Fig. 8: FGFR device overview. | Nature Communications

Fig. 8: FGFR device overview.

From: Development and deployment of a histopathology-based deep learning algorithm for patient prescreening in a clinical trial

Fig. 8

The device is a Docker container with the algorithm combined with error checks that allowed for easy integration into the clinical workflow. It takes an image along with the corresponding metadata as inputs, and outputs the likelihood of FGFR for that image. The device will show explanatory error message for the clinician when slides don’t meet pre-specified criteria (i.e., tissue site must be bladder, from MIBC disease stage, and 10 x magnification available). Similarly, it will notify the user if the image does not pass quality control (i.e., image is corrupted or missing, or there are insufficient high-quality tiles to perform a prediction). These checks ensured that the device would only run-on data of same distribution as the one used for training.

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