Multimodal Physiologic State Intelligence Study

NCT07814118 | Enrolling By Invitation DMC FDA Device

• 1 other identifier: TH-OMNIPHYS-2026-001
• Study Type: observational
• Target: 25,000
• Locations: 1 country
• Sites: 1

Brief Summary

The OMNIPHYS study will investigate whether multimodal ultrasound and other routinely collected clinical and physiologic data can be combined with artificial intelligence to characterize changes in a person's physiologic state over time. The study will examine cardiac, vascular, pulmonary, and systemic physiologic measurements and develop longitudinal models that describe baseline physiology, physiologic perturbation, compensation, deterioration, treatment response, and recovery. The study is observational and will not assign experimental treatments. The goal is to determine whether changes in multimodal physiologic patterns can be identified and characterized earlier and more reliably than conventional single-time-point assessment.

Conditions

Heart Failure; Cardiomyopathies; Pulmonary Hypertension; Myocarditis; Acute or Subacute; Myocarditis Due to Drug; Myocarditis Viral; Pericardial Diseases; Valvular Heart Diseases; Hypertension; Sepsis; Acute Kidney Injury; Pulmonary Edema - Acute; Pulmonary Edema With Heart Failure; Pulmonary Edemas; Acute Respiratory Distress Syndrome; Pulmonary Embolism Acute; Pulmonary Embolism (Diagnosis); Pulmonary Embolism Acute Massive; Pulmonary Embolism Subacute Massive; Venous Thrombosis Pulmonary; Venous Thrombosis (Disorder); Venous Thrombosis, Deep; Peripheral Arterial Disease; Peripheral Arterial Disease (PAD); Aortic Diseases; Muskuloskeletal Diseases; Brain Aging; Brain Aneurysm; Brain Aging Related Conditions; Ultrasound; Ultrasound Exams; Ultrasound Guidance

Keywords

Multimodal Physiologic State Modeling; Physiologic State Transition; Longitudinal Ultrasound; Artificial Intelligence; Machine Learning; Multimodal Imaging; Point-of-Care Ultrasound; 3D Ultrasound; Color Doppler; Doppler Ultrasound; Physiologic Phenotyping; Physiologic Trajectory; Clinical Deterioration; Early Detection; Digital Biomarkers; Federated Learning; Precision Medicine; Longitudinal Monitoring; Cardiac Ultrasound; Vascular Ultrasound; Human-AI Concordance; Cross-Device Generalizability; Model Drift; Out-of-Distribution Detection; Multimodal Ultrasound

Outcome Measures

Primary Outcomes (1)

• Physiologic Transition Detection Time (PTDT)

  Time interval, measured in days, between the first prespecified algorithmic detection of a clinically meaningful physiologic state transition and the corresponding predefined clinical reference event. Transitions may include progression from baseline or compensated physiology to pre-decompensation or acute decompensation. The analysis will evaluate the temporal relationship between multimodal physiologic signals and subsequent clinical events.

  Continuous longitudinal assessment from baseline (Year 0) through Year 50; PTDT will be calculated separately for each predefined clinical reference event occurring during the 50-year follow-up period.

Secondary Outcomes (9)

• Physiologic State Classification Accuracy

  Baseline (Year 0) through Year 50; physiologic state-classification accuracy will be assessed using all evaluable longitudinal observations and predefined clinical reference events collected during the 50-year follow-up period.

• Physiologic Trajectory Prediction

  Baseline (Year 0) through Year 50 of longitudinal follow-up.

• Time to Clinical Deterioration

  Baseline (Year 0) through Year 50 of longitudinal follow-up.

• Treatment Response Characterization

  Baseline (Year 0) through Year 50 of longitudinal follow-up.

• Physiologic Recovery Trajectory

  Baseline (Year 0) through Year 50 of longitudinal follow-up.

Study Arms (1)

OMNIPHYS Master Physiologic Cohort

A prospective longitudinal observational cohort of participants undergoing routine clinical evaluation and/or care. The cohort will be characterized using multimodal physiologic observations, including ultrasound-derived imaging, 2D and 3D imaging, color and spectral Doppler, cardiac and vascular measurements, pulmonary ultrasound findings, vital signs, ECG, oxygen saturation, laboratory data, medications, diagnoses, clinical encounters, and longitudinal outcomes. Participants will not be assigned to an experimental treatment. Disease-specific and physiologic subcohorts may be evaluated within the master cohort using a common longitudinal physiologic-state framework.

Device: OMNIPHYS Multimodal Ultrasound Imaging System

Interventions

OMNIPHYS Multimodal Ultrasound Imaging System DEVICE

Observational physiologic and imaging data acquisition.

OMNIPHYS Master Physiologic Cohort

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

OMNIPHYS will enroll a diverse adult population including healthy volunteers and participants receiving routine clinical evaluation or care. The study will include individuals across cardiovascular, vascular, pulmonary, renal, and systemic physiologic conditions, with disease-specific and physiologic subcohorts analyzed within a common longitudinal framework. Multimodal ultrasound, Doppler, physiologic measurements, clinical data, laboratory data, and longitudinal outcomes will be evaluated to characterize individual physiologic states, trajectories, transitions, treatment responses, and recovery patterns.

You may qualify if:

• Adults aged 18 years or older who are capable of providing informed consent, or who meet applicable legally authorized representative requirements where permitted.
• Individuals undergoing routine clinical evaluation, diagnostic imaging, monitoring, treatment, or longitudinal follow-up, including individuals with relevant cardiovascular, vascular, pulmonary, renal, systemic, or other conditions represented within the OMNIPHYS research framework.
• Healthy volunteers without the conditions under study may participate as reference participants.
• Ability to undergo at least one protocol-relevant physiologic or imaging assessment and/or contribute eligible routinely collected clinical data.
• Availability of sufficient clinical, physiologic, imaging, or longitudinal outcome data for the applicable analysis.
• Willingness to permit collection and longitudinal analysis of protocol-defined research data in accordance with informed consent and applicable privacy and data-governance requirements.

You may not qualify if:

• Inability or unwillingness to provide informed consent or otherwise meet applicable consent requirements.
• Inability to obtain interpretable protocol-relevant physiologic or imaging data when such data are required for the applicable analysis.
• Conditions or circumstances that, in the judgment of the responsible clinical investigator, make participation inappropriate or prevent meaningful longitudinal follow-up.
• Withdrawal of consent or request for discontinuation of research participation where applicable.
• Any circumstance that would prevent collection, secure handling, or analysis of study data in accordance with the approved protocol and applicable requirements.

Sponsors & Collaborators

• Truway Health, Inc.: lead

Study Sites (1)

Truway Health, Inc.

New York, New York, 10016, United States

Location

Related Publications (10)

• Kayarian F, Patel D, O'Brien JR, Schraft EK, Gottlieb M. Artificial intelligence and point-of-care ultrasound: Benefits, limitations, and implications for the future. Am J Emerg Med. 2024 Jun;80:119-122. doi: 10.1016/j.ajem.2024.03.023. Epub 2024 Mar 25.

  PMID: 38555712 BACKGROUND

• Bennett S, Johnson CL, Fisher G, Erskine F, Krasner S, Fletcher AJ, Leeson P. Development and Validation of Echocardiography Artificial Intelligence Models: A Narrative Review. J Clin Med. 2025 Oct 7;14(19):7066. doi: 10.3390/jcm14197066.

  PMID: 41096146 BACKGROUND

• Koutsoubis N, Waqas A, Yilmaz Y, Ramachandran RP, Schabath MB, Rasool G. Privacy-preserving Federated Learning and Uncertainty Quantification in Medical Imaging. Radiol Artif Intell. 2025 Jul;7(4):e240637. doi: 10.1148/ryai.240637.

  PMID: 40366260 BACKGROUND

• Rehman MHU, Hugo Lopez Pinaya W, Nachev P, Teo JT, Ourselin S, Cardoso MJ. Federated learning for medical imaging radiology. Br J Radiol. 2023 Oct;96(1150):20220890. doi: 10.1259/bjr.20220890.

  PMID: 38011227 BACKGROUND

• Guan H, Yap PT, Bozoki A, Liu M. Federated learning for medical image analysis: A survey. Pattern Recognit. 2024 Jul;151:110424. doi: 10.1016/j.patcog.2024.110424. Epub 2024 Mar 12.

  PMID: 38559674 BACKGROUND

• G S, Gopalakrishnan U, Parthinarupothi RK, Madathil T. Deep learning supported echocardiogram analysis: A comprehensive review. Artif Intell Med. 2024 May;151:102866. doi: 10.1016/j.artmed.2024.102866. Epub 2024 Apr 4.

  PMID: 38593684 BACKGROUND

• Kim J, Maranna S, Watson C, Parange N. A scoping review on the integration of artificial intelligence in point-of-care ultrasound: Current clinical applications. Am J Emerg Med. 2025 Jun;92:172-181. doi: 10.1016/j.ajem.2025.03.029. Epub 2025 Mar 17.

  PMID: 40117961 BACKGROUND

• Myhre PL, Grenne B, Asch FM, Delgado V, Khera R, Lafitte S, Lang RM, Pellikka PA, Sengupta PP, Vemulapalli S, Lam CSP. Artificial intelligence-enhanced echocardiography in cardiovascular disease management. Nat Rev Cardiol. 2026 Mar;23(3):164-182. doi: 10.1038/s41569-025-01197-0. Epub 2025 Aug 5.

  PMID: 40764834 BACKGROUND

• Sahashi Y, Ouyang D, Okura H, Kagiyama N. AI-echocardiography: Current status and future direction. J Cardiol. 2025 Jun;85(6):458-464. doi: 10.1016/j.jjcc.2025.02.005. Epub 2025 Mar 1.

  PMID: 40023671 BACKGROUND

• Wu D, Arntfield R, Millington SJ. Artificial Intelligence in Point-of-Care Ultrasound. J Intensive Care Med. 2026 Mar 2:8850666261427329. doi: 10.1177/08850666261427329. Online ahead of print.

  PMID: 41771537 BACKGROUND

Related Links

• PubMed - Artificial Intelligence in Point-of-Care Ultrasound
• PubMed - AI-echocardiography: Current status and future direction
• PubMed - Artificial intelligence-enhanced echocardiography in cardiovascular disease management
• PubMed - Federated learning for medical image analysis: A survey

MeSH Terms

Conditions

Heart Failure; Cardiomyopathies; Hypertension, Pulmonary; Myocarditis; Heart Valve Diseases; Hypertension; Sepsis; Acute Kidney Injury; Pulmonary Edema; Respiratory Distress Syndrome; Pulmonary Embolism; Disease; Venous Thrombosis; Peripheral Arterial Disease; Aortic Diseases; Intracranial Aneurysm; Clinical Deterioration

Condition Hierarchy (Ancestors)

Heart Diseases; Cardiovascular Diseases; Lung Diseases; Respiratory Tract Diseases; Vascular Diseases; Infections; Systemic Inflammatory Response Syndrome; Inflammation; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Renal Insufficiency; Kidney Diseases; Urologic Diseases; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Male Urogenital Diseases; Respiration Disorders; Embolism; Embolism and Thrombosis; Thrombosis; Atherosclerosis; Arteriosclerosis; Arterial Occlusive Diseases; Peripheral Vascular Diseases; Intracranial Arterial Diseases; Cerebrovascular Disorders; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Aneurysm; Disease Progression; Disease Attributes

Study Officials

• Gavin C Solomon, M.D.

  Truway Health, Inc.

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: September 2, 2026
• First Posted: September 10, 2026
• Study Start: September 2, 2026
• Primary Completion (Estimated): September 2, 2046
• Study Completion (Estimated): September 15, 2046
• Last Updated: September 10, 2026

Record last verified: 2026-09

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP, ICF, CSR, ANALYTIC CODE
• Time Frame: Beginning after publication of the primary results and continuing for as long as scientifically and ethically appropriate.
• Access Criteria: Qualified researchers submitting scientifically valid proposals through the designated study data-access process.

Locations

US (1)