Systems and methods are provided for neuro stimulation. In one implementation, a system is provided that includes a stimulator introduced into tissue at a target location and a central controller that communicates wirelessly with the stimulator. The stimulator includes a power system that receives wireless energy transmission, and an electrode system that transmits an electrical pulse for stimulating the target location. The central controller includes a power system that wirelessly delivers power to the stimulator, a communication system that wirelessly communicates with the stimulator, and a processing system that controls the power system and the communication system. The central controller may instruct the stimulator to transmit one or more electrical pulses to the target location to affect an endocrine function (e.g., affect the glucose level of a patient).

Embodiments of the present disclosure relate to techniques for inducing physiological perturbations in a subject via neuromodulation, e.g., peripheral neuromodulation of a region of interest of an organ. The nature and degree of the perturbations may be related to the subject's clinical condition. Accordingly, an assessment of one or more characteristics of the perturbations may be used to determine a clinical condition of the subject.

Described herein are methods and systems of performing immunotherapy on a subject and/or determining if a subject will be responsive to ablation immunotherapy.

The subject matter of the present disclosure generally relates to techniques for neuromodulation that include applying energy (e.g., ultrasound energy) into the tissue to cause a change in a glucose transporter pathway molecule and/or an incretin pathway molecule. In one embodiment, the neuromodulation is performed as a treatment of a metabolic disorder.

A method for pancreatic mass diagnosis and patient management includes: receiving CT images of a pancreas of a patient, the pancreas of the patient including a mass; performing a segmentation process on the CT images of the pancreas and the mass to obtain a segmentation mask of the pancreas and the mass of the patient; performing a mask-to-mesh process on the segmentation mask of the pancreas and the mass of the patient to obtain a mesh model of the pancreas and the mass of the patient; performing a classification process on the mesh model of the pancreas and the mass of the patient to identify a type and a grade of a segmented pancreatic mass; and outputting updated CT images of the pancreas of the patient, the updated CT images including the segmented pancreatic mass highlighted thereon and the type and the grade of the segmented pancreatic mass annotated thereon.

A medical image analyzing system and a medical image analyzing method are provided and include inputting at least one patient image into a first model of a first neural network module to obtain a result having determined positions and ranges of an organ and a tumor of the patient image; inputting the result into a plurality of second models of a second neural network module, respectively, to obtain a plurality of prediction values corresponding to each of the plurality of second models and a model number predicting having cancer in the plurality of prediction values; and outputting a determined result based on the model number predicting having cancer and a number threshold value. Further, processes between the first model and the second models can be automated, thereby improving identification rate of pancreatic cancer.

A method for calibration of the MRμTexture method is presented wherein a plurality of model datasets representing a continuum of structures with a continuum of biomarker values is generated by morphing data of a 2D structure or 3D structure of a first known disease state to a 2D structure or a 3D structure of a second known disease state. MRμTexture is applied in silico to extract a simulation data set of texture prevalence for a selected one of a plurality of intermediate morphed conditions corresponding to the plurality of model datasets.

Modulation of neural signaling of a pancreas-related sympathetic nerve is capable of improving glycaemic control by inhibiting T cell activation or migration to the pancreas, and hence providing a way of treating or preventing type 1 diabetes.

A severity calculator analyzes patient information including a patient avatar and a patient state and along with a patient trajectory calculator, computes a predicted outcome which is used to administer treatment. The severity calculator systems and computer program products are also disclosed.

In a method for generating a surrogate marker based on medical image data mapping an image region, the medical image data is detected using a first interface, a first subregion of the image region is selected by segmenting a first structure included in the image region, a first property of the first subregion is extracted, the surrogate marker is determined based on the first property, and the surrogate marker is provided using a second interface.