Literature DB >> 28026210

Interpretive Error in Radiology.

Stephen Waite1, Jinel Scott1, Brian Gale1, Travis Fuchs1, Srinivas Kolla1, Deborah Reede1.   

Abstract

OBJECTIVE: Although imaging technology has advanced significantly since the work of Garland in 1949, interpretive error rates remain unchanged. In addition to patient harm, interpretive errors are a major cause of litigation and distress to radiologists. In this article, we discuss the mechanics involved in searching an image, categorize omission errors, and discuss factors influencing diagnostic accuracy. Potential individual- and system-based solutions to mitigate or eliminate errors are also discussed.
CONCLUSION: Radiologists use visual detection, pattern recognition, memory, and cognitive reasoning to synthesize final interpretations of radiologic studies. This synthesis is performed in an environment in which there are numerous extrinsic distractors, increasing workloads and fatigue. Given the ultimately human task of perception, some degree of error is likely inevitable even with experienced observers. However, an understanding of the causes of interpretive errors can help in the development of tools to mitigate errors and improve patient safety.

Entities:  

Keywords:  bias; computer-aided detection (CAD); error; fatigue; malpractice; perception; workload

Mesh:

Year:  2016        PMID: 28026210     DOI: 10.2214/AJR.16.16963

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  28 in total

1.  A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop.

Authors:  Curtis P Langlotz; Bibb Allen; Bradley J Erickson; Jayashree Kalpathy-Cramer; Keith Bigelow; Tessa S Cook; Adam E Flanders; Matthew P Lungren; David S Mendelson; Jeffrey D Rudie; Ge Wang; Krishna Kandarpa
Journal:  Radiology       Date:  2019-04-16       Impact factor: 11.105

2.  Impact of Patient Photos on Detection Accuracy, Decision Confidence and Eye-Tracking Parameters in Chest and Abdomen Images with Tubes and Lines.

Authors:  Elizabeth A Krupinski
Journal:  J Digit Imaging       Date:  2019-10       Impact factor: 4.056

Review 3.  Imaging in polytrauma - Principles and current concepts.

Authors:  Pushpa Bhari Thippeswamy; Raja Bhaskara Rajasekaran
Journal:  J Clin Orthop Trauma       Date:  2020-12-05

Review 4.  The overview of the deep learning integrated into the medical imaging of liver: a review.

Authors:  Kailai Xiang; Baihui Jiang; Dong Shang
Journal:  Hepatol Int       Date:  2021-07-15       Impact factor: 6.047

Review 5.  Bias in Radiology: The How and Why of Misses and Misinterpretations.

Authors:  Lindsay P Busby; Jesse L Courtier; Christine M Glastonbury
Journal:  Radiographics       Date:  2017-12-01       Impact factor: 5.333

6.  Performance Comparison of Individual and Ensemble CNN Models for the Classification of Brain 18F-FDG-PET Scans.

Authors:  Tomomi Nobashi; Claudia Zacharias; Jason K Ellis; Valentina Ferri; Mary Ellen Koran; Benjamin L Franc; Andrei Iagaru; Guido A Davidzon
Journal:  J Digit Imaging       Date:  2020-04       Impact factor: 4.056

7.  Attention-Enriched Deep Learning Model for Breast Tumor Segmentation in Ultrasound Images.

Authors:  Aleksandar Vakanski; Min Xian; Phoebe E Freer
Journal:  Ultrasound Med Biol       Date:  2020-07-21       Impact factor: 2.998

8.  Diagnostic performance of standardized ultrasound protocol for detecting perforation in pediatric appendicitis.

Authors:  Erica L Riedesel; Blake C Weber; Matthew W Shore; Randi S Cartmill; Daniel J Ostlie; Charles M Leys; Kara G Gill; Jonathan E Kohler
Journal:  Pediatr Radiol       Date:  2019-07-24

9.  Does anthropomorphic model design in ex vivo studies affect diagnostic accuracy for dental root fracture using CBCT?

Authors:  Fedil Andraws Yalda; Rosalyn J Clarkson; Jonathan Davies; Peter G J Rout; Anita Sengupta; Keith Horner
Journal:  Dentomaxillofac Radiol       Date:  2020-06-09       Impact factor: 2.419

10.  The challenge of applying digital image processing software on intraoral radiographs for osteoporosis risk assessment.

Authors:  Joanna Gullberg; Ayman Al-Okshi; Dalia Homar Asan; Anita Zainea; Daniel Sundh; Mattias Lorentzon; Christina Lindh
Journal:  Dentomaxillofac Radiol       Date:  2021-07-29       Impact factor: 2.419

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