Deep Learning Models Classify Disease From Medical Imaging

In meta-analysis, sensitivity, specificity comparable for deep learning models and health care professionals

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THURSDAY, Sept. 26, 2019 (HealthDay News) -- Early evidence suggests that diagnostic performance of deep learning models is equivalent to that of health care professionals for interpreting medical imaging, according to a study published online Sept. 25 in The Lancet Digital Health.

Xiaoxuan Liu, M.B.Ch.B., from the University Hospitals Birmingham NHS Foundation Trust in the United Kingdom, and colleagues conducted a systematic review and meta-analysis to assess the diagnostic accuracy of deep learning algorithms versus health care professionals in classifying disease using medical imaging. Binary diagnostic accuracy data were extracted and contingency tables were constructed to derive the outcomes of interest: sensitivity and specificity. Data from 82 studies, describing 147 patient cohorts were included.

The researchers found that based on 69 studies, sensitivity ranged from 9.7 to 100 percent and specificity ranged from 38.9 to 100 percent. In 25 studies, an out-of-sample external validation was performed; 14 compared deep learning models and health care professionals in the same sample. In these studies, when the analysis was restricted to the contingency table for each study reporting the highest accuracy, pooled sensitivity was 87.0 and 86.4 percent for deep learning models and health care professionals, respectively, while pooled specificity was 92.5 and 90.5 percent, respectively.

"From this exploratory meta-analysis, we cautiously state that the accuracy of deep learning algorithms is equivalent to health care professionals, while acknowledging that more studies considering the integration of such algorithms in real-world settings are needed," the authors write.

Several authors disclosed financial ties to the medical technology industry.

Abstract/Full Text

Physician’s Briefing Staff

Physician’s Briefing Staff

Published on September 26, 2019

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