Excessive cephalocaudal anatomic (Z-axis) coverage can lead to unnecessary radiation exposure to a patient. In this study, an automated computing model was developed for identifying instances of potentially excessive Z-axis coverage with abdomen-pelvis examinations. Eight patient and imaging attributes including patient gender, age, height, weight, volume CT dose index (CTDIvol), dose length product (DLP), maximum abdomen width, and maximum abdomen thickness were used to build a feedforward neural network model to predict a target Z-axis coverage whether it is an excessive or non-excessive Z-axis coverage scans. 264 CT abdomen-pelvis exams were used to develop the model which is validated using 10-fold cross validation. The result showed that 244 out of 264 exams (92.4%) correctly predicted Z-axis excessive coverage. The promising results indicate that this tool has the potential to be used for CT exams of the chest and colon, urography, and other site-specified CT studies having defined limited length.