Evidence map›Paper›PMID 39579245›Full record

ArticleArchives of gynecology and obstetrics2024

Exploratory study on the enhancement of O-RADS application effectiveness for novice ultrasonographers via deep learning.

Tao Liu, Kuo Miao, Gaoqiang Tan, Hanqi Bu, Mingda Xu, Qiming Zhang, Qin Liu, Xiaoqiu Dong

Abstract read
PubMed Publisher
In one paragraph

Article in Archives of gynecology and obstetrics, 2024. 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. Article
  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

8 authors.

Tao LiuDepartment of Medical Ultrasound, The Fourth Affiliated Hospital of Harbin Medical University, No. 37 Yiyuan Street, Nangang District, Harbin, 150001, Heilongjiang, China.
Kuo MiaoDepartment of Medical Ultrasound, The Fourth Affiliated Hospital of Harbin Medical University, No. 37 Yiyuan Street, Nangang District, Harbin, 150001, Heilongjiang, China.
Gaoqiang TanDepartment of Medical Ultrasound, The Fourth Affiliated Hospital of Harbin Medical University, No. 37 Yiyuan Street, Nangang District, Harbin, 150001, Heilongjiang, China.
Hanqi BuDepartment of Medical Ultrasound, The Fourth Affiliated Hospital of Harbin Medical University, No. 37 Yiyuan Street, Nangang District, Harbin, 150001, Heilongjiang, China.
Mingda XuDepartment of Medical Ultrasound, The Fourth Affiliated Hospital of Harbin Medical University, No. 37 Yiyuan Street, Nangang District, Harbin, 150001, Heilongjiang, China.
Qiming ZhangDepartment of Medical Ultrasound, The Fourth Affiliated Hospital of Harbin Medical University, No. 37 Yiyuan Street, Nangang District, Harbin, 150001, Heilongjiang, China.
Qin LiuSchool of Basic Medical Sciences, Xiangnan College, Chenzhou, China.
Xiaoqiu DongDepartment of Medical Ultrasound, The Fourth Affiliated Hospital of Harbin Medical University, No. 37 Yiyuan Street, Nangang District, Harbin, 150001, Heilongjiang, China. dongxq0451@163.com.ORCID 0000-0001-7930-2340

Funding

Heilongjiang Da'ai Longjiang Charity Foundation HX2020-20Natural Science Foundation of Heilongjiang Province, China LH2022H033
6 · The paper itself

Abstract

purposeThe study aimed to create a deep convolutional neural network (DCNN) model based on ConvNeXt-Tiny to identify classic benign lesions (CBL) from other lesions (OL) within the Ovarian-Adnexal Reporting and Data System (O-RADS), enhancing the system's utility for novice ultrasonographers.

methodsTwo sets of sonographic images of pathologically confirmed adnexal lesions were retrospectively collected [development dataset (DD) and independent test dataset (ITD)]. The ConvNeXt-Tiny model, optimized through transfer learning, was trained on the DD using the original images directly and after automatic lesion segmentation by a U-Net model. Models derived from both training paradigms were validated on the ITD for sensitivity, specificity, accuracy, and area under the curve (AUC). Two novice ultrasonographers were assessed in O-RADS with and without assistance from the model for Application Effectiveness.

resultsThe ConvNeXt-Tiny model trained on original images scored AUCs of 0.978 for DD and 0.955 for ITD, while the U-Net segmented image model achieved 0.967 for DD and 0.923 for ITD; neither showed significant differences. When assessing the malignancy of lesions using O-RADS 4 and 5, the diagnostic performances of two novice ultrasonographers and senior ultrasonographer, as well as model-assisted classifications, showed no significant differences, except for one novice's low accuracy. This approach reduced classification time by 62 and 64 min. The kappa values with senior doctors' classifications rose from 0.776 and 0.761 to 0.914 and 0.903, respectively.

conclusionThe ConvNeXt-Tiny model demonstrated excellent and stable performance in distinguishing CBL from OL within O-RADS. The diagnostic performance of novice ultrasonographers using O-RADS is essentially equivalent to that of senior ultrasonographer, and the assistance of the model can enhance their classification efficiency and consistency with the results of senior ultrasonographer.

Indexed as

Deep LearningUltrasonographyAdnexal DiseasesAdnexa UteriAdultFemaleHumansMiddle AgedNeural Networks, ComputerOvarian NeoplasmsRetrospective StudiesSensitivity and SpecificityClassic benign lesions (CBL)Deep learningO-RADSSonographic images

Identifiers

PMID39579245

What OpenQuestion holds

Textmetadata
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.