Clinical value of artificial intelligence ultrasound-assisted diagnostic system in the differential diagnosis of benign and malignant thyroid nodules

Accurately distinguishing the benign thyroid nodules (BTNs) and malignant thyroid nodules (MTNs) is crucial for treatment planning and prognosis. This study aims to explore the clinical value of artificial intelligence ultrasound-assisted diagnostic system (AI-UADS) in diagnosing the BTNs and MTNs. In this retrospective case-control study, a total of 161 TNs from 91 patients confirmed by surgical pathology were included. All nodules underwent ultrasound examinations and were evaluated by both senior ultrasonographers and AI-UADS, independently. Using surgical pathology as the gold standard, the diagnostic performance of the 2 methods was analyzed using receiver operating characteristic curves (including classify the nodules based on size, location, capsule relationships, and presence or absence of Hashimoto thyroiditis [HT]). The area under the curve, accuracy, sensitivity, specificity, positive predictive value and negative predictive value of AI-UADS in the differential diagnosis of BTNs and MTNs were 0.933, 93.2%, 92.4%, 94.2%, 95.5%, and 90.3%, respectively, all of which were higher than ultrasonographers (0.821, 82.0%, 81.5%, 82.6%, 86.2%, and 77.0%, respectively) (all P < .05). Specifically, the accuracy in diagnosing nodules with a maximum diameter ≤10 mm (94.0% vs 80.7%) and in the left lobe (92.5% vs 81.3%); the accuracy, sensitivity, specificity, positive predictive value and negative predictive value in diagnosing nodules with far from the capsule ([93.8% vs 79.5%], [92.5% vs 75.5%], [94.9% vs 83.1%], [94.2% vs 80.0%], [93.3% vs 79.0%]) and without HT ([94.0% vs 82.7%], [93.2% vs 82.4%], [94.9% vs 83.1%], [95.8% vs 85.9%], [91.8% vs 79.0%]) of AI-UADS were higher than ultrasonographers (all P < .05). The AI-UADS demonstrates good diagnostic performance in differentiating BTNs and MTNs, and can serve as an effective auxiliary tool for ultrasonographers. It shows particular advantages in diagnosing nodules with a maximum diameter ≤10 mm, located in the left lobe, far from the capsule, and in patients without HT.

Authors

Institutions

Publication Details

Journal
Medicine
Published
2026-08-14
DOI
https://doi.org/10.1097/md.0000000000050126
Primary Topic
Thyroid Cancer Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Clinical value of artificial intelligence ultrasound-assisted diagnostic system in the differential diagnosis of benign and malignant thyroid nodules

Xingwang Zhu, Yuetian Zhang, Meng Tian, banggao ni et al.
Medicine
Thyroid Cancer Diagnosis and Treatment
article

Clinical value of artificial intelligence ultrasound-assisted diagnostic system in the differential diagnosis of benign and malignant thyroid nodules

Xingwang Zhu, Yuetian Zhang, Meng Tian, banggao ni, Haoyuan Zuo, Guangxi Liang
article en

Abstract

Accurately distinguishing the benign thyroid nodules (BTNs) and malignant thyroid nodules (MTNs) is crucial for treatment planning and prognosis. This study aims to explore the clinical value of artificial intelligence ultrasound-assisted diagnostic system (AI-UADS) in diagnosing the BTNs and MTNs. In this retrospective case-control study, a total of 161 TNs from 91 patients confirmed by surgical pathology were included. All nodules underwent ultrasound examinations and were evaluated by both senior ultrasonographers and AI-UADS, independently. Using surgical pathology as the gold standard, the diagnostic performance of the 2 methods was analyzed using receiver operating characteristic curves (including classify the nodules based on size, location, capsule relationships, and presence or absence of Hashimoto thyroiditis [HT]). The area under the curve, accuracy, sensitivity, specificity, positive predictive value and negative predictive value of AI-UADS in the differential diagnosis of BTNs and MTNs were 0.933, 93.2%, 92.4%, 94.2%, 95.5%, and 90.3%, respectively, all of which were higher than ultrasonographers (0.821, 82.0%, 81.5%, 82.6%, 86.2%, and 77.0%, respectively) (all P < .05). Specifically, the accuracy in diagnosing nodules with a maximum diameter ≤10 mm (94.0% vs 80.7%) and in the left lobe (92.5% vs 81.3%); the accuracy, sensitivity, specificity, positive predictive value and negative predictive value in diagnosing nodules with far from the capsule ([93.8% vs 79.5%], [92.5% vs 75.5%], [94.9% vs 83.1%], [94.2% vs 80.0%], [93.3% vs 79.0%]) and without HT ([94.0% vs 82.7%], [93.2% vs 82.4%], [94.9% vs 83.1%], [95.8% vs 85.9%], [91.8% vs 79.0%]) of AI-UADS were higher than ultrasonographers (all P < .05). The AI-UADS demonstrates good diagnostic performance in differentiating BTNs and MTNs, and can serve as an effective auxiliary tool for ultrasonographers. It shows particular advantages in diagnosing nodules with a maximum diameter ≤10 mm, located in the left lobe, far from the capsule, and in patients without HT.

MedicineVol. 105(33)
Deyang Stomatological Hospital (CN), First Hospital of Lanzhou University (CN), Lanzhou University (CN)
Good health and well-being
Openalex Percentile: Top 10%
Thyroid Cancer Diagnosis and Treatment
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.