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.

Systems and methods for virtual pancreatography (VP) are disclosed for non-invasive diagnosis and classification of pancreatic lesions. VP is an end-to-end visual diagnosis system that includes: automatic segmentation of the pancreatic gland and the lesions, extraction of the primary pancreatic duct, automatic classification of lesions into a plurality of lesion types, and specialized 3D and 2D exploratory visualizations of the pancreas, lesions and surrounding anatomy. Volume rendering is combined with pancreas and lesion centric visualizations and measurements for effective diagnosis.

Described herein are embodiments of an approach to diagnosing pancreatic cystic lesions using light scattering spectroscopy. In some embodiments, the approach includes an apparatus including a spatial gating probe to isolate light reflected by the epithelial tissue of the internal cyst surface using spatial gating. In some further embodiments, the apparatus includes a scanning fiber probe that is capable of rotational and linear motion in order to scan the entire internal surface of the cyst. Use of such an approach may be advantageous to improve the accuracy of diagnosing pancreatic cystic lesions as cancerous, precancerous, or benign.

Aspects of the disclosure relate to systems and methods for obtaining and interpreting magnetic resonance spectroscopy (MRS) data obtained from the pancreas of a subject. In some embodiments, systems and methods of the disclosure relate to analyzing MRS spectra of metabolites, for example y-Aminobutyric acid (GABA), to assess pancreatic islet density and function in a subject. In some embodiments, systems and methods described by the disclosure are useful for the diagnosis and/or treatment of diseases associated with impaired pancreatic function, for example 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.

Machine learning in an artificial pancreas is described. An artificial pancreas system may include a wearable glucose monitoring device, an insulin delivery system, and a computing device. Broadly speaking, the wearable glucose monitoring device provides glucose measurements of a person continuously. The artificial pancreas algorithm, which may be implemented at the computing device, determines doses of insulin to deliver to the person based on a variety of aspects for the purpose of maintaining the person's glucose within a target range, as indicated by those glucose measurements. The insulin delivery system then delivers those determined doses to the person. As the artificial pancreas algorithm determines insulin doses for the person over time and effectiveness of the insulin doses to maintain the person's glucose level in the target range is observed, an underlying model of the artificial pancreas algorithm may be updated to better determine insulin doses.

The present invention provides, among other things, methods of identifying cancerous or pre-cancerous tissue including providing a first region of tissue from a subject, calculating a roughness exponent for the first region of tissue, and comparing the roughness exponent of the first region of tissue to 0.5, wherein a difference of less than 0.2 between the roughness exponent of the first region of tissue and 0.5 indicates that the tissue is cancerous or pre-cancerous. Additionally, the present invention provides methods including providing a first view of a region of tissue, providing a second view of a region of tissue, calculating a first fractal dimension for the first view of the region of tissue, and calculating a second fractal dimension for the second view of the region of tissue, wherein if the fractal dimension of at least one of the first fractal dimension and the second fractal dimension is in the fractal zone, the region of tissue is considered cancerous. Also provided are systems for performing these assessments.

Described herein are embodiments of an approach to diagnosing pancreatic cystic lesions using light scattering spectroscopy. In some embodiments, the approach includes an apparatus including a spatial gating probe to isolate light reflected by the epithelial tissue of the internal cyst surface using spatial gating. In some further embodiments, the apparatus includes a scanning fiber probe that is capable of rotational and linear motion in order to scan the entire internal surface of the cyst. Use of such an approach may be advantageous to improve the accuracy of diagnosing pancreatic cystic lesions as cancerous, precancerous, or benign.

A medical device for the electromagnetic tracking of a medical instrument transported through the medical device. The medical device has a central axis and a channel that receives and transports a medical instrument through the medical device. The channel extends to a distal portion of the medical device and connects with an opening in the medical device that is not aligned with the central axis. The medical device includes a tracking component that is a plurality of coordinated electromagnetic sensors for generating a virtual axis of travel for the medical instrument, with the virtual axis passing through the opening of the device and being aligned with tool insertion axis.

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