Li Zhihai, Yin Meilin, Li Wenfeng
Department of Ultrasound, Dalang Hospital, Dongguan, Guandong, People's Republic of China.
Department of Science and Education, Dalang Hospital, Dongguan, Guandong, People's Republic of China.
J Multidiscip Healthc. 2024 Feb 8;17:609-617. doi: 10.2147/JMDH.S442479. eCollection 2024.
This study aimed to establish a stereoscopic neural learning network through deep learning and construct an artificial intelligence (AI) diagnosis system for the prediction of benign and malignant thyroid diseases, as well as repeatedly verified the diagnosis system and adjusted the data, in order to develop a type of AI-assisted thyroid diagnosis software with a low false negative rate and high sensitivity for clinical practice.
From July 2020 to April 2023, A total of 36 patients with thyroid nodules in our hospital were selected for diagnosis of thyroid nodules based on the ; samples were taken by aspiration biopsy or surgically and sent for pathological diagnosis. The ultrasonic diagnosis results were compared with the pathological results, a database was established based on the ultrasonic diagnostic characteristics and was entered in the AI-assisted diagnosis software for judgment of benign and malignant conditions. The data in the software were corrected based on the conformity rate and the reasons for misjudgment, and the corrected software was used to evaluate the benign and malignant conditions of the 36 patients, until the conformity rate exceeded 90%.
The initial conformity rate of the AI software for identifying benign and malignant conditions was 88%, while that of the software utilizing the database was 94%.
We established a stereoscopic neural learning network and construct an AI diagnosis system for the prediction of benign and malignant thyroid diseases, with a low false negative rate and high sensitivity for clinical practice.
本研究旨在通过深度学习建立立体神经学习网络，构建用于预测甲状腺疾病良恶性的人工智能（AI）诊断系统，并对诊断系统进行反复验证和数据调整，以开发一种临床实践中假阴性率低、灵敏度高的AI辅助甲状腺诊断软件。
选取2020年7月至2023年4月我院共36例甲状腺结节患者，根据……对甲状腺结节进行诊断；通过细针穿刺活检或手术获取样本并送检进行病理诊断。将超声诊断结果与病理结果进行比较，基于超声诊断特征建立数据库，并输入AI辅助诊断软件以判断良恶性情况。根据符合率和误判原因对软件中的数据进行校正，并用校正后的软件对36例患者的良恶性情况进行评估，直至符合率超过90%。
AI软件识别良恶性情况的初始符合率为88%，而利用数据库后的软件符合率为94%。
我们建立了立体神经学习网络并构建了用于预测甲状腺疾病良恶性的AI诊断系统，在临床实践中具有低假阴性率和高灵敏度。
