Journal
EUROPEAN RADIOLOGY
Volume 31, Issue 10, Pages 7969-7983Publisher
SPRINGER
DOI: 10.1007/s00330-021-07881-2
Keywords
Artificial intelligence; Deep learning; Diagnosis; computer-assisted; Neoplasms; Research design
Funding
- UK Research AMP
- Innovation London Medical Imaging and Artificial Intelligence Centre
- Wellcome Trust EPSRC Centre for Medical Engineering at King's College London [WT203148/Z/16/Z]
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The study reviewed the design and reporting of imaging studies using convolutional neural network models for radiological cancer diagnosis. Findings showed that many studies did not meet current design and reporting guidelines, highlighting opportunities for improvement. Clinical journals demonstrated higher compliance compared to technical journals.
Objectives To perform a systematic review of design and reporting of imaging studies applying convolutional neural network models for radiological cancer diagnosis. Methods A comprehensive search of PUBMED, EMBASE, MEDLINE and SCOPUS was performed for published studies applying convolutional neural network models to radiological cancer diagnosis from January 1, 2016, to August 1, 2020. Two independent reviewers measured compliance with the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Compliance was defined as the proportion of applicable CLAIM items satisfied. Results One hundred eighty-six of 655 screened studies were included. Many studies did not meet the criteria for current design and reporting guidelines. Twenty-seven percent of studies documented eligibility criteria for their data (50/186, 95% CI 21-34%), 31% reported demographics for their study population (58/186, 95% CI 25-39%) and 49% of studies assessed model performance on test data partitions (91/186, 95% CI 42-57%). Median CLAIM compliance was 0.40 (IQR 0.33-0.49). Compliance correlated positively with publication year (rho = 0.15, p = .04) and journal H-index (rho = 0.27, p < .001). Clinical journals demonstrated higher mean compliance than technical journals (0.44 vs. 0.37, p < .001). Conclusions Our findings highlight opportunities for improved design and reporting of convolutional neural network research for radiological cancer diagnosis.
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