Evidence map›Paper›PMID 23822459›Full record

ArticleMedical physics2013

Quality assurance and training procedures for computer-aided detection and diagnosis systems in clinical use.

Zhimin Huo, Ronald M Summers, Sophie Paquerault, Joseph Lo, Jeffrey Hoffmeister, Samuel G Armato, Matthew T Freedman, Jesse Lin, Shih-Chung Ben Lo, Nicholas Petrick and 4 more

Open access · bronzeAbstract read
In one paragraph

Article in Medical physics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
3.0field-weighted citation impact, top 8% 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

9 citing papers in PubMed, 25 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Review
  6. Article
  7. Deep Learning in Medical Image Analysis.Advances in experimental medicine and biology · 2020
    Review
  8. Progress in Fully Automated Abdominal CT Interpretation.AJR. American journal of roentgenology · 2016
    Review
  9. 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

14 authors at 8 institutions in 1 country.

Zhimin HuoCarestream Health Inc., 1049 Ridge Road West, Rochester, New York 14615, USA.
Ronald M Summers
Sophie Paquerault
Joseph Lo
Jeffrey Hoffmeister
Samuel G Armato
Matthew T Freedman
Jesse Lin
Shih-Chung Ben Lo
Nicholas Petrick
Berkman Sahiner
David Fryd
Hiroyuki Yoshida
Heang-Ping Chan
Center for Devices and Radiological Health · USGeorgetown University · USMolina Center for Energy and the Environment · USDuke University Hospital · USMassachusetts General Hospital · USUnited States Food and Drug Administration · USUniversity of Chicago · USUniversity of Michigan–Ann Arbor · US

Funding

Computer Aided Detection for Radiologic ImagesZIACL040004 · CLC · CLINICAL CENTER · PI SUMMERS, RONALD M. · 2009 to 2025
$0k
6 · The paper itself

Abstract

Computer-aided detection/diagnosis (CAD) is increasingly used for decision support by clinicians for detection and interpretation of diseases. However, there are no quality assurance (QA) requirements for CAD in clinical use at present. QA of CAD is important so that end users can be made aware of changes in CAD performance both due to intentional or unintentional causes. In addition, end-user training is critical to prevent improper use of CAD, which could potentially result in lower overall clinical performance. Research on QA of CAD and user training are limited to date. The purpose of this paper is to bring attention to these issues, inform the readers of the opinions of the members of the American Association of Physicists in Medicine (AAPM) CAD subcommittee, and thus stimulate further discussion in the CAD community on these topics. The recommendations in this paper are intended to be work items for AAPM task groups that will be formed to address QA and user training issues on CAD in the future. The work items may serve as a framework for the discussion and eventual design of detailed QA and training procedures for physicists and users of CAD. Some of the recommendations are considered by the subcommittee to be reasonably easy and practical and can be implemented immediately by the end users; others are considered to be "best practice" approaches, which may require significant effort, additional tools, and proper training to implement. The eventual standardization of the requirements of QA procedures for CAD will have to be determined through consensus from members of the CAD community, and user training may require support of professional societies. It is expected that high-quality CAD and proper use of CAD could allow these systems to achieve their true potential, thus benefiting both the patients and the clinicians, and may bring about more widespread clinical use of CAD for many other diseases and applications. It is hoped that the awareness of the need for appropriate CAD QA and user training will stimulate new ideas and approaches for implementing such procedures efficiently and effectively as well as funding opportunities to fulfill such critical efforts.

Indexed as

Diagnosis, Computer-AssistedEducation, MedicalQuality ControlReference StandardsSoftware

Identifiers

PMID23822459
PMCPMC5438240
OpenAlexW2089020430

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.