Deep Learning Under Scrutiny: Performance Against Health Care Professionals in Detecting Diseases from Medical Imaging - Systematic Review and Meta-Analysis

2019 ◽  
Author(s):  
Livia Faes ◽  
Xiaoxuan Liu ◽  
Aditya Kale ◽  
Alice Bruynseels ◽  
Mohith Shamdas ◽  
...  
Keyword(s):  
Systematic Review ◽  
Health Care ◽  
Deep Learning ◽  
Medical Imaging ◽  
Health Care Professionals ◽  
Meta Analysis

Weight bias among health care professionals: A systematic review and meta‐analysis

Obesity ◽  
2021 ◽  
Author(s):  
Blake J. Lawrence ◽  
Deborah Kerr ◽  
Christina M. Pollard ◽  
Mary Theophilus ◽  
Elise Alexander ◽  
...  
Keyword(s):  
Systematic Review ◽  
Health Care ◽  
Health Care Professionals ◽  
Meta Analysis ◽  
Weight Bias

scholarly journals Prevalence of anxiety in health care professionals during the COVID-19 pandemic: A rapid systematic review (on published articles in Medline) with meta-analysis

2021 ◽  
Vol 107 ◽  
pp. 110244
Author(s):  
Javier Santabárbara ◽  
Juan Bueno-Notivol ◽  
Darren M. Lipnicki ◽  
Beatriz Olaya ◽  
María Pérez-Moreno ◽  
...  
Keyword(s):  
Systematic Review ◽  
Health Care ◽  
Health Care Professionals ◽  
Meta Analysis

scholarly journals Audiovisual feedback device use by health care professionals during CPR: A systematic review and meta-analysis of randomised and non-randomised trials

Resuscitation ◽  
2014 ◽  
Vol 85 (4) ◽  
pp. 460-471 ◽  
Author(s):  
Shelley Kirkbright ◽  
Judith Finn ◽  
Hideo Tohira ◽  
Alexandra Bremner ◽  
Ian Jacobs ◽  
...  
Keyword(s):  
Systematic Review ◽  
Health Care ◽  
Health Care Professionals ◽  
Meta Analysis ◽  
Randomised Trials ◽  
Device Use

scholarly journals Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Ravi Aggarwal ◽  
Viknesh Sounderajah ◽  
Guy Martin ◽  
Daniel S. W. Ting ◽  
Alan Karthikesalingam ◽  
...  
Keyword(s):  
Systematic Review ◽  
Deep Learning ◽  
Medical Imaging ◽  
Diagnostic Accuracy ◽  
Breast Imaging ◽  
Meta Analysis ◽  
Digital Breast Tomosynthesis ◽  
Medical Diagnostics ◽  
Age Related Macular Degeneration ◽  
Age Related

AbstractDeep learning (DL) has the potential to transform medical diagnostics. However, the diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic accuracy of DL algorithms to identify pathology in medical imaging. Searches were conducted in Medline and EMBASE up to January 2020. We identified 11,921 studies, of which 503 were included in the systematic review. Eighty-two studies in ophthalmology, 82 in breast disease and 115 in respiratory disease were included for meta-analysis. Two hundred twenty-four studies in other specialities were included for qualitative review. Peer-reviewed studies that reported on the diagnostic accuracy of DL algorithms to identify pathology using medical imaging were included. Primary outcomes were measures of diagnostic accuracy, study design and reporting standards in the literature. Estimates were pooled using random-effects meta-analysis. In ophthalmology, AUC’s ranged between 0.933 and 1 for diagnosing diabetic retinopathy, age-related macular degeneration and glaucoma on retinal fundus photographs and optical coherence tomography. In respiratory imaging, AUC’s ranged between 0.864 and 0.937 for diagnosing lung nodules or lung cancer on chest X-ray or CT scan. For breast imaging, AUC’s ranged between 0.868 and 0.909 for diagnosing breast cancer on mammogram, ultrasound, MRI and digital breast tomosynthesis. Heterogeneity was high between studies and extensive variation in methodology, terminology and outcome measures was noted. This can lead to an overestimation of the diagnostic accuracy of DL algorithms on medical imaging. There is an immediate need for the development of artificial intelligence-specific EQUATOR guidelines, particularly STARD, in order to provide guidance around key issues in this field.

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