A method for analyzing an abdominal disease based on a medical image, includes receiving and preprocessing a medical image obtained by photographing an abdominal region of a patient to detect a plurality of analysis candidate regions and setting one of the plurality of analysis candidate regions as a ROI, calculating a nodule grade based on surface unevenness of the ROI, calculating a cellular heterogeneity coefficient based on pixel homogeneity of the ROI, and predicting and outputting an abdominal disease value based on the nodule grade and the cellular heterogeneity coefficient.

A biliary diagnostic device comprises a tubular body comprising an outer wall and an internal lumen, and a biliary diagnostic sensor comprising for analyzing biological matter in contact with the tubular body. A method of guiding an endoscope to a bile duct comprises inserting the endoscope into a duodenum, engaging a sensor with biological matter, electrically analyzing biological matter with the sensor to identify an electrical parameter, identifying liver bile in the biological matter from the electrical parameter, and guiding the endoscope through the duodenum based on the bile. A method of identifying biological matter comprises engaging a medical device sensor with biological matter in a bile duct, electrically analyzing biological matter with the sensor to identify an electrical parameter, identifying biological matter from a liver, pancreas or gall bladder from the electrical parameter, and outputting indicia of the biological matter to a user of the medical device.

Analysis of a breath sample is a noninvasive point-of-care tool with ever increasing clinical applicability. Herein we describe a method to analyze the content of low-level, trace volatile organic compounds from alveolar breath captured as a breath sample from a patient suspected of having a fatty liver disease. The breath sample is then analyzed using a gas phase analysis methodology, such as gas chromatography-mass spectroscopy (“GCMS”), to generate an analysis result, such as a GCMS spectrum. A computer system is then used to develop a fingerprint pattern from the analysis result. The fingerprint pattern is then used to determine a patient status for the patient. The fingerprint pattern is typically a grouping of 75 to 450 compounds of known concentration which are indicative of a particular fatty liver disease.

A non-invasive system for calculating a human or animal score, the system including a measurement slave device constructed and arranged to carry out measurements of biological parameters; a measure slave device constructed and arranged to carry out measurements of physical parameters; a master device constructed and arranged to collect the biological and physical parameters and calculate the human or animal score, the score including biological and physical parameters.

A device calculates a score reflecting a state of liver damage, the calculating device being designed to calculate a score using the following physical parameters: a parameter corresponding to inflammation and/or fibrosis; and a parameter corresponding to steatosis.

Magnetic resonance elastography (MRE) is an imaging technique for estimating the stiffness of tissues non-invasively. Shear waves are generated via external mechanical actuation and the tissue imaged with a specially designed MR pulse sequence. The resulting images are used to calculate the underlying properties. The application provides methods for acquiring MRE data using a single shot, echo planar imaging readout. The purpose of the developed sequence is to acquire MRE data using a single-shot, echo-planar imaging readout, avoiding to need for off-line image processing.

A device for the continuous and real-time monitoring of bilirubin, comprises a housing that encloses a waterproof/splash proof body. The body comprises a bilirubin and temperature measuring module; a wearable biocompatible strap; a buckle; a display unit; a safety module; a learning module; a battery and charging unit; a switch; and a wireless connectivity facilitator. The bilirubin and temperature measuring module comprises: one or more light emitting sources; a microprocessor; one or more photodetectors; a filter; and an analogue to digital converter. The device can be configured to be mounted on a patient's wrist or any other body part like palm, earlobe, cheek, finger, forehead, and feet.

A method of controlling a photoacoustic imaging diagnosis apparatus includes irradiating to an object an optical signal having a wavelength corresponding to an optical energy absorption wavelength of a contrast medium that is injected to the object; receiving a photoacoustic signal generated from the object in response to the optical signal; generating scan information representing an intensity of the photoacoustic signal based on the photoacoustic signal; determining a damage degree of the object by using a variation in the intensity of the photoacoustic signal during a preset time period included in the scan information; and displaying the damage degree of the object.

There is provided a method of evaluating a liver condition of a subject, the method includes computing a fluctuation parameter from a liver breath test based on at least one of a percentage dose recovery (PDR) curve and a delta over baseline (DOB) curve of an isotope labeled methacetin, or a salt or a derivative thereof, and evaluating at least one liver condition of the subject, based at least on the fluctuation parameter. There is provided herein a method of evaluating a liver condition of a subject, the method includes computing a hepatic impairment score based at least on a breath test related parameter and on a demographic parameter.

A method and (portable) device are provided for quantifying liver steatosis. The quantitative assessment of liver steatosis predicts whether a liver from the liver transplant donor is suitable for transplantation. Specifically, the quantitative assessment of liver steatosis predicts an associated risk for early allograft dysfunction. A biopsy image from a liver transplant donor is obtained from an automatic quantitative assessment of liver steatosis is made using a pre-trained artificial intelligence algorithm for identifying parameters of liver steatosis. The biopsy image is input to the pre-trained artificial intelligence algorithm. The quantitative assessment of liver steatosis is the output of the pre-trained artificial intelligence algorithm. Embodiments provide for rapidly diagnosing potential donor liver allografts at the time of procurement with a point-of-care device deployed with the transplant surgery team.