Literature DB >> 35699495

Advancing Research on Medical Image Perception by Strengthening Multidisciplinary Collaboration.

Melissa Treviño1,2, George Birdsong3, Ann Carrigan4, Peter Choyke5, Trafton Drew6, Miguel Eckstein7, Anna Fernandez8,9, Brandon D Gallas10, Maryellen Giger11, Stephen M Hewitt12, Todd S Horowitz1, Yuhong V Jiang13, Bonnie Kudrick14, Susana Martinez-Conde15, Stephen Mitroff16, Linda Nebeling1, Joseph Saltz17, Frank Samuelson10, Steven E Seltzer18,19, Behrouz Shabestari20, Lalitha Shankar21, Eliot Siegel22, Mike Tilkin23, Jennifer S Trueblood24, Alison L Van Dyke8, Aradhana M Venkatesan25, David Whitney26, Jeremy M Wolfe19,27,28.   

Abstract

Medical image interpretation is central to detecting, diagnosing, and staging cancer and many other disorders. At a time when medical imaging is being transformed by digital technologies and artificial intelligence, understanding the basic perceptual and cognitive processes underlying medical image interpretation is vital for increasing diagnosticians' accuracy and performance, improving patient outcomes, and reducing diagnostician burnout. Medical image perception remains substantially understudied. In September 2019, the National Cancer Institute convened a multidisciplinary panel of radiologists and pathologists together with researchers working in medical image perception and adjacent fields of cognition and perception for the "Cognition and Medical Image Perception Think Tank." The Think Tank's key objectives were to identify critical unsolved problems related to visual perception in pathology and radiology from the perspective of diagnosticians, discuss how these clinically relevant questions could be addressed through cognitive and perception research, identify barriers and solutions for transdisciplinary collaborations, define ways to elevate the profile of cognition and perception research within the medical image community, determine the greatest needs to advance medical image perception, and outline future goals and strategies to evaluate progress. The Think Tank emphasized diagnosticians' perspectives as the crucial starting point for medical image perception research, with diagnosticians describing their interpretation process and identifying perceptual and cognitive problems that arise. This article reports the deliberations of the Think Tank participants to address these objectives and highlight opportunities to expand research on medical image perception. Published by Oxford University Press 2021. This work is written by a US Government employee and is in the public domain in the US.

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Mesh:

Year:  2022        PMID: 35699495      PMCID: PMC8826981          DOI: 10.1093/jncics/pkab099

Source DB:  PubMed          Journal:  JNCI Cancer Spectr        ISSN: 2515-5091


  52 in total

1.  Visual search, image organization, and reader error in roentgen diagnosis. Studies of the psycho-physiology of roentgen image perception.

Authors:  W J TUDDENHAM
Journal:  Radiology       Date:  1962-05       Impact factor: 11.105

2.  The effects of local prevalence and explicit expectations on search termination times.

Authors:  Kazuya Ishibashi; Shinichi Kita; Jeremy M Wolfe
Journal:  Atten Percept Psychophys       Date:  2012-01       Impact factor: 2.199

3.  An Observational Study of Deep Learning and Automated Evaluation of Cervical Images for Cancer Screening.

Authors:  Liming Hu; David Bell; Sameer Antani; Zhiyun Xue; Kai Yu; Matthew P Horning; Noni Gachuhi; Benjamin Wilson; Mayoore S Jaiswal; Brian Befano; L Rodney Long; Rolando Herrero; Mark H Einstein; Robert D Burk; Maria Demarco; Julia C Gage; Ana Cecilia Rodriguez; Nicolas Wentzensen; Mark Schiffman
Journal:  J Natl Cancer Inst       Date:  2019-09-01       Impact factor: 13.506

Review 4.  Physician wellness: a missing quality indicator.

Authors:  Jean E Wallace; Jane B Lemaire; William A Ghali
Journal:  Lancet       Date:  2009-11-14       Impact factor: 79.321

Review 5.  Molecular Testing and the Pathologist's Role in Clinical Trials of Breast Cancer.

Authors:  Hyo Sook Han; Anthony M Magliocco
Journal:  Clin Breast Cancer       Date:  2016-02-12       Impact factor: 3.225

6.  Impact of prevalence and case distribution in lab-based diagnostic imaging studies.

Authors:  Brandon D Gallas; Weijie Chen; Elodia Cole; Robert Ochs; Nicholas Petrick; Etta D Pisano; Berkman Sahiner; Frank W Samuelson; Kyle J Myers
Journal:  J Med Imaging (Bellingham)       Date:  2019-01-21

7.  Long radiology workdays reduce detection and accommodation accuracy.

Authors:  Elizabeth A Krupinski; Kevin S Berbaum; Robert T Caldwell; Kevin M Schartz; John Kim
Journal:  J Am Coll Radiol       Date:  2010-09       Impact factor: 5.532

8.  The pop-up research centre - Challenges and opportunities.

Authors:  R J Toomey; M F McEntee; L A Rainford
Journal:  Radiography (Lond)       Date:  2019-06-26

9.  PRISM: A Platform for Imaging in Precision Medicine.

Authors:  Ashish Sharma; Lawrence Tarbox; Tahsin Kurc; Jonathan Bona; Kirk Smith; Pradeeban Kathiravelu; Erich Bremer; Joel H Saltz; Fred Prior
Journal:  JCO Clin Cancer Inform       Date:  2020-06

10.  A Pathologist-Annotated Dataset for Validating Artificial Intelligence: A Project Description and Pilot Study.

Authors:  Sarah N Dudgeon; Si Wen; Matthew G Hanna; Rajarsi Gupta; Mohamed Amgad; Manasi Sheth; Hetal Marble; Richard Huang; Markus D Herrmann; Clifford H Szu; Darick Tong; Bruce Werness; Evan Szu; Denis Larsimont; Anant Madabhushi; Evangelos Hytopoulos; Weijie Chen; Rajendra Singh; Steven N Hart; Ashish Sharma; Joel Saltz; Roberto Salgado; Brandon D Gallas
Journal:  J Pathol Inform       Date:  2021-11-15
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