Literature DB >> 33937783

Challenges Related to Artificial Intelligence Research in Medical Imaging and the Importance of Image Analysis Competitions.

Luciano M Prevedello1, Safwan S Halabi1, George Shih1, Carol C Wu1, Marc D Kohli1, Falgun H Chokshi1, Bradley J Erickson1, Jayashree Kalpathy-Cramer1, Katherine P Andriole1, Adam E Flanders1.   

Abstract

In recent years, there has been enormous interest in applying artificial intelligence (AI) to radiology. Although some of this interest may have been driven by exaggerated expectations that the technology can outperform radiologists in some tasks, there is a growing body of evidence that illustrates its limitations in medical imaging. The true potential of the technique probably lies somewhere in the middle, and AI will ultimately play a key role in medical imaging in the future. The limitless power of computers makes AI an ideal candidate to provide the standardization, consistency, and dependability needed to support radiologists in their mission to provide excellent patient care. However, important roadblocks currently limit the expansion of this field in medical imaging. This article reviews some of the challenges and potential solutions to advance the field forward, with focus on the experience gained by hosting image-based competitions. 2019 by the Radiological Society of North America, Inc.

Entities:  

Year:  2019        PMID: 33937783      PMCID: PMC8017381          DOI: 10.1148/ryai.2019180031

Source DB:  PubMed          Journal:  Radiol Artif Intell        ISSN: 2638-6100


  21 in total

1.  A De-Identification Pipeline for Ultrasound Medical Images in DICOM Format.

Authors:  Eriksson Monteiro; Carlos Costa; José Luís Oliveira
Journal:  J Med Syst       Date:  2017-04-13       Impact factor: 4.460

2.  DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning.

Authors:  Ke Yan; Xiaosong Wang; Le Lu; Ronald M Summers
Journal:  J Med Imaging (Bellingham)       Date:  2018-07-20

3.  Degenerative lumbar spinal canal stenosis: intra- and inter-reader agreement for magnetic resonance imaging parameters.

Authors:  Sebastian Winklhofer; Ulrike Held; Jakob M Burgstaller; Tim Finkenstaedt; Nicolae Bolog; Nils Ulrich; Johann Steurer; Gustav Andreisek; Filippo Del Grande
Journal:  Eur Spine J       Date:  2016-06-22       Impact factor: 3.134

4.  Content-Based Image Retrieval System for Pulmonary Nodules: Assisting Radiologists in Self-Learning and Diagnosis of Lung Cancer.

Authors:  Ashis Kumar Dhara; Sudipta Mukhopadhyay; Anirvan Dutta; Mandeep Garg; Niranjan Khandelwal
Journal:  J Digit Imaging       Date:  2017-02       Impact factor: 4.056

5.  Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks.

Authors:  D H Kim; T MacKinnon
Journal:  Clin Radiol       Date:  2017-12-18       Impact factor: 2.350

6.  The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository.

Authors:  Kenneth Clark; Bruce Vendt; Kirk Smith; John Freymann; Justin Kirby; Paul Koppel; Stephen Moore; Stanley Phillips; David Maffitt; Michael Pringle; Lawrence Tarbox; Fred Prior
Journal:  J Digit Imaging       Date:  2013-12       Impact factor: 4.056

7.  Performance of a Deep-Learning Neural Network Model in Assessing Skeletal Maturity on Pediatric Hand Radiographs.

Authors:  David B Larson; Matthew C Chen; Matthew P Lungren; Safwan S Halabi; Nicholas V Stence; Curtis P Langlotz
Journal:  Radiology       Date:  2017-11-02       Impact factor: 11.105

8.  Automated segmentation of hippocampal subfields in drug-naïve patients with Alzheimer disease.

Authors:  H K Lim; S C Hong; W S Jung; K J Ahn; W Y Won; C Hahn; I S Kim; C U Lee
Journal:  AJNR Am J Neuroradiol       Date:  2012-10-04       Impact factor: 3.825

9.  Quantitative parameters of CT texture analysis as potential markersfor early prediction of spontaneous intracranial hemorrhage enlargement.

Authors:  Qijun Shen; Yanna Shan; Zhengyu Hu; Wenhui Chen; Bing Yang; Jing Han; Yanfang Huang; Wen Xu; Zhan Feng
Journal:  Eur Radiol       Date:  2018-04-30       Impact factor: 5.315

10.  Distributed deep learning networks among institutions for medical imaging.

Authors:  Ken Chang; Niranjan Balachandar; Carson Lam; Darvin Yi; James Brown; Andrew Beers; Bruce Rosen; Daniel L Rubin; Jayashree Kalpathy-Cramer
Journal:  J Am Med Inform Assoc       Date:  2018-08-01       Impact factor: 7.942

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  15 in total

Review 1.  Artificial Intelligence for Radiation Oncology Applications Using Public Datasets.

Authors:  Kareem A Wahid; Enrico Glerean; Jaakko Sahlsten; Joel Jaskari; Kimmo Kaski; Mohamed A Naser; Renjie He; Abdallah S R Mohamed; Clifton D Fuller
Journal:  Semin Radiat Oncol       Date:  2022-10       Impact factor: 5.421

Review 2.  A narrative review of deep learning applications in lung cancer research: from screening to prognostication.

Authors:  Jong Hyuk Lee; Eui Jin Hwang; Hyungjin Kim; Chang Min Park
Journal:  Transl Lung Cancer Res       Date:  2022-06

3.  Toward an Ecologically Valid Conceptual Framework for the Use of Artificial Intelligence in Clinical Settings: Need for Systems Thinking, Accountability, Decision-making, Trust, and Patient Safety Considerations in Safeguarding the Technology and Clinicians.

Authors:  Avishek Choudhury
Journal:  JMIR Hum Factors       Date:  2022-06-21

4.  Effect of Training Data Volume on Performance of Convolutional Neural Network Pneumothorax Classifiers.

Authors:  Yee Liang Thian; Dian Wen Ng; James Thomas Patrick Decourcy Hallinan; Pooja Jagmohan; Soon Yiew Sia; Jalila Sayed Adnan Mohamed; Swee Tian Quek; Mengling Feng
Journal:  J Digit Imaging       Date:  2022-03-03       Impact factor: 4.903

5.  Weakly-supervised High-resolution Segmentation of Mammography Images for Breast Cancer Diagnosis.

Authors:  Carlos Fernandez-Granda; Krzysztof J Geras; Kangning Liu; Yiqiu Shen; Nan Wu; Jakub Chłędowski
Journal:  Proc Mach Learn Res       Date:  2021-07

6.  Development and application of artificial intelligence in cardiac imaging.

Authors:  Beibei Jiang; Ning Guo; Yinghui Ge; Lu Zhang; Matthijs Oudkerk; Xueqian Xie
Journal:  Br J Radiol       Date:  2020-02-06       Impact factor: 3.039

Review 7.  Artificial intelligence and machine learning for medical imaging: A technology review.

Authors:  Ana Barragán-Montero; Umair Javaid; Gilmer Valdés; Dan Nguyen; Paul Desbordes; Benoit Macq; Siri Willems; Liesbeth Vandewinckele; Mats Holmström; Fredrik Löfman; Steven Michiels; Kevin Souris; Edmond Sterpin; John A Lee
Journal:  Phys Med       Date:  2021-05-09       Impact factor: 2.685

8.  Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging.

Authors:  José Daniel López-Cabrera; Rubén Orozco-Morales; Jorge Armando Portal-Diaz; Orlando Lovelle-Enríquez; Marlén Pérez-Díaz
Journal:  Health Technol (Berl)       Date:  2021-02-05

9.  Creating a training set for artificial intelligence from initial segmentations of airways.

Authors:  Ivan Dudurych; Antonio Garcia-Uceda; Zaigham Saghir; Harm A W M Tiddens; Rozemarijn Vliegenthart; Marleen de Bruijne
Journal:  Eur Radiol Exp       Date:  2021-11-29

10.  Simulated diagnostic performance of low-field MRI: Harnessing open-access datasets to evaluate novel devices.

Authors:  T Campbell Arnold; Steven N Baldassano; Brian Litt; Joel M Stein
Journal:  Magn Reson Imaging       Date:  2021-12-28       Impact factor: 3.130

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