt and train an existing model with as little as 1/4th training data and time by selecting from the 24 available, pre-trained AI models, and a python-based library with APIs. The Clara deploy SDK streamlines model integration into your current medical imaging system for seamless communication with your PACS environment.
You can monitor the processing of different stages of the pipeline using the Argo dashboard and visualize the AI inference results using any standard medical viewer that can read DICOM. All of the Clara AI SDKs are supported for on-premise and in a cloud deployment. The modular nature of these SDKs makes them customizable for some of the features and capabilities or even helps create new workflows.
How E2E Cloud Can Help You Build AI with Clara Toolkits Using NVIDIA GPUs
The NVIDIA virtualization technology facilitates healthcare professionals to access the world’s most preeminent visual computing platform, Quadro, from a virtual workstation from anywhere, on any device. E2E Cloud enables healthcare institutes to benefit from this compelling and innovative technology by providing remote access for 3D volumetric image viewing and editing to radiologists, medical imaging specialists, and physicians.
E2E Cloud is an India-focused Cloud Computing company and a pioneer of contract-less cloud computing to Indian SMEs and startups. Trusted by over 10,000 clients, E2E’s GPU cloud offers low latency and up to 70% cost-savings while providing health professionals with accelerated machine learning and deep learning.