Evidence map›Paper›PMID 40488816›Full record

ArticleJapanese journal of radiology2025

Evaluation of AI diagnostic systems for breast ultrasound: comparative analysis with radiologists and the effect of AI assistance.

Sayumi Tsuyuzaki, Tomoyuki Fujioka, Emi Yamaga, Leona Katsuta, Mio Mori, Yuka Yashima, Mayumi Hara, Arisa Sato, Iichiroh Onishi, Jitsuro Tsukada and 3 more

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Japanese journal of radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Article
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

13 authors.

Sayumi TsuyuzakiDepartment of Diagnostic Radiology, Institute of Science Tokyo, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8519, Japan.
Tomoyuki FujiokaDepartment of Diagnostic Radiology, Institute of Science Tokyo, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8519, Japan. t-fujioka@dokkyomed.ac.jp.ORCID http://orcid.org/0000-0002-7141-8901
Emi YamagaDepartment of Diagnostic Radiology, Institute of Science Tokyo, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8519, Japan.
Leona KatsutaDepartment of Diagnostic Radiology, Institute of Science Tokyo, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8519, Japan.
Mio MoriDepartment of Diagnostic Radiology, Institute of Science Tokyo, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8519, Japan.
Yuka YashimaDepartment of Radiology, Dokkyo Medical University Saitama Medical Center, 2-1-50 Minamikoshigaya, Koshigaya, Saitama, 343-8555, Japan.
Mayumi HaraDepartment of Radiology, Kanto Central Hospital of the Mutual Aid Association of Public School Teachers, 6-25-1 Kamiyoga, Setagaya-ku, Tokyo, 158-8531, Japan.
Arisa SatoNitobe-Memorial Nakano General Hospital, 4-59-16 Tyuo, Nakano-ku, Tokyo, 164-8607, Japan.
Iichiroh OnishiDepartment of Diagnostic Pathology, Institute of Science Tokyo, Bunkyo-ku, Tokyo, 113-8510, Japan.
Jitsuro TsukadaDepartment of Diagnostic Radiology, Keio University School of Medicine, 35, Shinanomachi, Shinjyuku-ku, Tokyo, 160-8582, Japan.
Tomoyuki ArugaDepartment of Breast Surgery, Institute of Science Tokyo, Bunkyo-ku, Tokyo, 113-8510, Japan.
Kazunori KubotaDepartment of Radiology, Dokkyo Medical University Saitama Medical Center, 2-1-50 Minamikoshigaya, Koshigaya, Saitama, 343-8555, Japan.
Ukihide TateishiDepartment of Diagnostic Radiology, Institute of Science Tokyo, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8519, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe purpose of this study is to evaluate the diagnostic accuracy of an artificial intelligence (AI)-based Computer-Aided Diagnosis (CADx) system for breast ultrasound, compare its performance with radiologists, and assess the effect of AI-assisted diagnosis. This study aims to investigate the system's ability to differentiate between benign and malignant breast masses among Japanese patients. MATERIALS AND

methodsThis retrospective study included 171 breast mass ultrasound images (92 benign, 79 malignant). The AI system, BU-CAD™, provided Breast Imaging Reporting and Data System (BI-RADS) categorization, which was compared with the performance of three radiologists. Diagnostic accuracy, sensitivity, specificity, and area under the curve (AUC) were analyzed. Radiologists' diagnostic performance with and without AI assistance was also compared, and their reading time was measured using a stopwatch.

resultsThe AI system demonstrated a sensitivity of 91.1%, specificity of 92.4%, and an AUC of 0.948. It showed comparable diagnostic performance to Radiologist 1, with 10 years of experience in breast imaging (0.948 vs. 0.950; p = 0.893), and superior performance to Radiologist 2 (7 years of experience, 0.948 vs. 0.881; p = 0.015) and Radiologist 3 (3 years of experience, 0.948 vs. 0.832; p = 0.001). When comparing diagnostic performance with and without AI, the use of AI significantly improved the AUC for Radiologists 2 and 3 (p = 0.001 and 0.005, respectively). However, there was no significant difference for Radiologist 1 (p = 0.139). In terms of diagnosis time, the use of AI reduced the reading time for all radiologists. Although there was no significant difference in diagnostic performance between AI and Radiologist 1, the use of AI substantially decreased the diagnosis time for Radiologist 1 as well.

conclusionThe AI system significantly improved diagnostic efficiency and accuracy, particularly for junior radiologists, highlighting its potential clinical utility in breast ultrasound diagnostics.

Indexed as

Artificial IntelligenceBreast NeoplasmsDiagnosis, Computer-AssistedUltrasonography, MammaryAdultAgedAged, 80 and overBreastDiagnosis, DifferentialFemaleHumansMiddle AgedRadiologistsReproducibility of ResultsRetrospective StudiesSensitivity and SpecificityArtificial intelligence (AI)BI-RADSBreast ultrasoundComputer-aided diagnosis (CADx)Diagnostic accuracyRadiologist efficiency

Identifiers

PMID40488816
PMCPMC12479558

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.