Automatic thyroid nodule recognition and diagnosis in ultrasound imaging with the YOLOv2 neural network

Autor: Cheng Zhao, Shujian Yang, Yuxiu Gao, Yongjian Chen, Yun Lu, Guangye Tian, Shan Yang, Lei Wang
Rok vydání: 2018
Předmět:
Thyroid nodules
Adult
Male
medicine.medical_specialty
Artificial intelligence
Artificial Intelligence System
lcsh:Surgery
Thyroid Gland
lcsh:RC254-282
Computer-aided diagnosis systems
Diagnosis
Differential
03 medical and health sciences
Young Adult
0302 clinical medicine
Predictive Value of Tests
Image Interpretation
Computer-Assisted
Ultrasound
medicine
Humans
Thyroid Nodule
YOLOv2 neural network
Medical diagnosis
Aged
Retrospective Studies
Ultrasonography
Receiver operating characteristic
business.industry
Research
Thyroid
Nodule (medicine)
lcsh:RD1-811
Gold standard (test)
Middle Aged
lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens
medicine.disease
Prognosis
medicine.anatomical_structure
Oncology
ROC Curve
030220 oncology & carcinogenesis
030211 gastroenterology & hepatology
Surgery
Female
Radiology
Neural Networks
Computer
medicine.symptom
business
Zdroj: World Journal of Surgical Oncology
World Journal of Surgical Oncology, Vol 17, Iss 1, Pp 1-9 (2019)
ISSN: 1477-7819
Popis: Background 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. 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. Methods The ultrasound images of 276 patients were retrospectively selected. 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. Pathological diagnosis was the gold standard for the final diagnosis. The performances of the established system and the radiologists in diagnosing the benign and malignant thyroid nodules were compared. Results 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). This finding indicates a higher diagnostic accuracy (p = 0.0434). 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). The artificial intelligence diagnosis system had a higher specificity (89.91% vs 77.98%, p = 0.026). Conclusions Compared 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. As an auxiliary tool, this artificial intelligence diagnosis system can provide radiologists with sufficient assistance in the diagnosis of benign and malignant thyroid nodules.
Databáze: OpenAIRE
Externí odkaz: