Impact of a CAD system in a screen-film mammography screening program: A prospective study

Gómez, Sonia Sánchez; Tabanera, M. Torres; Bolívar, A. Vega; Miranda, Morgana; Mazo, Alejandro; Ruiz, Miguel Á.; Miravete, Paula Martínez; Asturiano, E. Lag; Muñoz-Cacho, Pedro; Macias, Thomas

doi:10.1016/j.ejrad.2010.08.031

Cited by 8 publications

(7 citation statements)

References 20 publications

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“…In the 6 studies 20,22,24,29,39,41 evaluating CDSSs in a representative clinical environment for the same 10 evaluation metrics, 20 results were reported with statistical significance. Of these, 16 (80%) showed no difference in performance, and 4 (20%) reported an increase in sensitivity, area under the receiver operating curves, PPV, or interobserver agreement (eTable 4 in the Supplement).…”

Section: Resultsmentioning

confidence: 99%

Association of Clinician Diagnostic Performance With Machine Learning–Based Decision Support Systems

et al. 2021

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Key Points Question Is clinician diagnostic performance associated with the use of machine learning–based clinical decision support systems? Findings In this systematic review of 37 studies, no robust evidence was found to suggest an association between the use of machine learning–based clinical algorithms to support rather than replace human decision-making and improved clinician diagnostic performance. Meaning Caution should be observed when estimating the current ability of machine learning algorithms to affect patient care, and emphasis on the evaluation of the human-computer interaction is needed.

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Section: Resultsmentioning

confidence: 99%

Association of Clinician Diagnostic Performance With Machine Learning–Based Decision Support Systems

et al. 2021

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“…Many studies had not been performed in screening settings or had selected or enriched populations, sometimes without comparison between single reading + CAD and double reading [ 14 , 29 - 57 ]. Nine studies had large populations, but compared only single reading + CAD with single reading [ 58 - 66 ]. One study that only described different cancer types was excluded [ 67 ].…”

Section: Resultsmentioning

confidence: 99%

Is single reading with computer-aided detection (CAD) as good as double reading in mammography screening? A systematic review

et al. 2012

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BackgroundIn accordance with European guidelines, mammography screening comprises independent readings by two breast radiologists (double reading). CAD (computer-aided detection) has been suggested to complement or replace one of the two readers (single reading + CAD).The aim of this systematic review is to address the following question: Is the reading of mammographic x-ray images by a single breast radiologist together with CAD at least as accurate as double reading?MethodsThe electronic literature search included the databases Pub Med, EMBASE and The Cochrane Library. Two independent reviewers assessed abstracts and full-text articles.Results1049 abstracts were identified, of which 996 were excluded with reference to inclusion and exclusion criteria; 53 full-text articles were assessed for eligibility. Finally, four articles were included in the qualitative analysis, and one in a GRADE synthesis.ConclusionsThe scientific evidence is insufficient to determine whether the accuracy of single reading + CAD is at least equivalent to that obtained in standard practice, i.e. double reading where two breast radiologists independently read the mammographic images.

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“…NB classifier calculates the posterior probability of the class c given the set of features (33) where P(x i | c) is the probability of the feature x i given the class c and P(c) is the prior probability of the class [135]. Both probabilities can be estimated from the frequency table.…”

Section: Classificationmentioning

confidence: 99%

“…CAD helped the doctors to improve the interpretations of images in terms of accuracy in detection and productivity in time to read and interpret the images [31][32][33][34][35][36][37]. A study regarding CAD systems showed an increase in the radiologists' performance for those who used CAD systems [38].…”

Section: Introductionmentioning

confidence: 99%

Methods Used in Computer-Aided Diagnosis for Breast Cancer Detection Using Mammograms: A Review

Ramadan

2020

Journal of Healthcare Engineering

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According to the American Cancer Society’s forecasts for 2019, there will be about 268,600 new cases in the United States with invasive breast cancer in women, about 62,930 new noninvasive cases, and about 41,760 death cases from breast cancer. As a result, there is a high demand for breast imaging specialists as indicated in a recent report for the Institute of Medicine and National Research Council. One way to meet this demand is through developing Computer-Aided Diagnosis (CAD) systems for breast cancer detection and diagnosis using mammograms. This study aims to review recent advancements and developments in CAD systems for breast cancer detection and diagnosis using mammograms and to give an overview of the methods used in its steps starting from preprocessing and enhancement step and ending in classification step. The current level of performance for the CAD systems is encouraging but not enough to make CAD systems standalone detection and diagnose clinical systems. Unless the performance of CAD systems enhanced dramatically from its current level by enhancing the existing methods, exploiting new promising methods in pattern recognition like data augmentation in deep learning and exploiting the advances in computational power of computers, CAD systems will continue to be a second opinion clinical procedure.

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Impact of a CAD system in a screen-film mammography screening program: A prospective study

Cited by 8 publications

References 20 publications

Association of Clinician Diagnostic Performance With Machine Learning–Based Decision Support Systems

Association of Clinician Diagnostic Performance With Machine Learning–Based Decision Support Systems

Is single reading with computer-aided detection (CAD) as good as double reading in mammography screening? A systematic review

Methods Used in Computer-Aided Diagnosis for Breast Cancer Detection Using Mammograms: A Review

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