Yes, you read that title correctly. A new study, the first of its type, is using “machine learning” to predict outcomes based on CT data, and neurologic grade. I will admit that I do not understand all of the system input data and how it was analyzed. I feel like I need another degree to grasp some of what is presented in this paper. If you’re analytically minded and want to learn more, please check out the paper for the full story because I may not represent it fully.
Historically, we have predicted neurologic recovery based on the most reliable factor which is the presence, or absence of deep pain (nociception). If an animal is paraplegic (no motor in the pelvic limbs) but has deep pain intact, we predict a 90% or greater chance of recovery for that pet. Timing and other variables play a minor role in recovery potential, too. If the same animal loses deep pain, their odds of recovery drop to around 50%. We have tried to predict myelomalacia, or motor recovery with MRI or CT characteristics over the years and have not been fully successful. Clients have higher expectations now, in the digital age. They want to know: will MY DOG walk again? Not what are the population odds? Although this paper doesn’t exactly say that we can do that, I think we’re edging closer to that possibility.
Based on this paper, the authors propose that this new learning tool can look at the neurologic grade on examination AND the CT properties and predict ambulation. (If I could insert the mind blowing emoji here I would!) Okay, maybe that’s a bit of an oversimplification but…it’s close. While they don’t say it’s predictive for an individual, it gets us closer than before.
Results
214 dogs were included, of which 74 were Dachshunds and 65 were Frenchies
128/214 dogs were deep pain positive (DPP)
86/214 dogs were deep pain negative (DPN)
The recovery rate for all dogs with 77%; 123/128 DPP (96%), 42/86 DPN (49%). These stats line up with what we already would have predicted
None of the radiomics features were associated with recovery on UNIVARIABLE analysis. I.e. one feature didn’t stand alone
The AI model outperformed simply knowing the DP status for predicting recovery to ambulation (p=0.02).
Neurologic grade was the MOST IMPORTANT feature in the AI model’s decision making process but, as I read it, the AI model did a better job of predicting WHICH dog would recover and which wouldn’t.
Is the future here? Are we going to see imaging centers offering AI prediction models? Are we edging people and examinations out of the equation? Not so fast. Do any of you use a calculator in your daily work? I do. Sixty years ago, the calculator was a wild idea. People who knew how to do math could do math faster using a calculator. The data put into the calculator was accurate, and the people inputting in knew it was accurate. People who couldn’t do math, relied on calculators and hoped the answers were correct because they couldn’t know if the output was the number expected because they didn’t understand the input numbers. I think the same is true of AI. If we know how to perform a good neurologic exam, and then pair it with a CT, the results put out by the AI algorithm could be more powerful than just doing recovery predictions based on the exam alone. However, if we do an incomplete or incompetent neurologic examination, we won’t know if the AI prediction model is giving us good data. Or, worse yet, we won’t know that we didn’t do a good exam, and we will believe the AI prediction data without knowing that the data input was bad. Also, don’t forget, the most useful part of the AI prediction model was the neurologic grade. Neurologic grade is obtained by doing a good neurologic examination. If you lose the exam, you will lose data. In 2 years, I will likely look back at this TidBit Tuesday with a different reaction than today. But today – I’m still feeling pretty confident that we need to touch our patients!!.
What do you think of AI? Many of us are using it for note taking and some for radiology. Do you have an AI receptionist? I hope you enjoyed this week’s TidBit Tuesday. I look forward to working with you soon!
Reference: Machine Learning and Quantitative CT radiomics prediction of postoperative functional recovery in paraplegic dogs (Low D, et al) ACVS 2025