System and method for measuring diabetes mellitus condition of a subject is disclosed. The disclosed system and method includes thermal sensors for capturing thermal images and/or videos of a body part; and a processing engine to detect a predefined region of the body part in each frame of the captured images and/or videos. The processing engine segments one or more portions from the detected predefined region in each frame of the captured images and/or videos to identify a region of interest comprising major arteries in the segmented portions. Based on the ROI, the engine extracts pixel values, representing biosignals, from each frame of the captured images and/or videos so as to determine one or more parameters associated with the hemodynamic factors and a rate of atherosclerosis of the subject. Further, a risk score for the diabetes mellitus condition based on the determined parameters using computational models is measured.

Systems and methods are disclosed herein for determining a diagnosis based on a base skin tone of a patient. In an embodiment, the system receives a base skin tone image of a patient, generates a calibrated base skin tone image by calibrating the base skin tone image using a reference calibration profile, and determines a base skin tone of the patient based on the calibrated base skin tone image. The system receives a concern image of a portion of the patient's skin, and selects a set of machine learning diagnostic models from a plurality of sets of candidate machine learning diagnostic models based on the base skin tone of the patient, each of the sets of candidate machine learning diagnostic models trained to receive the concern image and output a diagnosis of a condition of the patient.

The present invention relates to a system for diagnosing small bowel cleanliness. The system may comprise: a similarity analysis unit for analyzing to select a representative image of similar small bowel images from among a plurality of small bowel images; an image classification unit for, when a series of a plurality of small bowel images in which cleanliness is to be diagnosed are received in a state where the plurality of small bowel images have been learned, classifying small bowel cleanliness according to scores by predicting the small bowel cleanliness by applying the representative image to a learning result; and a cleanliness diagnosis unit for calculating final small bowel cleanliness for the series of the plurality of small bowel images on the basis of a score for small bowel cleanliness of the representative image and the number of small bowel images similar to the representative image.

The invention provides systems, methods and computer program products for monitoring vascular perfusion in replanted tissue flaps. Specifically, the invention provides a non-invasive solution for monitoring of vascular perfusion at a site of tissue replantation, and that is capable of detecting problems in vascular perfusion and raising alarms in real times. The invention achieves the above function objectives by means of non-invasive sensors that continuously monitor selected parameters related to tissue condition. The monitored data parameters are used to determine a real time condition of replanted tissue.

Systems and methods for analyzing complexity of surgical footage are disclosed. A system may include at least one processor configured to analyze frames of the surgical footage to identify an anatomical structure in a first set of frames. The processor may access first historical data based on an analysis of first frame data captured from a first group of surgical procedures and analyze the first set of frames to determine a first complexity level. The processor may analyze the surgical footage to identify in a second set of frames a medical tool, an anatomical structure, and an interaction between the medical tool and the anatomical structure. The processor may access second historical data based on an analysis of a second frame data captured from a second group of surgical procedures and analyze the second set of frames to determine a second complexity level associated with the second set of frames.

Quantitative colorimetric carbon dioxide detection and measurement systems are disclosed. The systems can include a gas conduit, a colorimetric indicator adapted to exhibit a color change in response to exposure to carbon dioxide gas, a temperature controller operatively coupled to the colorimetric indicator and configured to control the temperature of the colorimetric indicator, an electro-optical sensor assembly including a light source or sources adapted to transmit light to the colorimetric indicator, and a photodiode or photodiodes configured to detect light reflected from the colorimetric indicator and to generate a measurement signal, and a processor in communication with the electro-optical sensor assembly. The processor can be configured to receive the measurement signal generated by the electro-optical sensor assembly and to compute a concentration of carbon dioxide based on the measurement signal. Methods for using the systems are also disclosed including providing a breathing therapy to a patient or user.

The technology described in this document can be embodied in systems and computer-implemented methods for determining a score representing an amount of staining of the cornea. The methods include obtaining a digital image of the cornea stained with a tracer material, receiving a selection of a portion of the image, and processing, by a processing device, the selection to exclude areas with one or more artifacts to define an evaluation area. For each of a plurality of pixels within the evaluation area, a plurality of Cartesian color components are determined and a hue value in a polar coordinate based color space is calculated from the components. An amount of staining of the cornea is then determined as a function of the hue value. The methods also include assigning a score to the evaluation area based on the amount of staining calculated for the plurality of pixels.

Provided is an image processing device including a dividing unit which divides a captured image of skin into regions in a multidimensional manner, a feature value calculating unit which calculates a feature value of a color property in each region divided by the dividing unit, and an evaluating unit which calculates an evaluation value of the skin using the feature values calculated by the feature value calculating unit.

In one embodiment, a method comprising: (a) receiving a set of physiological data associated with at least one health condition of an animal subject; (b) receiving a set of environmental data associated with one or more environment conditions to which the subject is or has been exposed; (c) determining a set of operating parameters for at least one environmental device based at least partially on at least a portion of the set of physiological data and at least a portion of the set of environmental data; and (d) transmitting the set of operating parameters to the at least one environmental device to at least partially control at least one controlled environmental condition to which the subject is exposed to thereby at partially control the at least one health condition.

A hyperspectral facial analysis system and method for personalized health scoring to assess the risk that a person has a disease. Embodiments capture images in multiple spectral bands, such as visible, infrared, and ultraviolet, and analyze these images to generate multiple health metrics, such as pallor, temperature, sweat, and chromophores. These metrics may be combined into an overall health score that may be used for screening. Image analysis may focus on the area under the eyes, where skin is thinnest. Images may be compared to a reference population to identify anomalous values, so that health scoring automatically adjusts for local conditions. Pallor may be calculated based on hue and saturation of visible light images. Temperature may be calculated based on infrared image intensity. Sweat may be calculated using cross-polarized images to identify specular highlights. Chromophores may be calculated by comparing frequency domain ultraviolet images to those of the reference population.