Artificial Intelligence Using Open Source BI-RADS Data Exemplifying Potential Future Use

• Published in: Journal of the American College of Radiology Vol. 16; no. 1; pp. 64 - 72
• Main Author: Ghosh, Adarsh
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.01.2019
• Subjects: Algorithms; Artificial Intelligence; BI-RADS; Breast Neoplasms - diagnostic imaging; Diagnosis, Computer-Assisted - methods; Female; Humans; machine learning; Predictive Value of Tests; radiologist-augmented workflow; BI-RADS; machine learning; Artificial intelligence; radiologist-augmented workflow
• ISSN: 1546-1440; 1558-349X; 1558-349X
• DOI: 10.1016/j.jacr.2018.09.040

With much hype about artificial intelligence (AI) rendering radiologists redundant, a simple radiologist-augmented AI workflow is evaluated; the premise is that inclusion of a radiologist’s opinion into an AI algorithm would make the algorithm achieve better accuracy than an algorithm trained on imaging parameters alone. Open-source BI-RADS data sets were evaluated to see whether inclusion of a radiologist’s opinion (in the form of BI-RADS classification) in addition to image parameters improved the accuracy of prediction of histology using three machine learning algorithms vis-à-vis algorithms using image parameters alone. BI-RADS data sets were obtained from the University of California, Irvine Machine Learning Repository (data set 1) and the Digital Database for Screening Mammography repository (data set 2); three machine learning algorithms were trained using 10-fold cross-validation. Two sets of models were trained: M1, using lesion shape, margin, density, and patient age for data set 1 and image texture parameters for data set 2, and M2, using the previous image parameters and the BI-RADS classification provided by radiologists. The area under the curve and the Gini coefficient for M1 and M2 were compared for the validation data set. The models using the radiologist-provided BI-RADS classification performed significantly better than the models not using them (P < .0001). AI and radiologist working together can achieve better results, helping in case-based decision making. Further evaluation of the metrics involved in predictor handling by AI algorithms will provide newer insights into imaging.