Fig. 3: Clinical validation via blind tests, cross-reactivity, concentration prediction, and daily monitoring. | Nature Communications

Fig. 3: Clinical validation via blind tests, cross-reactivity, concentration prediction, and daily monitoring.

From: Sample-to-answer platform for the clinical evaluation of COVID-19 using a deep learning-assisted smartphone-based assay

Fig. 3: Clinical validation via blind tests, cross-reactivity, concentration prediction, and daily monitoring.The alternative text for this image may have been generated using AI.

a–e Blind test; a Workflow of the clinical validation process using a blind test using 1500 test images (n = 1000 patients and n = 500 healthy controls). Using SMARTAI-LFA, we carried out blind tests (n = 1500) of untrained individuals (n = 10) and human experts (n = 10) every 150 test images and compared it with SMARTAI-LFA results, showing great enhancement in sensitivity, specificity, and accuracy using a SMARTAI-LFA. b The ROC curve and c prediction accuracy. d, e Answers for three positive clinical sample images, clarifying the AI’s decision ability. f Cross-reactivity using different respiratory viruses, revealing no cross-reactivity. g The concentration prediction ability of SMARTAI-LFA using a heat map, representing the ability of quantitative analysis. h The sample concentration prediction with clinical patient sample (female, 33 y) according to dilution factors. i Daily COVID-19 test of clinical sample (Male, 27 y), showing the ability of daily monitoring of virus titers via SMARTAI-LFA. SMARTAI-LFA deep learning-assisted smartphone-based LFA, ROC receiver operating characteristic, AI artificial intelligence.

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