Predicting Perceived Reporting Complexity of Abdominopelvic Computed Tomography With Deep Learning. Issue 4 (18th July 2022)

• Record Type: Journal Article
• Authors: Cheng, Phillip M.; Whang, Gilbert; Allgood, Evan; Tejura, Tapas K.
• Is Part Of: Journal of computer assisted tomography. Volume 46:Issue 4(2022)
• Page Start: 499
• Page End: 504
• Publication Date: 2022-07-18
• Subjects: computed tomography -- artificial neural networks -- digital image processing -- deep learning -- relative value units; Tomography -- Periodicals; Tomography -- Periodicals; Tomography; Periodicals; 616.0757
• DOI: 10.1097/RCT.0000000000001324
• Languages: English
• ISSNs: 0363-8715

Abstract

Abstract : Objective: The purpose of this pilot study was to examine human and automated estimates of reporting complexity for computed tomography (CT) studies of the abdomen and pelvis. Methods: A total of 1019 CT studies were reviewed and categorized into 3 complexity categories by 3 abdominal radiologists, and the majority classification was used as ground truth. Studies were randomized into a training set of 498 studies and a test set of 521 studies. A 2-stage neural network model was trained on the training set; the first-stage image-level classifier produces image embeddings that are used in the second-stage sequential model to provide a study-level prediction. Results: All 3 human reviewers agreed on ratings for 470 of the 1019 studies (46%); at least 2 of the 3 reviewers agreed on ratings for 1010 studies (99%). After training, the neural network model predicted complexity labels that agreed with the radiologist consensus rating on 55% of the studies; 90% of the incorrect predicted categories were errors where the predicted category differed from the consensus rating by one level of complexity. Conclusions: There is moderate interrater agreement in radiologist-perceived reporting complexity for CT studies of the abdomen and pelvis. Automated prediction of reporting complexity in radiology studies may be a useful adjunct to radiology practice analytics.