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Glushkov P.S.

Petrovsky National Research Center of Surgery

Marushchak E.A.

Petrovsky National Research Center of Surgery;
Pirogov Russian National Research Medical University

Azimov R.Kh.

Petrovsky National Research Center of Surgery;
Patrice Lumumba Peoples’ Friendship University of Russia

Zubareva E.A.

Pirogov Russian National Research Medical University

Levikin K.E.

Petrovsky National Research Center of Surgery

Shemyatovsky K.A.

Petrovsky National Research Center of Surgery

Karneev N.A.

Petrovsky National Research Center of Surgery

Khusanov Sh.S.

Petrovsky National Research Center of Surgery

Gorsky V.A.

Petrovsky National Research Center of Surgery;
Patrice Lumumba Peoples’ Friendship University of Russia;
Pirogov Russian National Research Medical University

Artificial intelligence in ultrasound diagnosis of thyroid nodules

Authors:

Glushkov P.S., Marushchak E.A., Azimov R.Kh., Zubareva E.A., Levikin K.E., Shemyatovsky K.A., Karneev N.A., Khusanov Sh.S., Gorsky V.A.

More about the authors

Journal: Pirogov Russian Journal of Surgery. 2024;(12‑2): 109‑116

DOI: 10.17116/hirurgia2024122109

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Table of contents

ARTIFICIAL INTELLIGENCE IN ULTRASOUND DIAGNOSIS OF THYROID NODULES
ARTIFICIAL INTELLIGENCE IN ULTRASOUND DIAGNOSIS OF THYROID NODULES

To cite this article:

Glushkov PS, Marushchak EA, Azimov RKh, et al. . Artificial intelligence in ultrasound diagnosis of thyroid nodules. Pirogov Russian Journal of Surgery. 2024;(12‑2):109‑116. (In Russ.)
https://doi.org/10.17116/hirurgia2024122109

Подписка 2025-2 (Pirogov Russian Journal of Surgery)
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OBJECTIVE

To analyze the efficacy of the S-Detect AI system of the Samsung RS85 ultrasound scanner (South Korea) in stratifying thyroid nodules compared to data obtained by specialist of ultrasound diagnostics.

MATERIAL AND METHODS

In 2024, we analyzed ultrasound data of 80 patients with thyroid nodules who required fine-needle aspiration biopsy (FNAB). Patients underwent thyroid ultrasound with nodule assessment according to the EU TI-RADS classification (GE Voluson E8 scanner, USA). After that, each patient underwent repeated thyroid ultrasound (Samsung RS85 scanner with built-in AI algorithms) to stratify nodules according to the EU TI-RADS classification. After ultrasound, all patients underwent FNAB of nodules with subsequent assessment of cytological results according to the Bethesda classification (2023). We assessed sensitivity, specificity and prognostic value of assessment by expert and AI.

RESULTS. S

Ensitivity, specificity and diagnostic value were high for both expert opinion and AI. However, AI was prone to overdiagnosis. At the same time, expert assessment was characterized by more common false negative results. Diagnostic value of studies conducted by expert and AI was similar.

CONCLUSION

At the modern stage, AI algorithms do not show significant advantages in stratifying thyroid tumors over ultrasound diagnostician with extensive experience.

Keywords:

thyroid nodules
stratification
artificial intelligence
FNAB
EU TI-RADS

Authors:

Glushkov P.S.

Petrovsky National Research Center of Surgery

Marushchak E.A.

Petrovsky National Research Center of Surgery;
Pirogov Russian National Research Medical University

Azimov R.Kh.

Petrovsky National Research Center of Surgery;
Patrice Lumumba Peoples’ Friendship University of Russia

Zubareva E.A.

Pirogov Russian National Research Medical University

Levikin K.E.

Petrovsky National Research Center of Surgery

Shemyatovsky K.A.

Petrovsky National Research Center of Surgery

Karneev N.A.

Petrovsky National Research Center of Surgery

Khusanov Sh.S.

Petrovsky National Research Center of Surgery

Gorsky V.A.

Petrovsky National Research Center of Surgery;
Patrice Lumumba Peoples’ Friendship University of Russia;
Pirogov Russian National Research Medical University

Received:

26.08.2024

Accepted:

11.09.2024

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