Evidence map›Paper›PMID 35875151›Full record

ArticleFrontiers in oncology2022

A Comparative Study of Multiple Deep Learning Models Based on Multi-Input Resolution for Breast Ultrasound Images.

Huaiyu Wu, Xiuqin Ye, Yitao Jiang, Hongtian Tian, Keen Yang, Chen Cui, Siyuan Shi, Yan Liu, Sijing Huang, Jing Chen and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. 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

12 authors.

Huaiyu WuDepartment of Ultrasound, First Clinical College of Jinan University, Second Clinical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, China.
Xiuqin YeDepartment of Ultrasound, First Clinical College of Jinan University, Second Clinical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, China.
Yitao JiangResearch and Development Department, Microport Prophecy, Shanghai, China.
Hongtian TianDepartment of Ultrasound, First Clinical College of Jinan University, Second Clinical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, China.
Keen YangDepartment of Ultrasound, First Clinical College of Jinan University, Second Clinical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, China.
Chen CuiResearch and Development Department, Microport Prophecy, Shanghai, China.
Siyuan ShiResearch and Development Department, Microport Prophecy, Shanghai, China.
Yan LiuThe Key Laboratory of Cardiovascular Remodeling and Function Research, Chinese Ministry of Education and Chinese Ministry of Health, and The State and Shandong Province Joint Key Laboratory of Translational Cardiovascular Medicine, Cheeloo College of Medicine, Shandong University, Qilu Hospital of Shandong University, Jinan, China.
Sijing HuangDepartment of Ultrasound, First Clinical College of Jinan University, Second Clinical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, China.
Jing ChenDepartment of Ultrasound, First Clinical College of Jinan University, Second Clinical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, China.
Jinfeng XuDepartment of Ultrasound, First Clinical College of Jinan University, Second Clinical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, China.
Fajin DongDepartment of Ultrasound, First Clinical College of Jinan University, Second Clinical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: The purpose of this study was to explore the performance of different parameter combinations of deep learning (DL) models (Xception, DenseNet121, MobileNet, ResNet50 and EfficientNetB0) and input image resolutions (REZs) (224 × 224, 320 × 320 and 488 × 488 pixels) for breast cancer diagnosis. Methods: This multicenter study retrospectively studied gray-scale ultrasound breast images enrolled from two Chinese hospitals. The data are divided into training, validation, internal testing and external testing set. Three-hundreds images were randomly selected for the physician-AI comparison. The Wilcoxon test was used to compare the diagnose error of physicians and models under Results: A total of 13,684 images of 3447 female patients are finally included. In external test the 224 and 320 REZ achieve the best performance in MobileNet and EfficientNetB0 respectively (AUC: 0.893 and 0.907). Meanwhile, 448 REZ achieve the best performance in Xception, DenseNet121 and ResNet50 (AUC: 0.900, 0.883 and 0.871 respectively). In physician-AI test set, the 320 REZ for EfficientNetB0 (AUC: 0.896, Conclusion: Based on the gray-scale ultrasound breast images, we obtained the best DL combination which was better than the physicians.

Indexed as

artifical intelligencebreast cancerdeep learningresolutionultrasound

Identifiers

PMID35875151
PMCPMC9302001

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.