Evidence map›Paper›PMID 42601685›Full record

ArticleMedicine2026

Clinical value of artificial intelligence ultrasound-assisted diagnostic system in the differential diagnosis of benign and malignant thyroid nodules: A retrospective case-control study.

Meng Tian, Guangxi Liang, Xingwang Zhu, Yuetian Zhang, Haoyuan Zuo, Banggao Ni

Abstract read
In one paragraph

Article in Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Meng TianPeople's Hospital of Deyang City, Endocrine Thyroid Disease Diagnosis and Treatment Center, Deyang, Sichuan, China.ORCID 0009-0008-0983-5684
Guangxi LiangPeople's Hospital of Deyang City, Endocrine Thyroid Disease Diagnosis and Treatment Center, Deyang, Sichuan, China.
Xingwang ZhuLanzhou University, The First School of Clinical Medical, Lanzhou, Gansu, China.
Yuetian ZhangPeople's Hospital of Deyang City, Endocrine Thyroid Disease Diagnosis and Treatment Center, Deyang, Sichuan, China.
Haoyuan ZuoPeople's Hospital of Deyang City, Endocrine Thyroid Disease Diagnosis and Treatment Center, Deyang, Sichuan, China.
Banggao NiPeople's Hospital of Deyang City, Endocrine Thyroid Disease Diagnosis and Treatment Center, Deyang, Sichuan, China.

Funding

the Key Science and Technology Project of Gansu Province 1602FKDA001
6 · The paper itself

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.

Indexed as

Artificial IntelligenceThyroid NeoplasmsThyroid NoduleAdultAgedCase-Control StudiesDiagnosis, DifferentialFemaleHumansMaleMiddle AgedRetrospective StudiesROC CurveSensitivity and SpecificityUltrasonographyartificial intelligence ultrasoundassisted diagnostic system (AI-UASD)benign thyroid nodules (BTNs)malignant thyroid nodules (MTNs)

Identifiers

PMID42601685
PMCPMC13480895

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.