Evidence map›Paper›PMID 32903956›Full record

ReviewEuropean thyroid journal2020

Computer-Aided Diagnosis Systems in Diagnosing Malignant Thyroid Nodules on Ultrasonography: A Systematic Review and Meta-Analysis.

Lei Xu, Junling Gao, Quan Wang, Jichao Yin, Pengfei Yu, Bin Bai, Ruixia Pei, Dingzhang Chen, Guochun Yang, Shiqi Wang and 1 more

Open access · bronzeAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in European thyroid journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 4 pooled it
4.8field-weighted citation impact, top 4% of its field
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

31 citing papers in PubMed, 4 syntheses or guidelines pooled it, 55 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. A comparison of artificial intelligence versus radiologists in the diagnosis of thyroid nodules using ultrasonography: a systematic review and meta-analysis.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2022
    Pooled it
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  6. Article
  7. Article
  8. Article
  9. Review
  10. Thyroid ultrasound and its ancillary techniques.Reviews in endocrine & metabolic disorders · 2024
    Review
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  17. The Future of Thyroid Nodule Risk Stratification.Endocrinology and metabolism clinics of North America · 2022
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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

11 authors at 5 institutions in 1 country.

Lei XuKey Laboratory of Biomedical Information Engineering of the Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.
Junling GaoKey Laboratory of Biomedical Information Engineering of the Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.
Quan WangLaboratory of Surgical Oncology, Peking University People's Hospital, Peking University, Beijing, China.
Jichao YinXi'an Hospital of Traditional Chinese Medicine, Xi'an, China.
Pengfei YuXijing Hospital, Fourth Military Medical University, Xi'an, China.
Bin BaiXijing Hospital, Fourth Military Medical University, Xi'an, China.
Ruixia PeiXi'an Hospital of Traditional Chinese Medicine, Xi'an, China.
Dingzhang ChenXijing Hospital, Fourth Military Medical University, Xi'an, China.
Guochun YangXi'an Hospital of Traditional Chinese Medicine, Xi'an, China.
Shiqi WangXijing Hospital, Fourth Military Medical University, Xi'an, China.
Mingxi WanKey Laboratory of Biomedical Information Engineering of the Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.
Shaanxi Provincial Hospital of Traditional Chinese Medicine · CNAir Force Medical University · CNXi'an Jiaotong University · CNPeking University · CNXijing Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundComputer-aided diagnosis (CAD) systems are being applied to the ultrasonographic diagnosis of malignant thyroid nodules, but it remains controversial whether the systems add any accuracy for radiologists.

objectiveTo determine the accuracy of CAD systems in diagnosing malignant thyroid nodules.

methodsPubMed, EMBASE, and the Cochrane Library were searched for studies on the diagnostic performance of CAD systems. The diagnostic performance was assessed by pooled sensitivity and specificity, and their accuracy was compared with that of radiologists. The present systematic review was registered in PROSPERO (CRD42019134460).

resultsNineteen studies with 4,781 thyroid nodules were included. Both the classic machine learning- and the deep learning-based CAD system had good performance in diagnosing malignant thyroid nodules (classic machine learning: sensitivity 0.86 [95% CI 0.79-0.92], specificity 0.85 [95% CI 0.77-0.91], diagnostic odds ratio (DOR) 37.41 [95% CI 24.91-56.20]; deep learning: sensitivity 0.89 [95% CI 0.81-0.93], specificity 0.84 [95% CI 0.75-0.90], DOR 40.87 [95% CI 18.13-92.13]). The diagnostic performance of the deep learning-based CAD system was comparable to that of the radiologists (sensitivity 0.87 [95% CI 0.78-0.93] vs. 0.87 [95% CI 0.85-0.89], specificity 0.85 [95% CI 0.76-0.91] vs. 0.87 [95% CI 0.81-0.91], DOR 40.12 [95% CI 15.58-103.33] vs. DOR 44.88 [95% CI 30.71-65.57]).

conclusionsThe CAD systems demonstrated good performance in diagnosing malignant thyroid nodules. However, experienced radiologists may still have an advantage over CAD systems during real-time diagnosis.

Indexed as

Artificial intelligenceThyroid noduleUltrasonography

Identifiers

PMID32903956
PMCPMC7445671
OpenAlexW2992208066

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.