Literature DB >> 21255952

Learning from diagnostic errors: a good way to improve education in radiology.

Antonio Pinto1, Ciro Acampora, Fabio Pinto, Elena Kourdioukova, Luigia Romano, Koenraad Verstraete.   

Abstract

PURPOSE: To evaluate the causes and the main categories of diagnostic errors in radiology as a method for improving education in radiology.
MATERIAL AND METHODS: A Medline search was performed using PubMed (National Library of Medicine, Bethesda, MD) for original research publications discussing errors in diagnosis with specific reference to radiology. The search strategy employed different combinations of the following terms: (1) diagnostic radiology, (2) radiological error and (3) medical negligence. This review was limited to human studies and to English-language literature. Two authors reviewed all the titles and subsequently the abstracts of 491 articles that appeared pertinent. Additional articles were identified by reviewing the reference lists of relevant papers. Finally, the full text of 75 selected articles was reviewed.
RESULTS: Several studies show that the etiology of radiological error is multi-factorial. The main category of claims against radiologists includes the misdiagnoses. Radiologic "misses" typically are one of two types: either missed fractures or missed diagnosis of cancer. The most commonly missed fractures include those in the femur, the navicular bone, and the cervical spine. The second type of "miss" is failure to diagnose cancer. Lack of appreciation of lung nodules on chest radiographs and breast lesions on mammograms are the predominant problems.
CONCLUSION: Diagnostic errors should be considered not as signs of failure, but as learning opportunities.
Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

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Year:  2011        PMID: 21255952     DOI: 10.1016/j.ejrad.2010.12.028

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  9 in total

Review 1.  Errors in imaging patients in the emergency setting.

Authors:  Antonio Pinto; Alfonso Reginelli; Fabio Pinto; Giuseppe Lo Re; Federico Midiri; Carlo Muzj; Luigia Romano; Luca Brunese
Journal:  Br J Radiol       Date:  2016-02-03       Impact factor: 3.039

2.  Experiences with a self-test for Dutch breast screening radiologists: lessons learnt.

Authors:  J M H Timmers; A L M Verbeek; R M Pijnappel; M J M Broeders; G J den Heeten
Journal:  Eur Radiol       Date:  2013-09-22       Impact factor: 5.315

3.  Value of audits in breast cancer screening quality assurance programmes.

Authors:  Tanya D Geertse; Roland Holland; Janine M H Timmers; Ellen Paap; Ruud M Pijnappel; Mireille J M Broeders; Gerard J den Heeten
Journal:  Eur Radiol       Date:  2015-04-23       Impact factor: 5.315

4.  Detection and classification of mandibular fracture on CT scan using deep convolutional neural network.

Authors:  Xuebing Wang; Zineng Xu; Yanhang Tong; Long Xia; Bimeng Jie; Peng Ding; Hailong Bai; Yi Zhang; Yang He
Journal:  Clin Oral Investig       Date:  2022-02-26       Impact factor: 3.573

5.  Sources of error in emergency ultrasonography.

Authors:  Antonio Pinto; Fabio Pinto; Angela Faggian; Giuseppe Rubini; Ferdinando Caranci; Luca Macarini; Eugenio Annibale Genovese; Luca Brunese
Journal:  Crit Ultrasound J       Date:  2013-07-15

6.  Is quality and completeness of reporting of systematic reviews and meta-analyses published in high impact radiology journals associated with citation rates?

Authors:  Christian B van der Pol; Matthew D F McInnes; William Petrcich; Adam S Tunis; Ramez Hanna
Journal:  PLoS One       Date:  2015-03-16       Impact factor: 3.240

Review 7.  Traumatic fractures in adults: missed diagnosis on plain radiographs in the Emergency Department.

Authors:  Antonio Pinto; Daniela Berritto; Anna Russo; Federica Riccitiello; Martina Caruso; Maria Paola Belfiore; Vito Roberto Papapietro; Marina Carotti; Fabio Pinto; Andrea Giovagnoni; Luigia Romano; Roberto Grassi
Journal:  Acta Biomed       Date:  2018-01-19

8.  Synthesis of fracture radiographs with deep neural networks.

Authors:  Nicholas Chedid; Praneeth Sadda; Anish Gonchigar; Jonathan Langdon; Jack Porrino; Andrew Haims; Richard Andrew Taylor
Journal:  Health Inf Sci Syst       Date:  2020-05-30

9.  Deep learning can be used to train naïve, nonprofessional observers to detect diagnostic visual patterns of certain cancers in mammograms: a proof-of-principle study.

Authors:  Jay Hegdé
Journal:  J Med Imaging (Bellingham)       Date:  2020-02-04
  9 in total

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