Medical research scientist working on a desktop computer with automatic segmentation of the results of medical imaging on the screen in a modern scientific research laboratory. Medical staff in white coats in the background.

2022 marks another new year and time for digital transformation in healthcare. After many changes caused by the pandemic in the last two years, the future of the healthcare sector is expected to bring new innovations and technologies in the coming years.

The distribution of the medical picture is expected to improve in 2022 as never before, which will be a turning point in everyday clinical practice through artificial intelligence (AI). What is the layout of the medical image?

Medical image segmentation involves the extraction of areas of interest (ROI) from image data, such as magnetic resonance imaging (MRI) or computed tomography (CT) scans.

The primary purpose of disseminating this data is to identify areas of anatomy that are necessary for a particular study or further analysis, such as mimicking the physical properties or locating roughly CAD-designed implants in a patient. As a result, sharing manual medical images is a tedious task.

However, recent advances in AI software have made it easier to quickly complete routine tasks like this without compromising quality.

Accelerated by artificial intelligence

The healthcare industry has witnessed an extraordinary level of success and achievement with artificial intelligence-driven techniques in countless areas of science and industry, with an extensive analysis of medical images. With the availability of high-performance hardware and a variety of software tools, recent advances in traditional machine learning and deep learning mean that it is much easier than before in the clinical setting.

However, only recently have we scratched the surface of what can be achieved by implementing artificial intelligence. There are many tasks and processes that remain opportunities for efficient automation using data-based algorithms.

The process of transforming image data into measurable and quantifiable functions can force users to generate and enable data for diagnostic or predictive artificial intelligence models, from improving the clinical workflow by automating planning and operations to improving the quality of medical images to registration, distribution and classification. - managed decisions.

Special AI algorithms can be used in the process of medical development, especially in oncology. This technology is also used to analyze different organs from different types of studies, such as CT, MRI and PET. It allows you to extract new biomarkers from medical images, correlates a myriad of features to find patterns that cannot be obtained by conventional means.

Finally, machine learning professionals are now able to create advanced solutions for medical imaging and deep learning medical imaging.

Advantages of medical image analysis in practice

Medical image analysis is undoubtedly one of the most important techniques used by radiologists and medical physicists. Accurate characterization of abnormal tissue is key to determining the course of treatment as a trajectory of drug discovery, as it allows professionals to quantify and evaluate treatment efficacy and patient response.

As a direct example, suppose we want to understand the volume characteristics of a brain tumor in a longitudinal study. In this study, one patient was scanned multiple times and multiple MRI scans were examined by a single reader. In an ideal world, a perceived reduction in the number of tumors acquired would mean that everything is fine, but this reduction could easily be due to poor reproducibility and evaluation variability associated with manual manipulation.

This is where AI comes into play. As a powerful software tool that can deliver automatically, in a completely innovative way, Artificial Intelligence is able to directly analyze and provide targeted, repetitive measurements of areas of interest. there is no room for error.

The future of medical image sharing

Artificial intelligence algorithms not only help us build automated channels for long-term tasks, such as sharing medical images, but also help healthcare professionals look beyond the visible. participation that people cannot see for themselves. However, these tools need to be fully validated and validated on large, unbiased data sets to be suitable for clinicians, as their results can have multiple consequences.

Therefore, having a sufficiently in-depth model is a good first step with follow-up actions, which will include the design and implementation of theoretical and experimental validation of key pipeline processing elements. access.