Abstract: The present disclosure relates to noninvasive methods for detecting liver fibrosis. Disclosed herein are noninvasive liver fibrosis detection methods that use Doppler Ultrasound devices and a physics-based machine learning method. Further disclosed herein are methods for detecting liver fibrosis in a subject by detecting and measuring the presence of a shift in the frequency of blood flow in the hepatic vein as compared to the frequency of blood flow in the portal vein.

Abstract: Embodiments of the present systems and methods may provide improved capability to predict the risk of recurrence of ductal carcinoma in situ (DCIS) conditions using whole slide image analysis based on machine learning techniques. For example, in an embodiment, a computer-implemented method for determining treatment of a patient may comprise receiving an image of living tissue of a patient, annotating the entire image into tissue structures, extracting texture features from the annotated image, determining a distribution of the extracted texture features relative to tissue conditions, classifying the patient into a risk group based on the distribution, and treating the patient accordingly based on the risk group.

Abstract: The present disclosure relates to noninvasive methods for detecting liver fibrosis. Disclosed herein are noninvasive liver fibrosis detection methods that use Doppler Ultrasound devices and a physics-based machine learning method. Further disclosed herein are methods for detecting liver fibrosis in a subject by detecting and measuring the presence of a shift in the frequency of blood flow in the hepatic vein as compared to the frequency of blood flow in the portal vein.