Staging of AKI according to KDIGO criteria. حسب Christian Porschen (12967868)

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Overall accuracy of human vs. pyAKI generated labels by classification method. حسب Christian Porschen (12967868)

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Comparison between pyAKI’s generated labels and the labels generated by the MIMIC-code implementation of the KDIGO criteria. حسب Christian Porschen (12967868)

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pyAKI data model. حسب Christian Porschen (12967868)

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Workflow of validating the pyAKI pipeline. حسب Christian Porschen (12967868)

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Accuracy of human-assigned and pyAKI-generated labels across different acute kidney injury stages at each point in time. حسب Christian Porschen (12967868)

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Fig 4 - حسب Ashutosh P. Raman (19825936)

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Fig 1 - حسب Ashutosh P. Raman (19825936)

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Emission wavelength and full width at half maximum (FWHM) values for various metabolically important endogenous fluorophores after 405 nm laser excitation [30–31]. حسب Ashutosh P. Raman (19825936)

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Comparison of performance between the machine learning pipelines for tissue classification. حسب Ashutosh P. Raman (19825936)

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Fig 2 - حسب Ashutosh P. Raman (19825936)

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pdig.0000602.t003 - Machine learning approaches in non-contact autofluorescence spectrum classification حسب Ashutosh P. Raman (19825936)

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Fig 7 - حسب Ashutosh P. Raman (19825936)

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Plot of 5 most important principal components (PCs), defined as those PCs making up 95% of total dataset variance together, and their respective variance intensity at each waveleng... حسب Ashutosh P. Raman (19825936)

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Average spectra for both tissue classes, with standard error regions. حسب Ashutosh P. Raman (19825936)

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Fig 9 - حسب Ashutosh P. Raman (19825936)

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Fig 8 - حسب Ashutosh P. Raman (19825936)

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Specific configuration of autofluorescence device with freshly resected tissue sample prior to laser excitation and subsequent signal acquisition. حسب Ashutosh P. Raman (19825936)

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Specific hyperparameters optimized for each non-ANN model in the Nested Grid Search process. حسب Ashutosh P. Raman (19825936)

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