Noting that overlapping imaging features on contrast-enhanced computed tomography (CT) can make it challenging to differentiate between acute diverticulitis and colon cancer, researchers say an emerging deep learning model may provide enhanced sensitivity and specificity for these conditions. In a retrospective study recently published in JAMA Network Open, researchers developed and tested a three-dimensional (3D) convolutional neural network (CNN) for 585 patients (mean age of 63.2) who underwent surgery for colon cancer or acute diverticulitis between July 1, 2005 and October 1, 2020, had venous phase CT imaging within 60 days prior to surgery and had segmental wall thickening in the colon that was independent of disease stage. In comparison to mean sensitivity and specificity rates of 77.6 percent and 81.6 percent, respectively, for radiologist readers, the study authors noted an 83.3 percent sensitivity rate and an 86.6 percent specificity rate for the 3D CNN model. The combination of the deep learning model and radiologist assessment resulted in an eight percent increase in sensitivity (85.6 percent) and a 9.7 percent increase in specificity (91.3 percent) over radiologist assessments, according to the study findings. The study authors also noted the reduction of false-negative rates with the 3D CNN model.