Anumana, Inc.

Systems and methods for non-invasive or minimally invasive collection and analysis of physiological data (e.g., blood pressure, electrolytes, genetic markers, B cell repertoire) to assess patient health status, screen for conditions, or aid in diagnosis.

Utilizing machine learning, particularly deep learning, to analyze medical data such as images, sensor readings, or physiological signals for disease prediction, diagnosis, or treatment assessment.

Systems and methods that use imaging technologies, computer vision, and augmented reality to provide real-time guidance, localization, and visualization during surgical procedures or for detailed anatomical assessment.

Processes for creating or manipulating three-dimensional digital representations of objects or environments, including mesh generation, surface fitting, and depth estimation from multiple views.

Design and application of devices that are inserted into the body or implanted to treat diseases, modulate physiological functions, or repair anatomical structures.

Development of devices and methods for non-invasive or minimally invasive collection and analysis of physiological data, often from wearable sensors, to monitor health, activity, or specific conditions.

Systems that integrate digital technology, sensors, or connectivity to monitor, track, or automate aspects of medication administration, often providing data feedback, personalized recommendations, or secure logging.

Digital platforms and systems that deliver tailored therapeutic interventions, guidance, or recommendations to patients based on their individual health data, biometric feedback, and computational models (e.g., AI/ML, physiological simulations).

Techniques for combining and analyzing information from multiple distinct data modalities (e.g., text, image, video, audio, sensor data) to derive richer insights or improve system performance and decision-making.