Evidence map›Paper›PMID 39822361›Full record

ArticleGland surgery2024

Application of an artificial intelligence-assisted diagnostic system for breast ultrasound: a prospective study.

Zhi-Ying Jin, Jun-Kang Li, Rui-Lan Niu, Nai-Qin Fu, Ying Jiang, Shi-Yu Li, Zhi-Li Wang

Abstract read
In one paragraph

Article in Gland surgery, 2024. 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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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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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

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No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Zhi-Ying JinDepartment of Ultrasound, The First Medical Center, Chinese PLA General Hospital, Beijing, China.
Jun-Kang LiDepartment of Ultrasound, The First Medical Center, Chinese PLA General Hospital, Beijing, China.
Rui-Lan NiuDepartment of Ultrasound, The First Medical Center, Chinese PLA General Hospital, Beijing, China.
Nai-Qin FuDepartment of Ultrasound, The First Medical Center, Chinese PLA General Hospital, Beijing, China.
Ying JiangDepartment of Ultrasound, The First Medical Center, Chinese PLA General Hospital, Beijing, China.
Shi-Yu LiDepartment of Ultrasound, The First Medical Center, Chinese PLA General Hospital, Beijing, China.
Zhi-Li WangDepartment of Ultrasound, The First Medical Center, Chinese PLA General Hospital, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Accurate diagnosis of breast cancer is of great importance to improve the prognosis of patients. Artificial intelligence (AI)-assisted diagnostic system for breast ultrasound is gradually being applied in the identification of benign and malignant breast lesions. This study aimed to evaluate the diagnostic performance and optimal application of AI Methods: A total of 501 consecutive patients with 679 breast lesions were prospectively included in the study. Junior and senior radiologists were asked to interpret images of lesions with and without AI assistance, respectively. Three application modes of AI were employed: AI alone, adjusted Breast Imaging Reporting and Data System (BI-RADS; incorporating BI-RADS obtained by AI into BI-RADS obtained by radiologists), and second reading mode (combining characteristic information extracted by AI to conduct a second reading so as to obtain a new BI-RADS). The diagnostic performances of these application modes were analyzed and compared. Results: The area under the curve (AUC) of junior radiologists increased from 0.879 to 0.921 in BI-RADS Conclusions: AI-assisted ultrasound is useful for the diagnosis of breast lesions, particularly for junior radiologists and lesions ≤2 cm. The use of the second reading mode can achieve excellent diagnostic performance.

Indexed as

artificial intelligence (AI)Breast cancerdiagnosisultrasonography

Identifiers

PMID39822361
PMCPMC11733640

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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.