There are different levels where AI can be applied in radiology as well as other diagnostic imaging applications, depending on the step in the workflow from acquisition to interpretation, post processing and analysis.
The first level is at the Image Acquisition level. For example, one of the challenges with doing a CT scan is to have the patient center coincide with the center of the radiation beam, which will result in optimal dose distribution and corresponding image quality. In addition to the patient being centered, the distribution of the radiation dose, depending on the body part is also important, e.g. a lower dose for the head than for the pelvis. Instead of having a technologist making an educated guess, the machine can assist with this and automate the positioning process, again to optimize dose which means not using more than necessary.
De-noising of images is also an important feature. Typically, with lower radiation techniques, more noise is created, which can compromise a diagnosis and ultimately patient care. This is especially true for screening, where there is no direct indication to perform a CT study and limiting dose is important. An algorithm can be taught what noise looks like in a typical low-dose image and uses that knowledge to apply image processing to remove the noise to allow a lower dose technique to be used. The same principle is used to remove common artifacts such as created by metal parts in an X-ray. If the algorithm is taught how a typical artifact shows up in an image, it could remove it or, at a minimum, reduce it thus improving image quality and contributing to a better diagnosis.
An important feature for AI would be regulating the workflow, i.e. determining which cases should be considered “urgent” aka STAT based on automatic abnormality detection. These cases would be bumped to the top of the worklist to be seen by the radiologist.
The opposite is true as well, some of the images could be considered totally “clear,” i.e. having no indication and therefore not needing to be seen by a radiologist. This is useful in mass-screenings, e.g. for TB among immigrants, or black lung disease for people working in coal mines. These “normal” cases could be eliminated from a worklist.
The next level of AI is at the post-processing and reading level. CAD is probably the most common form of AI, where an image is marked using an annotation indicating a certain finding, which serves as a “second opinion.”
AI can also increase the productivity dramatically by assisting in creating a report. Macro’s can be used to automatically create sentences for common findings, again based on learning what phrases a user would typically use for a certain indication.
Standard measurements such as used for obstetrics can be automated. The algorithm can detect the head and indicate automatically its circumference and diameter which are standard measurements to indicate growth.
One of the labor-intensive activities is the annual contouring of certain anatomical parts such as the optical nerve in skull images. This contouring is used by radiation therapy software to determine where to minimize radiation to prevent potential damage. Automating the contouring process could potentially save a lot of time.
Automatic labeling of the spine vertebrae for the radiologist also saves time, which could also improve accuracy. This time savings might only be seconds, but it would add up when a radiologist is reviewing a large number of such cases.
Determining the age of a patient based on the x-ray such as of a hand is a good example of quantification, another example is the amount of calcium in a bone indicating potential osteoporosis.
Some of the indications are characterized by a certain number of occurrences within a particular region, for example the number of “bad cells” indicating cancer in a certain area when looking at a tissue specimen through a microscope, or, in the case of digital pathology, displayed on a monitor. Labeling particular cells and automatic counting them offers a big time savings for a pathologist.
One of the frequent complaints heard about the workstation functionality is that the hanging protocols, i.e. how the images are organized for a radiologist are often cumbersome to configure and do not always work. AI can assist in having “self-learning” hanging protocols based on radiologist preferences and also be more intelligent in determining the body part to determine what hanging protocol is applicable.
As AI becomes integrated in the workflow, the expectation is that it is “always-on,” meaning that it is seamlessly operates in the background, without a user having to push any buttons or launch a separate application to have an AI “opinion.”
One of the challenges is also to make sure that relevant prior studies are available, which might need to be retrieved from local and/or remote image sources, for example from a VNA or cloud. AI can assist by learning what prior studies are typically used as a comparison and do an intelligent discovery of where they might be archived.
Not only do radiologists want to see prior imaging studies, but also additional medical information that might be stored in an Electronic Health Record or EMR such as lab results, patient history, medications, etc. Typically, a radiologist would have access to that information, especially as most PACS systems are migrating to become EMR driven, however for teleradiology companies, the lack of access to EMR data is a major issue, where AI might be able to assist.
AI is just starting to make an impact, we have only seen the tip of the iceberg, but it is clear that there can be major improvements made using this exciting technology.