AI-powered solutions are excellent at precisely organizing and evaluating big datasets. By spotting patterns and correlations in data that might not be immediately obvious to the human eye, they are intended to increase diagnostic precision and lower the possibility of error.
Artificial intelligence (AI) systems used in oncology mostly rely on machine learning and deep learning, letting computers learn from massive datasets and classify or predict things. To create these systems, artificial intelligence (AI) models must be trained on enormous volumes of labeled data, such as patient records or medical images, for them to identify particular characteristics of cancer.
a. Image Acquisition Optimization
Frequent imaging procedures for cancer patients expose them to radiation, cumulative contrast doses, and sometimes extended tests (if an MRI is performed). Transferring images from one high-dimensional data space to another is an efficient task for deep neural networks. As a result, oncology patients may benefit from several innovative applications, including decreased contrast/radiotracer dosage, quicker MRI acquisition, and CT-dose reduction.
b. CT-Dose Reduction
A CT image can be transferred from the low-dose or high-noise space to the high-dose or low-noise representation using deep neural networks' ability to map data from one high-dimensional data space to another. To drastically lower radiation exposure, new methods based on deep learning reconstruction (DLR) are presently being developed. In 2019, two DLR solutions were approved by the FDA and made available for clinical use.
Reconstruction techniques based on DLR have reduced radiation exposure and/or enhanced image quality while providing a respectably quick reconstruction time. A recent DLR pilot study described volumetric tomographic imaging that was produced using a patient-specific prior and ultrasparse data sampling, or single projection, which, if confirmed, can further minimize the radiation exposure.
c. Optimization of MRI Acquisition
In oncologic imaging, magnetic resonance imaging, or MRI, is essential. Since whole-body MRI has been available, it can also be used as a staging, therapy response assessment, and surveillance tool in addition to being a problem-solving tool for lesion characterization, local assessment, and tumor staging.
Long scan durations can cause motion artifacts, increase costs, and cause pain for patients, making it one of the most difficult problems to deal with. These problems might be resolved by recent advancements in AI. For instance, under-sampling in deep learning-based techniques has been used to speed up MRI scan times. These techniques fall into numerous categories and include image-based reconstruction, k-space-based reconstruction, adversarial networks, and super-resolution.
