Enhancing Focused Ultrasound Literature Review Through Natural Language Processing-Driven Text Classification

Over the past decade, focused ultrasound (FUS) has emerged as a promising therapeutic modality for various medical conditions. However, the exponential growth in published literature on FUS therapies has made the literature review process increasingly time-consuming, inefficient, and error-prone. Machine learning approaches offer a promising solution to address these challenges. Therefore, the purpose of our study is to 1) explore and compare machine learning techniques for text classification of scientific abstracts, and 2) integrate these machine learning techniques into the conventional literature review process. A classified dataset of 3588 scientific abstracts related to and unrelated to FUS therapies sourced from the PubMed database was used to train various traditional machine learning and deep learning models. The fine-tuned Bio-ClinicalBERT model, which we named FusBERT, had comparatively optimal performance metrics with an accuracy of 0.91, precision of 0.85, recall of 0.99, and F1 of 0.91. FusBERT was then successfully integrated into the literature review process. Ultimately, the integration of this model into the literature review pipeline will reduce the number of irrelevant manuscripts that the clinical team must screen, facilitating efficient access to emerging findings in the field.