Effects of incorrect computer-aided detection (CAD) output on human decision-making in mammography

Alberdi, Eugenio; Povyakalo, Andrey; Strigini, Lorenzo; Ayton, Peter

doi:10.1016/j.acra.2004.05.012

Cited by 98 publications

(101 citation statements)

References 9 publications

(15 reference statements)

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“…Importantly, specific situations with high degrees of uncertainty (node 21), especially when other more reliable sources of information are missing (node 23), may make operators vulnerable and cause them to rely on computer support more than they would normally do, even if they do not trust its reliability. We found evidence for this in our study of CAD use with difficult-to-detect cancers [35].…”

Section: A Case Study: Computer Aided Detection (Cad) For Mammographysupporting

confidence: 54%

“…Our study provided evidence of automation bias effects in the use of CAD; effects which could not be attributed to complacency and could actually coexist with users' reported mistrust towards the tool [35]. Previous studies had concluded that on average using CAD was either beneficial or ineffectual.…”

Section: A Case Study: Computer Aided Detection (Cad) For Mammographymentioning

confidence: 48%

“…In fact, operators' performance could be worse with computer support even for demands for which the tool provides correct advice. Evidence from the case study on CAD in breast screening strongly supports this view [35,36]. Nodes 18 and 16, in conjunction with node 6, illustrate the "cry wolf" situation that may explain phenomena like this.…”

Section: A Case Study: Computer Aided Detection (Cad) For Mammographymentioning

confidence: 50%

“…Many of the considerations we present originate from a case study we conducted in the area of CAD for breast cancer screening [35][36][37][38][39][40]. In breast screening, expert clinicians ("readers") examine mammograms (X-ray images of a woman's breasts), and decide whether the patient should be "recalled" for further tests because they suspect cancer.…”

Section: A Case Study: Computer Aided Detection (Cad) For Mammographymentioning

confidence: 99%

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Why Are People’s Decisions Sometimes Worse with Computer Support?

Alberdi

Strigini

Povyakalo

et al. 2009

Lecture Notes in Computer Science

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This is the unspecified version of the paper.This version of the publication may differ from the final published version. Abstract. In many applications of computerised decision support, a recognised source of undesired outcomes is operators' apparent over-reliance on automation. For instance, an operator may fail to react to a potentially dangerous situation because a computer fails to generate an alarm. However, the very use of terms like "over-reliance" betrays possible misunderstandings of these phenomena and their causes, which may lead to ineffective corrective action. For instance, training or procedural changes are favored responses, but they do not address all causes of apparently "over-reliant" behaviour. We review relevant literature in the area of "automation bias" and describe the diverse mechanisms that may be involved in human errors when using computer support. We discuss these mechanisms, with reference to errors of omission when using "alerting systems", with the help of examples of novel counterintuitive findings we obtained from a case study in a health care application, as well as other examples from the literature. Permanent repository link

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Section: A Case Study: Computer Aided Detection (Cad) For Mammographysupporting

confidence: 54%

Section: A Case Study: Computer Aided Detection (Cad) For Mammographymentioning

confidence: 48%

Section: A Case Study: Computer Aided Detection (Cad) For Mammographymentioning

confidence: 50%

Section: A Case Study: Computer Aided Detection (Cad) For Mammographymentioning

confidence: 99%

See 2 more Smart Citations

Why Are People’s Decisions Sometimes Worse with Computer Support?

Alberdi

Strigini

Povyakalo

et al. 2009

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…As a result, although CAD is able to detect a fraction of cancers missed by radiologists albeit with a relatively high false-positive rates [18], most of these subtle masses are cued by CAD only on one view and thus are discarded by radiologists as false-positives [19,20]. As important, improperly using CAD (e.g., as "the first reader") could not only increase radiologists' recall rates but also reduce their detection sensitivity in the non-cued regions [21].…”

Section: Introductionmentioning

confidence: 99%

An Interactive System for Computer-Aided Diagnosis of Breast Masses

Wang

Liu

et al. 2012

J Digit Imaging

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Although mammography is the only clinically accepted imaging modality for screening the general population to detect breast cancer, interpreting mammograms is difficult with lower sensitivity and specificity. To provide radiologists "a visual aid" in interpreting mammograms, we developed and tested an interactive system for computeraided detection and diagnosis (CAD) of mass-like cancers. Using this system, an observer can view CAD-cued mass regions depicted on one image and then query any suspicious regions (either cued or not cued by CAD). CAD scheme automatically segments the suspicious region or accepts manually defined region and computes a set of image features. Using content-based image retrieval (CBIR) algorithm, CAD searches for a set of reference images depicting "abnormalities" similar to the queried region. Based on image retrieval results and a decision algorithm, a classification score is assigned to the queried region. In this study, a reference database with 1,800 malignant mass regions and 1,800 benign and CAD-generated false-positive regions was used. A modified CBIR algorithm with a new function of stretching the attributes in the multi-dimensional space and decision scheme was optimized using a genetic algorithm. Using a leave-one-out testing method to classify suspicious mass regions, we compared the classification performance using two CBIR algorithms with either equally weighted or optimally stretched attributes. Using the modified CBIR algorithm, the area under receiver operating characteristic curve was significantly increased from 0.865 ±0.006 to 0.897±0.005 (p<0.001). This study demonstrated the feasibility of developing an interactive CAD system with a large reference database and achieving improved performance.

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Computer‐Aided Diagnosis (CAD) and Image‐Guided Decision Support

2009

PACS and Imaging Informatics

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Effects of incorrect computer-aided detection (CAD) output on human decision-making in mammography

Cited by 98 publications

References 9 publications

Why Are People’s Decisions Sometimes Worse with Computer Support?

Why Are People’s Decisions Sometimes Worse with Computer Support?

An Interactive System for Computer-Aided Diagnosis of Breast Masses

Computer‐Aided Diagnosis (CAD) and Image‐Guided Decision Support

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