Evidence map›Paper›PMID 41553093›Full record

ArticleEuropean thyroid journal2026

Artificial intelligence-assisted risk stratification of thyroid nodules with atypia of undetermined significance.

Jinyoung Kim, Jeongmin Lee, Jeonghoon Ha, Ohjoon Kwon, Ki-Hyun Baek, Chang Myeon Song, Hyeon A Lee, Hyemi Kwon, Inyoung Youn, Mi-Ri Kwon and 1 more

Abstract read
In one paragraph

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

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Jinyoung KimDivision of Endocrinology and Metabolism, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.ORCID 0000-0003-4581-9493
Jeongmin LeeDivision of Endocrinology and Metabolism, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.ORCID 0000-0001-9074-8087
Jeonghoon HaDivision of Endocrinology and Metabolism, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.ORCID 0000-0001-9219-7135
Ohjoon KwonDepartment of Surgery, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.ORCID 0000-0002-8293-9577
Ki-Hyun BaekDivision of Endocrinology and Metabolism, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.ORCID 0000-0002-4097-1077
Chang Myeon SongDepartment of Otolaryngology-Head and Neck Surgery, College of Medicine, Hanyang University, Seoul, Republic of Korea.ORCID 0000-0001-5267-0135
Hyeon A LeeDepartment of Otolaryngology-Head and Neck Surgery, College of Medicine, Hanyang University, Seoul, Republic of Korea.ORCID 0000-0003-4452-8053
Hyemi KwonDivision of Endocrinology and Metabolism, Department of Internal Medicine, School of Medicine, Sungkyunkwan University, Seoul, Republic of Korea.ORCID 0000-0003-4257-3133
Inyoung YounDepartment of Radiology, School of Medicine, Sungkyunkwan University, Seoul, Republic of Korea.ORCID 0000-0002-3518-3332
Mi-Ri KwonDepartment of Radiology, School of Medicine, Sungkyunkwan University, Seoul, Republic of Korea.ORCID 0000-0001-8225-3690
Dong-Jun LimDivision of Endocrinology and Metabolism, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.ORCID 0000-0003-0995-6482

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Given the limitations of conventional approaches in managing indeterminate thyroid nodules, there remains an unmet need for non-invasive assistant tools to improve risk stratification. This study aimed to evaluate the clinical applicability of an artificial intelligence (AI) model for thyroid nodules with atypia of undetermined significance (AUS) cytology. Methods: We retrospectively reviewed patients who underwent fine-needle aspiration (FNA) for thyroid nodules between January 2019 and December 2020 across five medical institutions in Korea. Nodules initially diagnosed as AUS and later confirmed as benign or malignant were included. A previously developed deep learning-based AI model, AI-Thyroid, was employed to provide binary classifications (benign or malignant) and malignancy risk estimates. Results: A total of 165 thyroid nodules were analyzed. The median (interquartile range) longest diameter was 1.30 cm (0.80-2.10), and the malignancy rate of the cohort was 39%. In binary classification tasks, the model achieved a sensitivity of 0.91 and a negative predictive value of 0.87. The area under the curve (AUC) based on estimated malignancy risk was 0.75 (95% confidence interval (CI): 0.68-0.83), and the AUC derived from K-TIRADS categories 2-5 was 0.76 (95% CI: 0.69-0.83), indicating comparable diagnostic accuracy with the traditional scoring system. Subgroup analyses demonstrated that the model achieved a sensitivity of 98% in nodules smaller than 1.5 cm. Conclusion: AI-assisted ultrasound analysis offers supplementary diagnostic information for thyroid nodules with AUS cytology. Its high sensitivity and negative predictive value may assist clinicians in decision-making processes, particularly for small, low-risk thyroid nodules.

Indexed as

Artificial IntelligenceThyroid NeoplasmsThyroid NoduleAdultBiopsy, Fine-NeedleFemaleHumansIntelligent SystemsMaleMiddle AgedRetrospective StudiesRisk Assessmentartificial intelligencedeep learningthyroid neoplasmsthyroid nodulesultrasonography

Identifiers

PMID41553093
PMCPMC13138589

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.