Ross W Filice1. 1. MedStar Georgetown University Hospital, Washington, District of Columbia. Electronic address: ross.w.filice@gunet.georgetown.edu.
Abstract
PURPOSE: Radiology-pathology correlation has long been foundational to continuing education, peer learning, quality assurance, and multidisciplinary patient care. The objective of this study was to determine whether modern deep-learning language-modeling techniques could reliably match pathology reports to pertinent radiology reports. METHODS: The recently proposed Universal Language Model Fine-Tuning for Text Classification methodology was used. Two hundred thousand radiology and pathology reports were used for adaptation to the radiology-pathology space. One hundred thousand candidate radiology-pathology pairs, evenly split into match and no-match categories, were used for training the final binary classification model. Matches were defined by a previous-generation artificial intelligence anatomic concept radiology-pathology correlation system. RESULTS: The language model rapidly adapted very closely to the prior anatomic concept-matching approach, with 100% specificity, 65.1% sensitivity, and 73.7% accuracy. For comparison, the previous methodology, which was intentionally designed to be specific at the expense of sensitivity, had 98.0% specificity, 65.1% sensitivity, and 73.2% accuracy. CONCLUSIONS: Modern deep-learning language-modeling approaches are promising for radiology-pathology correlation. Because of their rapid adaptation to underlying training labels, these models advance previous artificial intelligence work in that they can be continuously improved and tuned to improve performance and adjust to user and site-level preference.
PURPOSE: Radiology-pathology correlation has long been foundational to continuing education, peer learning, quality assurance, and multidisciplinary patient care. The objective of this study was to determine whether modern deep-learning language-modeling techniques could reliably match pathology reports to pertinent radiology reports. METHODS: The recently proposed Universal Language Model Fine-Tuning for Text Classification methodology was used. Two hundred thousand radiology and pathology reports were used for adaptation to the radiology-pathology space. One hundred thousand candidate radiology-pathology pairs, evenly split into match and no-match categories, were used for training the final binary classification model. Matches were defined by a previous-generation artificial intelligence anatomic concept radiology-pathology correlation system. RESULTS: The language model rapidly adapted very closely to the prior anatomic concept-matching approach, with 100% specificity, 65.1% sensitivity, and 73.7% accuracy. For comparison, the previous methodology, which was intentionally designed to be specific at the expense of sensitivity, had 98.0% specificity, 65.1% sensitivity, and 73.2% accuracy. CONCLUSIONS: Modern deep-learning language-modeling approaches are promising for radiology-pathology correlation. Because of their rapid adaptation to underlying training labels, these models advance previous artificial intelligence work in that they can be continuously improved and tuned to improve performance and adjust to user and site-level preference.