Background Machine learning has been increasingly used to develop algorithms that can
improve medical diagnostics and prognostication and has shown promise in improving the …

Purpose This study was conducted to evaluate the diagnostic performance of machine
learning in differentiating follicular adenoma from carcinoma using preoperative …

Objective The objective of this research was to create a deep learning network that utilizes
multiscale images for the classification of follicular thyroid carcinoma (FTC) and follicular …

Background Current diagnosis and classification of thyroid nodules are susceptible to
subjective factors. Despite widespread use of ultrasonography (USG) and fine needle …

Noninvasive differentiating thyroid follicular adenoma from carcinoma preoperatively is of
great clinical value to decrease the risks resulted from excessive surgery for patients with …

Deep learning systems (DLSs) have been developed for the histopathological assessment
of various types of tumors, but none are suitable for differential diagnosis between follicular …

Objectives This study aims to identify distinct ultrasound (US) characteristics for
distinguishing follicular thyroid carcinoma (FTC) from follicular thyroid adenoma (FTA), and …

Ultrasonography with fine-needle aspiration biopsy is commonly used to detect thyroid
cancer. However, thyroid ultrasonography is prone to subjective interpretations and …

Background: Currently, follicular thyroid carcinoma (FTC) has a relatively low incidence with
a lack of effective preoperative diagnostic means. To reduce the need for invasive diagnostic …

Objectives To evaluate the application value of a generally trained artificial intelligence (AI)
automatic diagnosis system in the malignancy diagnosis of follicular-patterned thyroid …