Digital Diagnostics Inc.

Joseph Ferrante

Elliot Swart

Elizabeth Asai

Elektra Efstratiou Alivisatos

Kelsey Gross

Kelsey J Gross

Systems and methods are disclosed herein for determining a diagnosis based on a base skin tone of a patient. In an embodiment, the system receives a base skin tone image of a patient, generates a calibrated base skin tone image by calibrating the base skin tone image using a reference calibration profile, and determines a base skin tone of the patient based on the calibrated base skin tone image. The system receives a concern image of a portion of the patient's skin, and selects a set of machine learning diagnostic models from a plurality of sets of candidate machine learning diagnostic models based on the base skin tone of the patient, each of the sets of candidate machine learning diagnostic models trained to receive the concern image and output a diagnosis of a condition of the patient.

Using a set of machine learning diagnostic models to determine a diagnosis based on a skin tone of a patient

A cleaning wizard monitors and provides feedback for cleaning of medical equipment to ensure that cleaning is performed based on best practices. The cleaning wizard receives a video stream comprising an item of medical equipment and inputs a first set of video frames from the video stream into a first machine learning model. The first machine learning model is trained to output whether the first set of video frames corresponds to activity that initiates a cleaning protocol for the item of medical equipment. Responsive to the cleaning protocol being initiated, the cleaning wizard inputs a second set of video frames into a second machine learning model trained to output whether the second set of frames meets criteria of the cleaning protocol. Responsive to all criteria of the cleaning protocol being met, the cleaning wizard transmits a notification to an operator that the cleaning protocol is complete.

Monitoring surface cleaning of medical surfaces using video streaming

A diagnosis system trains a set of machine-learned diagnosis models that are configured to receive an image of a patient and generate predictions on whether the patient has one or more health conditions. In one embodiment, the set of machine-learned models are trained to generate predictions for images that contain two or more underlying health conditions of the patient. In one instance, the symptoms for the two or more health conditions are shown as two or more overlapping skin abnormalities on the patient. By using the architectures of the set of diagnosis models described herein, the diagnosis system can generate more accurate predictions for images that contain overlapping symptoms for two or more health conditions compared to existing systems.

Growing community of inventors

Cambridge, MA, United States of America

Joseph Ferrante