1 research outputs found
Automatic detection and diagnosis of sacroiliitis in CT scans as incidental findings
Early diagnosis of sacroiliitis may lead to preventive treatment which can
significantly improve the patient's quality of life in the long run.
Oftentimes, a CT scan of the lower back or abdomen is acquired for suspected
back pain. However, since the differences between a healthy and an inflamed
sacroiliac joint in the early stages are subtle, the condition may be missed.
We have developed a new automatic algorithm for the diagnosis and grading of
sacroiliitis CT scans as incidental findings, for patients who underwent CT
scanning as part of their lower back pain workout. The method is based on
supervised machine and deep learning techniques. The input is a CT scan that
includes the patient's pelvis. The output is a diagnosis for each sacroiliac
joint. The algorithm consists of four steps: 1) computation of an initial
region of interest (ROI) that includes the pelvic joints region using
heuristics and a U-Net classifier; 2) refinement of the ROI to detect both
sacroiliac joints using a four-tree random forest; 3) individual sacroiliitis
grading of each sacroiliac joint in each CT slice with a custom slice CNN
classifier, and; 4) sacroiliitis diagnosis and grading by combining the
individual slice grades using a random forest. Experimental results on 484
sacroiliac joints yield a binary and a 3-class case classification accuracy of
91.9% and 86%, a sensitivity of 95% and 82%, and an Area-Under-the-Curve of
0.97 and 0.57, respectively. Automatic computer-based analysis of CT scans has
the potential of being a useful method for the diagnosis and grading of
sacroiliitis as an incidental finding