The present invention relates to a method and system for identifying treatable and remediable factors of Dementia and aging cognitive changes, to provide recommendations for aiding in the diagnosis of dementia or predementia symptoms in a subject. According to an embodiment of the invention, the method comprising: receiving data relative to medical history and examinations, processing said received data by applying an algorithm(s) relative to the Intensive Neuropsychogeriatric Evaluation, Treatment and Prevention (INETAP) method, and verifying whether said processed data is sufficient for indicating of advanced Dementia Potential Remediable Conditions (PRCs), and outputting data for aiding in the diagnosis of one of the following: dementia PRCs, pre-dementia PRCs, no dementia/pre-dementia, or Dementia without treatment horizon.

Provided are an information processing apparatus, an information processing method, and a program capable of accumulating appropriate relearning data. An information processing apparatus includes an input unit that inputs input data to a learned model acquired in advance through machine learning using learning data, an acquisition unit that acquires output data output from the learned model through the input using the input unit, a reception unit that receives correction performed by a user for the output data acquired by the acquisition unit, and a storage controller that performs control for storing, as relearning data of the learned model, the input data and the output data that reflects the correction received by the reception unit in a storage unit in a case where a value indicating a correction amount acquired by performing the correction for the output data is equal to or greater than a threshold value.

Provided are an information processing apparatus, an information processing method, and a program capable of accumulating appropriate relearning data. An information processing apparatus includes an input unit that inputs input data to a learned model acquired in advance through machine learning using learning data, an acquisition unit that acquires output data output from the learned model through the input using the input unit, a reception unit that receives correction performed by a user for the output data acquired by the acquisition unit, and a storage controller that performs control for storing, as relearning data of the learned model, the input data and the output data that reflects the correction received by the reception unit in a storage unit in a case where a value indicating a correction amount acquired by performing the correction for the output data is equal to or greater than a threshold value.

Systems, methods, and instrumentalities are disclosed for switching a control scheme to control a set of system modules and/or modular devices of a surgical hub. A surgical hub may determine a first control scheme that is configured to control a set of system modules and/or modular devices. The surgical hub may receive an input from one of the set of modules or a device located in an OR. The surgical hub may make a determination that at least one of a safety status level or an overload status level of the surgical hub is higher than its threshold value. Based on at least the received input and the determination, the surgical hub may determine a second control scheme to be used to control the set of system modules. The surgical hub may send a control program indicating the second control scheme to one or more system modules and/or modular devices.

An AI based platform for processing information collected during a medical procedure. A method includes capturing images and speech during a medical procedure; processing the images using a trained classifier to identify image-based quality-of-care indicators (QIs); converting the speech into text; parsing the text into sentences; performing a search and replace on predefined text patterns in the sentences; identifying text-based QIs in the sentences; classifying sentences into sentence types using a trained model; updating sentences by integrating the image-based QIs with text-based QIs; and outputting structured data that includes sentences organized by sentence type.

An AI based platform for processing information collected during a medical procedure. A method includes capturing images and speech during a medical procedure; processing the images using a trained classifier to identify image-based quality-of-care indicators (QIs); converting the speech into text; parsing the text into sentences; performing a search and replace on predefined text patterns in the sentences; identifying text-based QIs in the sentences; classifying sentences into sentence types using a trained model; updating sentences by integrating the image-based QIs with text-based QIs; and outputting structured data that includes sentences organized by sentence type.

A system, computer program product and method for facilitating concurrent monitoring of multiple obstetrics patients over a data network are proposed. Data conveying pregnancy progression information, including maternal and fetal vital sign information associated with the obstetrics patients, is received and processed to derive criticality levels for the obstetrics patients. As a first feature, notification data related to a particular obstetrics patient may be selectively transmitted to a medical expert based on different conditions including for example an associated criticality level exceeding a threshold and/or receipt of a consultation request. As a second feature, used together or separately from the first, a graphical user interface (GUI) may present a user with an ordered list of obstetrics patients dynamically adaptable based on the criticality levels. The GUI allows a user to select an obstetrics patient from the list and then adapts the GUI to present an expanded set of pregnancy progression information related to the selected obstetrics patient. Optionally, the GUI may also provide a tool allowing the user to cause a notification to be transmitted to a medical expert over the data network.

A system, computer program product and method for facilitating concurrent monitoring of multiple obstetrics patients over a data network are proposed. Data conveying pregnancy progression information, including maternal and fetal vital sign information associated with the obstetrics patients, is received and processed to derive criticality levels for the obstetrics patients. As a first feature, notification data related to a particular obstetrics patient may be selectively transmitted to a medical expert based on different conditions including for example an associated criticality level exceeding a threshold and/or receipt of a consultation request. As a second feature, used together or separately from the first, a graphical user interface (GUI) may present a user with an ordered list of obstetrics patients dynamically adaptable based on the criticality levels. The GUI allows a user to select an obstetrics patient from the list and then adapts the GUI to present an expanded set of pregnancy progression information related to the selected obstetrics patient. Optionally, the GUI may also provide a tool allowing the user to cause a notification to be transmitted to a medical expert over the data network.

A vehicle with an emergency reporting function transmits an emergency report in a case where the vehicle is involved in an emergency. The vehicle includes a vehicle communicator and a vehicle memory. The vehicle communicator transmits the emergency report to an operator terminal. The operator terminal acquires emergency-related information by accessing a memory holding the emergency-related information. The emergency-related information includes limited-access information about the vehicle, an occupant of the vehicle, or both. The vehicle memory holds authentication information to be used to access the limited-access information held in the memory. The vehicle communicator transmits the authentication information to the operator terminal, and causes the operator terminal to acquire the limited-access information from the memory, as a part of the emergency-related information, by using the authentication information received from the vehicle.

There is provided a method for determining dementia risk factors by a server using deep learning. In this instance, the method for determining dementia risk factors includes acquiring biometric information from each subject corresponding to a first control group through a wearable device, acquiring measurement information for each subject corresponding to the first control group, deriving a first dementia risk factor based on the biometric information and the measurement information for each subject, and deriving a second dementia risk factor related to the first dementia risk factor via deep learning performed based on the biometric information related to the first dementia risk factor and control group information.