Real-world applications of deep convolutional neural networks in diagnostic cancer imaging.

Abstract

Ultrasound is integral to the management of thyroid nodules. However, the interpretation of thyroid ultrasound studies is time- and training-intensive and has inherent inter-observer inconsistencies, resulting in a decrement in its positive predictive value (1). Compounding these limitations, fine-needle aspiration (FNA) yields 15% to 30% cytologically indeterminate nodules, which often leads to unnecessary diagnostic surgeries (2).