A method for acquiring at least one two-dimensional image of a part of arches of a patient includes steps carried out by the patient or other person who is not a dental health professional, for example, including placing a dental separator in the mouth of the patient in order to separate the lips of the patient and improve the visibility of the teeth during the acquisition of said at least one two-dimensional image, and acquiring, in a mouth closed position and with a personal image acquisition apparatus, said at least one two-dimensional image.

An intraoral imaging apparatus, a medical apparatus, and a program capable of providing auxiliary data for determination regarding diseases having differences in intraoral findings are provided. The intraoral imaging apparatus includes: an imaging device that acquires an intraoral image; a light source that emits light to a subject of the imaging device; a storage apparatus that stores an algorithm for performing determination of a specific disease; and an arithmetic apparatus, in which the arithmetic apparatus executes: a determination process of determining a possibility of the predetermined disease based on the image and the algorithm; and an output process of outputting a result of the determination process.

The present invention relates to a breath analyzing system and method. In particular the invention relates to a breath analyzing system and method arranged to provide tracer-aided classification of the presence of a breath intoxicating substance above a limit concentration and providing status to a user about the progression of the classification. The method/system detects a peak in the tracer signal and defines an evaluation period corresponding to the duration of the peak. Measurements classification of the concentration of the intoxicating substance is used for the evaluation period, and if required to achieve a result, for a plurality of evaluation periods.

A condition visualization system includes a first acquisition unit, an estimation unit, a processing unit, an output unit, a second acquisition unit, and a correction information generation unit. The first acquisition unit acquires a user's biometric information. The estimation unit obtains, based on the biometric information, an estimated value representing the user's mental and physical condition. The processing unit generates, based on the estimated value, mental and physical condition information. The output unit outputs the mental and physical condition information to a display unit. The second acquisition unit acquires a subjective value with respect to the user's mental and physical condition. The correction information generation unit generates correction information by using a plurality of data sets, each including the estimated value and the subjective value. The processing unit generates the mental and physical condition information by making correction to the estimated value based on the correction information.

Provided is a sensor device capable of detecting leakage from a pouch with a higher accuracy. An odor sensor (41) detects odor leaking from a pouch (3) attached to a stoma (21) of a user (2). A radio communication interface (42) notifies, via a notification device (5), the user (2) that odor is detected.

Patient sleep is staged using personalized circadian models built with data collected by wearable devices over daytime and nighttime hours, thus capturing a patient's personal circadian rhythms. The circadian model is used to identify sleep intervals in incoming nightly data for the patient. The identified sleep intervals are analyzed by the machine learning system which stages epochs of sleep. Methods include receiving patient heart rate data from over a plurality of circadian cycles; creating a circadian model for the patient with a defined operation for applying sleep labels to new data from the wearable device; applying the circadian model to nightly test data from the device to identify a sleep interval; and assigning, with a classifier, sleep stages to epochs of the sleep interval.

Disclosed are various examples and embodiments of systems, devices, components and methods configured to extract atrial signals from electrical signals acquired from a patient suffering from atrial fibrillation. The electrical signals acquired from the patient may be intra-cardiac signals or body surface electrode signals, or both. At least portions of QRS or QRS-T complexes corresponding to determined initial synchronization times are used to generate Fast Fourier Transforms (FFTs) corresponding to the extracted QRS complexes. A series of steps follow to generate isolated atrial signals corresponding to each electrical signal by subtracting generated reconstructed signals corresponding to each such electrical signal therefrom.

A wireless, patient-worn, physiological sensor configured to, among other things, help manage a patient that is at risk of forming one or more pressure ulcers is disclosed. According to an embodiment, the sensor includes a base having a top surface and a bottom surface. The sensor also includes a substrate layer including conductive tracks and connection pads, a top side, and a bottom side, where the bottom side of the substrate layer is disposed above the top side of the base. Mounted on the substrate layer are a processor, a data storage device, a wireless transceiver, an accelerometer, and a battery. In use, the sensor senses a patient's motion and wirelessly transmits information indicative of the sensed motion to, for example, a patient monitor. The patient monitor receives, stores, and processes the transmitted information.

A display device includes a display panel, an input sensor, and a readout circuit. The display panel is configured to display an image. The input sensor is disposed on the display panel. The readout circuit is configured to output a moisture level signal corresponding to sensing signals received from the input sensor in a skin measurement mode.

Rather than rely on variation from physician to physician and limited imaging information for assessing plaque vulnerability of a patient, medical imaging and other information are used by a machine-implemented classifier to predict plaque rupture. Anatomical, morphological, hemodynamic, and biochemical features are used in combination to classify plaque.