High body mass index is known to create problems for various medical imaging modalities, from CT to ultrasound. Could it also affect the accuracy of artificial intelligence algorithms? Researchers asked this question as it pertains to lung nodule detection in a new study in European Journal of Radiology.
X-ray photons attenuate as they pass through body tissue, which can decrease image quality and produce more noise.
- This is particularly a challenge for CT exams that don’t use a lot of radiation, like low-dose CT lung screening.
At the same time, AI algorithms are being developed to make LDCT screening more efficient, such as by identifying and classifying lung nodules.
- But if high BMI makes CT images noisier, will that affect AI’s performance? Researchers from the Netherlands tested the idea in 352 patients who got LDCT screening as part of the Lifelines study.
Researchers compared patients at both the high end of the BMI spectrum (mean 39.8) and low end (mean 18.7).
- Lung nodule detection by both Siemens Healthineers’ AI-Rad Companion Chest CT algorithm and a human radiologist was performed and compared.
Across the study population, researchers found…
- There was no statistically significant difference in AI’s sensitivity between high and low BMI groups (0.75 vs. 0.80, p = 0.37).
- Nor was there any difference in the human radiologist’s sensitivity (0.76 vs. 0.84, p = 0.17).
- AI had fewer false positives per scan in the high BMI group than low BMI (0.30 vs. 0.55), a difference that was statistically significant (p = 0.05).
- While the difference in false positives with the human radiologist was not statistically significant (0.05 vs. 0.16, p = 0.09).
The study authors attributed AI’s lower performance to more noise in the high BMI scans.
- They recommended that AI developers include people with both high and low BMI in datasets used for training algorithms.
The Takeaway
The results offer some comfort that patient BMI probably doesn’t have a huge effect on AI performance for nodule detection in lung screening, but it suggests a possible effect that might have achieved statistical significance with a larger sample size. More study in the area is definitely needed given the rising importance of AI for CT lung cancer screening.
