As we watch the medical imaging future unfold, there are reasonable questions to consider.
As a radiologist, I am very excited. I welcome the the upcoming changes and new tools being developed as we speak. While the computer science and data analytics experts create these tools, I challenge my colleagues to create a framework that best utilize these tools and elevate the standard of care.
A machine learning algorithm can find things that I cannot. This is happening right now and it is impressive. My first question is, what if the study has no findings? How could we avoid an unnecessary study to begin with? The financial, time and possible radiation cost could be avoided. Can we apply deep learning algorithms to clinical scenarios and avoid the CT radiation to a 11 year boy with belly pain for a possible appendicitis and ultimately has enteritis?
In a similar vein, why does a common emergent CT scan start at the diaphragm and end at the symphysis pubis? Over the years we have found if the patient complains of pain from ribs down, we’ll probably find something in there. Can we tailor the exam? If there truly is pain at McBurney’s point in the right lower quadrant, can we scan a smaller area? For younger patients the radiation dose savings can add up.
Finally, when these systems become so efficient at finding masses, nodules, cysts, focal thickening and small fluid collections, how do we follow them up? We have a decent amount of them now and the system is inefficient at best and non-existent at worst. Who will lean forward to this space? Radiologists, primary care or our computer scientists and data analyst colleagues?
Let’s create the framework to expect nodules and carefully track them. I for one want to avoid a situation where a family member is diagnosed with advanced lung cancer only to see a small nodule on the edge of an abdomen CT scan which included the lung bases a year ago.
These are reasonable short term goals. The horizon is broad and to tell you the truth, I am looking forward to the next, next thing.