Abstract: A machine learning model for stratification of early diabetic foot complication using thermogram images is provided that is operable to predictively diagnose a risk for diabetic foot ulceration formation via receiving a thermogram of a foot; identifying, via a machine learning model, a risk factor of diabetic foot ulceration on the foot; and outputting, from the machine learning model, the risk factor as a diagnosis.

Abstract: Systems, methods, apparatuses, and computer program products for real-time, personalized cardiac monitoring for early detection of heart-beat anomalies. One method may include a device selecting at least one set of clean ECG segments, and at least one set of corrupted ECG segments; transforming at least one of a one-dimensional or two-dimensional version cycle-CANs trained to transform ECG signals from at least one different dataset; and restoring the at least one set of corrupted ECG segments based upon a one- or two-dimensional operational cycle-GAN trained over the batches.