Journal of multidisciplinary healthcare · 2024
Clinical Application of an Artificial Intelligence System for Diagnosing Thyroid Disease Based on a Computer Neural Network Deep Learning Model.
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Li Zhihai, Yin Meilin, Li Wenfeng
Аннотация
PURPOSE: 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.
PATIENTS AND METHODS: 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 Expert Consensus on Thyroid Ultrasound; 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%.
RESULTS: 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%.
CONCLUSION: 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.
PATIENTS AND METHODS: 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 Expert Consensus on Thyroid Ultrasound; 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%.
RESULTS: 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%.
CONCLUSION: 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.