Thyroid cancer is the most common malignancy in the endocrine system, with an ever-increasing incidence. As an auxiliary tool, this artificial intelligence diagnosis system can provide radiologists with sufficient assistance in the diagnosis of benign and malignant thyroid nodules. ConclusionsĬompared with the performance of experienced radiologists, the artificial intelligence system has comparable sensitivity and accuracy for the diagnosis of malignant thyroid nodules and better diagnostic ability for benign thyroid nodules.

The artificial intelligence diagnosis system had a higher specificity (89.91% vs 77.98%, p = 0.026). The sensitivity, positive predictive value, negative predictive value, and accuracy of the artificial intelligence diagnosis system for the diagnosis of malignant thyroid nodules were 90.5%, 95.22%, 80.99%, and 90.31%, respectively, and the performance did not significantly differ from that of the radiologists ( p > 0.05). This finding indicates a higher diagnostic accuracy ( p = 0.0434). The artificial intelligence diagnosis system correctly identified the lesion area, with an area under the receiver operating characteristic (ROC) curve of 0.902, which is higher than that of the radiologists (0.859). The performances of the established system and the radiologists in diagnosing the benign and malignant thyroid nodules were compared. Pathological diagnosis was the gold standard for the final diagnosis. The diagnoses of the radiologists were determined according to the Thyroid Imaging Reporting and Data System the images were automatically recognized and diagnosed by the established artificial intelligence system. The ultrasound images of 276 patients were retrospectively selected. The performance of the system in the diagnosis of thyroid nodules was evaluated, and the application value of artificial intelligence in clinical practice was investigated. For additional information visit Linking to and Using Content from MedlinePlus.In this study, images of 2450 benign thyroid nodules and 2557 malignant thyroid nodules were collected and labeled, and an automatic image recognition and diagnosis system was established by deep learning using the YOLOv2 neural network. Any duplication or distribution of the information contained herein is strictly prohibited without authorization. Links to other sites are provided for information only - they do not constitute endorsements of those other sites. A licensed physician should be consulted for diagnosis and treatment of any and all medical conditions. The information provided herein should not be used during any medical emergency or for the diagnosis or treatment of any medical condition. This site complies with the HONcode standard for trustworthy health information: verify here. Learn more about A.D.A.M.'s editorial policy editorial process and privacy policy. is among the first to achieve this important distinction for online health information and services. follows rigorous standards of quality and accountability. is accredited by URAC, for Health Content Provider (URAC's accreditation program is an independent audit to verify that A.D.A.M.

Thyroid sonogram