The present disclosure is directed to providing digital health services. In some embodiments, systems and methods for conducting virtual or remote sessions between patients and clinicians are disclosed. During the sessions, media content (e.g., images, video content, audio content, etc.) may be captured as the patient performs one or more tasks. The media content may be presented to the clinician and used to evaluate a condition of the patient or a state of the condition, adjust treatment parameters, provide therapy, or other operations to treat the patient. The analysis of the media content may be aided by one or more machine learning/artificial intelligence models that analyze various aspects of the media content, augment the media content, or other functionality to aid in the treatment of the patient.

The present disclosure is directed to providing digital health services. In some embodiments, systems and methods for conducting virtual or remote sessions between patients and clinicians are disclosed. During the sessions, media content (e.g., images, video content, audio content, etc.) may be captured as the patient performs one or more tasks. The media content may be presented to the clinician and used to evaluate a condition of the patient or a state of the condition, adjust treatment parameters, provide therapy, or other operations to treat the patient. The analysis of the media content may be aided by one or more machine learning/artificial intelligence models that analyze various aspects of the media content, augment the media content, or other functionality to aid in the treatment of the patient.

A neuromelanin sensitive magnetic resonance imaging (MRI) technique, method and computer-accessible medium for measuring the extent of, providing a diagnosis of, monitoring the treatment of, assessing novel treatments for, or determining a prognosis related to one or more neurological conditions. To support these applications, the present disclosure accurately determines the normative range of neuromelanin-sensitive MRI signal and volume metrics in cognitively normal older adults. Those displaying certain characteristic neuromelanin-sensitive MRI signals falling outside of the normative range should be assessed and treated according to the particular diagnosis as provided by the present application.

Provided are apparatuses, a non-transitory computer-readable medium or media, for enhancing prediction of neurodevelopmental disorder using a fundus image of a subject. In certain aspects, disclosed a method including the steps of: receiving the fundus image; segmenting a region of interest for the fundus image based on a machine learning model; mapping an adversarial noise to the region of interest; processing the fundus image to classify one or more features contained in the region of interest, which is mapped by the adversarial noise, using the machine learning model; and predicting, based on a classification, whether the fundus image is indicative of presence of neurodevelopmental disorder in the subject.

A system and kit for capturing a 3D image of a body a user includes a plurality of pillar segments being configurable between an assembled configuration and a disassembled configuration. In the assembled configuration, the pillar segments are joined to form one or more upstanding sensing pillars. A plurality of sensors operable to capture image data are distributed along the one or more sensing pillars. The plurality of sensors have fields of view that are overlapping when supported on the sensing pillars. In the disassembled configuration, transportation of the pillar segments is facilitated. The system and kit may be suitable for use at a remote location. Additional functionalities may include a power storage unit, solar charging panels, climate control subsystem, and wireless communication submodule. In operation, the sensing pillars may be enclosed within an enclosure.

A system utilizing five or less body worn sensors may be used to profile the motor function of Parkinson's disease patients, integrate the outcome with patient self-reported information and translate the results to clinically relevant information, valuable for the monitoring of Parkinson's disease progression and symptom manifestation. The hardware of the system may deploy algorithms for the offline processing of the sensors' data, once the wearable monitoring devices are docked for charging, with no intervention required by the user. The system may also allow patients to mount the wearable devices featuring the sensors to any of a limited number of body parts, without taking care to mount each monitoring device to a specific body part. Finally, the system may allow a physician to register for a subscription-based service, pairing him/her with patients using the system, and granting him/her permission to remotely review the disease progression of the patients, as calculated by the system.

A computer-implemented system, and method for classifying Parkinson's Disease (PD) from magnetic resonance imaging (MRI) data. The method includes receiving an MRI image. The method includes processing the MRI image through a C3BAM-Net convolutional neural network (CNN) architecture to obtain a plurality of attention-enhanced feature maps. The method includes classifying the input MRI image into one of a plurality of the PD categories based on the plurality of attention-enhanced feature maps. Where the C3BAM-Net CNN includes a plurality of convolutional layers, a plurality of Rectified Linear Unit (ReLU) activations, a plurality of max pooling layers, a plurality of Convolutional Block Attention Modules (CBAMs), a flattening layer, and a plurality of dense layers. The method includes each CBAM of the plurality of CBAMs includes a Channel Attention Module (CAM) and a Spatial Attention Module (SAM) arranged sequentially.

Provided herein are, inter alia, methods for identifying subjects suffering from depression that will respond to treatment with an antidepressant.

A system and method for dual-task neurological therapy using a combination of primary and secondary tasks to facilitate neurogenesis and neuroplasticity in targeted regions of the brain using computer-enhanced dual-task analysis and treatment. The system and method involve having a subject engage in primary and secondary tasks at levels of intensity or stress associated with increased neurogenesis and neuroplasticity. In some embodiments, novel secondary tasks are selected to vary the tasks to help with neurogenesis and neuroplasticity, novel content for the secondary tasks are generated by a generative AI model, adjustments are made to the tasks during performance using a feedback mechanism to adjust for the abilities and performance of the patient, and empathetic feedback is generated by a generative AI model and provided to the patient during performance of tasks.

Systems and methods rely on feedback from an active medical device or devices (e.g., neurostimulator coupled to sensing and stimulation elements such as electrodes) to assess the effectiveness of a patient's drug regimen. Such reliance may include analyzing characteristics in physiological data acquired by the medical device(s), for example, in the form of responses evoked from the patient by electrical stimulation waveforms. Systems and methods further involved adjusting one or more parameters according to which a combination therapy consisting of at least a drug regimen and an electrical stimulation therapy are delivered to a patient, in an effort to optimize the therapeutic effect of the combination. The adjustments may be automatically by one or more implanted or external hosts working together or alone, and/or with the input of a physician.