A data acquisition device includes measuring instruments to generate physiological and/or psychological data streams. Microprocessors within the acquisition device process the generated data streams into metrics, which feed into stress function algorithms. Algorithm processing may occur either on the device, or metrics may be communicated via wireless communication for external processing on mobile devices and/or cloud-based platforms. The calculated stress functions inform cloud-based computational systems biology-derived models describing the dynamics of hormones and neurotransmitters released in the body in response to stressful stimuli. Stress hormone levels are quantified using these models, and are used in combination to serve as biologically inspired metrics of acute and chronic stress an individual is experiencing.

A system includes an earpiece and a telecommunications device in communication with the earpiece. The earpiece includes a power source, a processor configured to process at least one algorithm stored within the earpiece, and an optical sensor. The optical sensor includes at least one optical emitter configured to direct optical energy to a region of an ear of a subject wearing the earpiece and at least one optical detector configured to sense absorbed, scattered, and/or reflected optical energy emanating from the ear region. The telecommunications device is configured to modify the at least one algorithm, to download additional algorithms to the earpiece, and to activate and deactivate the optical sensor.

Wearable apparatus for monitoring various physiological and environmental factors are provided. Real-time, noninvasive health and environmental monitors include a plurality of compact sensors integrated within small, low-profile devices, such as earpiece modules. Physiological and environmental data is collected and wirelessly transmitted into a wireless network, where the data is stored and/or processed.

In some aspects, a system is disclosed for predicting values of a physiological parameter for a patient. The system can include a processor and a memory device. The processor can: receive measurements of a physiological parameter for a patient; determine, using a recurrent neural network, values of the physiological parameter from the measurements for the patient; and output the values for presentation to a caregiver. The memory device can store the recurrent neural network.

Wearable apparatus for monitoring various physiological and environmental factors are provided. Real-time, noninvasive health and environmental monitors include a plurality of compact sensors integrated within small, low-profile devices, such as earpiece modules. Physiological and environmental data is collected and wirelessly transmitted into a wireless network, where the data is stored and/or processed.

Wearable apparatus for monitoring various physiological and environmental factors are provided. Real-time, noninvasive health and environmental monitors include a plurality of compact sensors integrated within small, low-profile devices, such as earpiece modules. Physiological and environmental data is collected and wirelessly transmitted into a wireless network, where the data is stored and/or processed.

A system includes a computing device that receives a query thyroid image, where the query thyroid image is an ultrasound image of a thyroid comprising a thyroid nodule of interest. The computing device processes the query thyroid nodule image using a machine learning model to identify at least one labelled thyroid image from a plurality of labelled thyroid images that is similar to the query thyroid nodule image. The plurality of labelled thyroid images are used as training data to generate the machine learning model. The at least one labelled thyroid image has labels associated therewith and comprises an ultrasound image of a thyroid nodule that has a confirmed diagnosis. The computing device generates an output report based on the labels associated with the at least one labelled thyroid image, where the output report indicates whether the thyroid nodule of interest resembles a malignant thyroid nodule or benign thyroid nodule.

A system and a method for assessing a viability of the parathyroid gland, the system includes a memory unit for storing an image of a subject's parathyroid gland detected by a near-infrared sensor, an information extractor for extracting feature information from the image and a processor that includes a machine learning model into which the feature information is inputted, and generates a blood flow index of the parathyroid gland from the feature information based on the machine learning model, the method includes performing an image pickup of a subject's parathyroid region with a near-infrared sensor, extracting feature information from an image of the subject's parathyroid region, and generating a blood flow index of the parathyroid gland from the feature information based on a machine learning model.

An apparatus and a method for identifying a position of a parathyroid gland and assessing a viability of the parathyroid gland. The apparatus irradiates light having a selected first wavelength to the parathyroid surgery area, and identifies the position of the parathyroid gland through a light separation process after acquiring image information of the parathyroid surgery area. Also, the apparatus irradiates the light of a selected second wavelength to the parathyroid gland or an adjacent area of the parathyroid gland, obtains a diffuse speckle pattern generated in the parathyroid gland, and performs viability assessment of the parathyroid gland with enhanced reliability.

A system that can include a processor that obtains voice data of a subject. The processor can compare the voice data of the subject with reference normal and abnormal voice data. If the voice data of the subject is abnormal, the processor can generate an abnormal subject voice data that is indicative of differences between the subject voice data and the normal voice data. By comparing the generated abnormal subject voice data with the reference normal and abnormal voice data, the processor can determine what condition the subject may be suffering from and the severity of that condition.